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Statistical Analysis: an Introduction using R

This book aims to introduce the principles of statistics and modern statistical analysis for a non-mathematical audience, using the free statistical package R.

If you are just browsing, we recommend setting up your personal Wikibooks style as specified in /Statistics and R.

Contents

 * Cover page / Foreword
 * /Introduction/: Statistics is common sense
 * Chapter 0: A basic introduction to the R software package

 * /Chapter 1/: Assumptions and models
 * /Chapter 2/: Datasets and types of data
 * /Chapter 3/: Statistical models
 * /Chapter 4/: Model formulation and EDA
 * /Chapter 5/: Probability and likelihood
 * /Chapter 6/: The error process
 * Including hypothesis testing

 * /Chapter 7/: Models for discrete data
 * /Chapter 8/: Models for continuous data
 * /Chapter 9/: Univariate models
 * : to be continued

 * Appendix A: R topics in text
 * These can be used on their own as a basic introduction to R

 * Appendix B: Further references
 * Appendix C: Resources for contributors
 * Appendix D: Freely available datasets used in the book

Authors
Yan


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Statistical Analysis: an Introduction using R/Cover

An understanding of statistics is an invaluable tool in much of modern science. This is particularly true of fields such as the biosciences and social sciences in which data are “noisy”, that is where chance or uncontrollable effects have a substantial impact on the data recorded. This wikibook is aimed at the scientifically-minded reader who has not been exposed to much statistical thinking. As well as the Statistics wikibook, and guides on the RWiki, there are many introductory statistical texts on the market. This differs from most in that, although it quickly introduces “advanced” topics such as likelihood and Bayesian methods, it does not do so through formal mathematical proofs, but primarily via graphical methods and simulation. The aim is to produce a text that can be used as a “crash course” in statistics for basic-undergraduate-level readers who want to understand how to analyse their data using modern techniques, but who are not particularly mathematical.

The advent of computers has made an enormous impact on the way in which statistics is done in the real world. Although many introductory statistics courses start by describing simple t-tests and chi-squared tests, most scientists now use more advanced techniques to analyse their data. In particular, there is substantial focus on what are known as general and generalised linear models, as well as (more recently) a renewed interest in Bayesian techniques and methods such as bootstrapping. Instead of explaining overly simple t-tests and the like, leaving underlying theory until later, this book aims to do the opposite. The computer program ‘R’, and its flexible graphical capabilities, are used to give a feel for the underlying theory of statistics, notably the concepts of probability, likelihood, probability density functions, sampling distributions, and hypothesis testing. These principles can then be applied to understanding the various commonly used statistical tests, and easily extended to sophisticated statistics such as generalized linear modelling.

It is intended for the main text of the book to give a general understanding of statistical methodology, as independent as possible of the statistical package used. This text should be interspersed with relevant "R topics": sections of annotated R code, clearly distinguished from the main body of the book. It should be possible to use these R topics by themselves as a set of tutorials: an introduction to basic concepts in R.

Using this book, or any portions of it, in any other R or stats-related projects is encouraged: please feel free to copy and use any parts as you see fit. Books with partially overlapping aims to this are detailed at http://www.ling.uni-potsdam.de/~vasishth/SFLS.html and http://wiener.math.csi.cuny.edu/UsingR/. However, neither of these are editable or redistributable.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Statistical Analysis: an Introduction using R/R basics

Why R?
R is a command-driven statistical package. At first sight, this can make it rather daunting to use. However, there are a number of reasons to learn statistics using this computer program. The two most important are:
 * R is free; you can download it from http://www.r-project.org and install it onto just about any sort of computer you like.
 * R allows you to do all the statistical tests you are likely to need, from simple to highly advanced ones. This means that you should always be able to perform the right analysis on your data.

An additional bonus is that R has excellent graphics and programming capabilities, so can be used as an aid to teaching and learning. For example, all the illustrations in this book have been produced using R; by clicking on any illustration, you can obtain the R commands used to produce it.

A final benefit, which is of more use once you have some basic knowledge of either statistics or R, is that there are many online resources to help users of R. A list is available in the appendix to this book.

How to use this book with R
The main text in this book describes the why and how of statistics, which is relevant whatever statistical package you use. However, alongside the main text, there are a large number of "R topics": exercises and examples that use R to illustrate particular points. You may find that it takes some time to get used to R, especially if you are unfamiliar with the idea of computer languages.

Don't worry! The topics in this chapter and in Chapter 2 should get you going, to the point where you can understand and use R's basic functionality. This chapter is intended to get you started: once you have installed R, there are topics on how to carry out simple calculations and use functions, how to store results, how to get help, and how to quit. The few exercises in Chapter 1 mainly show the possibilities open to you when using R, then Chapter 2 introduces the nuts and bolts of R usage: in particular vectors and factors, reading data into data frames, and plotting of various sorts. From then on, the exercises become more statistical in nature.

If you wish to work straight through these initial exercises before statistical discussion, they are collected here. Note that when working through R topics online, you may find it more visually appealing if you set up wikibooks to display R commands nicely. If the R topics get in the way of reading the main text, they can be hidden by clicking on the arrow at the top right of each box.

Starting R
If you don't already have R installed on your computer, download the latest version for free from http://www.r-project.org, and install the base system. You don't need to install any extra packages yet. Once you have installed it, start it up, and you should be presented with something like this: R version 2.11.1 (2010-05-31) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license' or 'licence' for distribution details.

Natural language support but running in an English locale

R is a collaborative project with many contributors. Type 'contributors' for more information and 'citation' on how to cite R or R packages in publications.

Type 'demo' for some demos, 'help' for on-line help, or 'help.start' for an HTML browser interface to help. Type 'q' to quit R.

> You are now in an R session. R is a command-driven program, and the ominous-looking ">" character means that R is now waiting for you to type something. Don't be daunted. You will soon get the hang of the simplest commands, and that is all you should need for the moment. And you will eventually find that the command-line driven interface gives you a degree of freedom and power that is impossible to achieve using more "user-friendly" packages.

Setting up wikibooks
Before you start on the main text, we recommend that you add a few specific wikibooks preferences. The first three lines will display the examples of R commands in a nicer format. The last line gives a nicer format to figures consisting of multiple plots (known as subfigures). You can do this by creating a user CSS file, as follows. pre {padding:0; border: none; margin:0; line-height: 1.5em; } .code .input ol {list-style: none; font-size: 1.2em; margin-left: 0;} .code .input ol li div:before {content: "\003E \0020";} table.subfigures div.thumbinner, table.subfigures tr td, table.subfigures {border: 0;}
 * Make sure you are logged in (and create yourself an account if you do not have one already).
 * Visit your personal css stylesheet, at Special:MyPage/skin.css.
 * Click on "Edit this page".
 * Paste the following lines into the large edit box
 * If you know any CSS, make any alterations you like to this stylesheet.
 * Finally save the page by clicking on "Save page",

Enough! Let's move on to the main text.


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Statistical Analysis: an Introduction using R/Chapter 1

Why statistics?
Figure 1.1 shows one of the standard sets of data available in the R statistical package. In the 1920s, braking distances were recorded for cars travelling at different speeds. Analysing the relationship between speed and braking distance can influence the lives of a great number of people, via changes in speeding laws, car design, and the like. Other datasets, for example concerning medical or environmental information, have an even greater impact on humans and our understanding of the world. But real-world data are often "messy", as shown in the plot. Most people looking at the plot would be happy to conclude that speed and stopping distance are linked in some way. However, this cannot be the whole story because even at the same speed, different stopping distances were recorded.

This combination of intuitively sensible patterns and messiness is perhaps one of the most common scientific results. Statistical analysis can help us to clarify and judge the patterns we think we see, as well as revealing, out of the mess, effects that may be otherwise difficult to discern. It can be used as a tool to explain the world around us, and perhaps equally importantly, to convince others of the correctness of our explanations. This emphasis on informed judgement and convincing others is important: ideally, statistics should aid a clear explanation, rather than blind by force of argument.

A common misconception of statistical analysis is that it is necessarily technical and "hard to understand". Indeed "I don't understand statistics" is a cry frequently heard. But if an analysis can only be understood by statisticians, it has, to a great extent, failed. For an analysis to convince an audience, one or more reasonable explanations for a situation should carefully and comprehensibly lay down. Statistical analysis of data (which has, ideally, been collected to test these explanations) can then be used to justify a conclusion that can be generally agreed upon. As a rule, the simpler the explanation - the more parsimonious - the more it is to be preferred. Hence a good explanation is one which is sensible and comprehensible to an informed observer, which makes clear predictions, but is as simple as possible.

This book is intended to teach you how to formulate and test such "explanations" in a statistical manner. This is the basis of statistical analysis.

The statistical method
As with the "scientific method", to which it bears a number of similarities, it is open to debate whether or not there exists a universal "statistical method". Nevertheless, most statistical analyses involve questioning some aspect of the world, coming up with potential answers to these questions and then formalising these explanations by proposing statistical models that may account for particular aspects of the data.

Hence the three major parts to an analysis are
 * 1) Deciding the questions you wish to tackle (or more generally, the aim of the analysis)
 * 2) Proposing some reasonable explanations which have the potential to answer these questions
 * 3) Formalise these explanations as statistical models
 * 4) Collecting appropriate data
 * 5) (Using more or less technical methods) test the models using the data.

For example ***

Hence the majority of analyses that you are likely to perform assume, either explicitly or implicitly, an underlying "statistical model". So understanding statistical models, and what they entail, is fundamental to understanding statistics.

Statistical models
Chapter 3 discusses statistical models in greater detail, but it is worth introducing some general concepts here. Models provide some predictive ***. What distinguishes a statistical model is that there is also an element of uncertainty in the process. Hence statistical models consist of two components: one which is fixed, and one which captures the uncertainty. This is

As an example, we can return to the situation depicted in Figure 1.1. A point to which we will return many times, is that a good understanding of the background, or context of the data is essential to any analysis. In this case, it is important to know that the data concerns speed and braking distances of cars. Our general knowledge about driving can then guide our choice of model. In particular, we can probably guess that it seems reasonable to model a situation in which speed has an effect on braking distance (rather than, for example, the other way around). It seems reasonable to treat speed as something that is determined by other factors, and outside the scope of our analysis ***.

Hence an initial, reasonable model might assume that the braking distance is determined by speed, plus some sort of random error. To model this precisely, to construct an imaginary set of braking distances given a set of speeds, we need to be more precise. In particular, we need to specify how a speed affects distance, and what the error looks like***.

Later on we'll see how to specify this mathematically. For the moment, we'll do it graphically and by simulation

Common to assume "Normal" error ****

e.g. maybe no effect at all: there is a fixed braking distance regardless of speed: the only reason why distance vary is due to chance error. => graphically show

we need to be more precise than this: we

one of the most important things to It is reasonable to 1.2(a-d). In each of the four figures, a different statistical model has been proposed: the predictive part of each model is shown as a red line, and the uncertainty (the fluctuations attributed to chance or error) shown as light blue lines. In all cases, the model has assumed that the uncertainty is all in the effect of braking distance (the light blue lines are all vertical).

To show the uncertainty on its own, the right hand plot in each figure shows the residuals ***.
 * The straight (orange) line. A linear relationship is perhaps the simplest way that a relationship between, which is why ***. simplest explanation for the pattern is that stopping distance is directly proportional to speed, so that for every extra mile-per-hour, stopping distance increases by a fixed amount: graphically, this represents a straight line on the plot. It is common to.
 * The curved (red) line. However, this has not taken into account any : perhaps . As remarked before, this is not enough to explain all the data: It may be less clear that lowess is a model ****.

The "cars" example highlights the importance of prior knowledge when doing statistics. Data should not be treated as pure "numbers", but considered in context. Here, because we know that the data are the outcome of a relatively well-understood physical system, we should be led to ask if a particular relationship is expected, which then encourages us to use a simple quadratic fit.

Thus in any analysis it is vital to understand the data: to know where they come from, how they relate to the real world, and — by plotting or other forms of “exploratory data analysis” — what they look like. Indeed, plotting data is one of the most fundamental skills in a statistical analysis: it is a powerful tool for revealing patterns and convincing others. If necessary this can then be backed up by more formal, mathematical techniques. The use of graphics in this book is twofold: not only can they be used to explore data, but formal statistical methods can often be understood and pictured graphically, rather than by heavy use of mathematics. This is one of the principal aims of this book.

The Scientific Method
"The statistician cannot excuse himself from the duty of getting his head clear on the principles of scientific inference, but equally no other thinking man can avoid a like obligation."

- R. A. Fisher

This book focuses on the important role of statistics in the scientific method. Fundamentally, science involves carefully testing a number of rational explanations, or "hypotheses", that purport to explain observed phenomena. Usually, this is done by proposing plausible hypotheses and then trying to eliminate one or more of them by careful experimentation or data collection (this is known as the Hypothetico-deductive method). This means that a basic requirement for a scientific hypothesis is that it can be proved wrong: that it is, in Popper's words "falsifiable". In this chapter, we will see that it is logically impossible to "prove" that a hypothesis is correct; nevertheless, the more tests that a hypothesis passes, the more we should be inclined to believe it.

Ideally, scientific research involves a repeated process of generating hypotheses, eliminating as many as possible, and refining the remaining ones, in a process known as "strong inference" (cite Platt). There are two very different steps involved here: a rather speculative one in which hypotheses are produced or refined, and a strictly logical one in which they are eliminated.

These two steps have their counterparts in statistical analysis. The branch of statistics concerned with hypothesis testing is designed to identify which hypotheses seem improbable, and hence may be eliminated. The branch of statistics concerned with exploratory analysis is designed to identify plausible explanations for a set of data. While we will begin our discussion of these techniques separately, it should be emphasised that in practice, the distinction is not so clear-cut. These two branches of statistical practice are better seen as extremes of a continuum of techniques available to the researcher. For example, many hypotheses involve numerical parameters, such as the slope of a best fit line. Statistical estimation of such parameters may be seen as a test of a hypothesis, but may also be seen as a suggestion for a refined or even novel explanation of the facts.

Hypothesis testing
To properly test a hypothesis, the right sort of data need to be collected. In fact, the targeted collection of data and (where possible) the careful design of experiments, is probably the most important process in science. This need not be difficult. For example, imagine that our hypothesis is that (for genetic reasons) a person cannot have both blond hair and brown eyes. This hypothesis can be disproved by a single observation of a person with that combination of features. Table 1.1 shows the results of a 1974 survey of students from the University of Delaware. As with any test, we need to make a few assumptions about this data, for example that students with dyed hair have not been included, or are listed under their original hair colour. If this is the case, then we can see immediately that we can reject the blond&brown:impossible hypothesis: there were 7 students with brown eyes and blond hair.

Imagine, however, if the survey had not revealed any students with brown eyes and blond hair. Although this would be consistent with our hypothesis, it would not be enough to prove it correct. It could be that brown eyes and blond hair are merely very rare, it being pure chance that we did not see any. This is a general problem. It is impossible to be sure that a hypothesis is correct: there may always be another, very similar explanation for the same observations.

However, as seen in this example, it is possible to reject hypotheses. For this reason, science relies on eliminating hypotheses. Often, therefore, scientists construct one or more hypotheses which they believe not to be the case, purely in order to be rejected. If the aim of a study is to convince other people of one particular theory or hypothesis, then a good way to do so is to define hypotheses which encompass as many reasonable alternative explanations as can be envisaged. If all of these can be disproved, then the remaining, original hypothesis becomes much more convincing.

The null hypothesis
In most scientific observations, there is an element of chance, and so the most important hypothesis to try to eliminate – at least initially – is that the observed data are due to chance alone. This is usually known as the null hypothesis.

In our initial example, the null hypothesis is relatively obvious: it is that there is no association between hair and eye colour (any seeming associations are purely due to chance). But constructing an appropriate null hypothesis is not always so easy. Here are three examples which we will investigate later, ranging from the simple to the highly complex.

 * 1) The sex-ratio among children born in a hospital (e.g. on 18 December 1997 in the Mater Mother's Hospital in Brisbane, Australia, a record 44 children were born, of which 18 were female). A reasonable null hypothesis might be that males and females are equally probable, regardless of sex. Since it is known that humans usually have a male-biased sex ratio, a different null hypothesis (say 51% males) may be more reasonable.

 * 1) The relationship seen in  Figure 1.1 between speeds and stopping distances in cars. A reasonable null hypothesis might be that there is no relationship between the speed of a car and its stopping distance. However, Car stopping distances (no ``association'' between x & y) - More difficult, because error distribution is unknown. here's one way of doing it: could e.g. take ranks of x & ranks of y. Or sample.

In both cases, we need to *model* the null hypothesis: what would we expect if

 * 1) Deaths and serious injuries in cars in the UK from 1969-1985.  Figure 1.2 shows that Seatbelts **** More complex null models - e.g. seatbelt - if we fit a st. line, we need to make some assumption about variation from the line. Or we can take the actual values as representative of the variation Here is a more complex that has affected most of the population in the UK is the introduction of compulsory wearing of seatbelts in cars: a law that came into force on 31st January 1983. Null model involves other factors (e.g. petrol price)

We can model the null hypothesis as long as we have enough detail. **what do you need for different egs**. Because there is random error, we need to do this lots of times. We will see that a lot of statistics relies on rejecting the null hypothesis based on how often it gives results like those observed.

Other hypotheses
The same goes for others, e.g. seatbelt??? Comparing models

Types of error

Estimation
Hypotheses may not be a simple yes/no matter, but more sophisticated, e.g.

what sex ratio is suggested by the data? (that's easy, but how confident are we about the accuracy of this estimate?)

cars: we are convinced that there is a straight-line relationship between speed & braking distance: what is the slope of the line (but maybe physics would suggest a different relationship - straight line is usually the default) - already here we are making some sort of choice of model.

MLE brief description ``if the model is correct, what is the most likely value for these parameters?''

Exploratory analysis
An infinite number of models are out there. In combination with good understanding and /parsimony/, can be used to construct hypotheses & *models*. D.F?

E.g. are there better ideas than a straight line fit?

Is this at all sensible (e.g. if cannot go <0)

Anscombe

Outliers? ???residuals (probably not)

what about interactions? Titanic sex vs. class?

Use colours to pick out types

Human eye good at spotting patterns (but ... even if there are none). E.g. time series

even if we have a prediction (model), how well does it fit the assumptions?

Which are important (as opposed to significant) factors?

Conclusion
Should have enough background to describe picture from []

How wide can we throw conclusions
Is each experiment only one data point, etc?


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General Chemistry/Introduction to Quantum Theory

Introduction to Quantum Mechanics
In the late 19th century, many physicists believed that they had made great progress in physics, and there wasn't much more that needed to be discovered. The classical physics at the time was widely accepted in the scientific community. However, by the early 20th century, physicists discovered that the laws of classical mechanics break down in the atomic world, and experiments such as the photoelectric effect completely contradict the laws of classical physics. As a result of these crises, physicists began to construct new laws of physics which would apply to the atomic world; these theories would be collectively known as quantum mechanics. Quantum mechanics, in some ways, completely changed the way physicists view the universe, and it also marked the end of the idea of a clockwork universe (the idea that the universe was predictable).

Electromagnetic Radiation
Electromagnetic radiation (ER) is a form of energy that sometimes acts like a wave, and other times acts like a particle. Visible light is a well-known example. All forms of ER have two inversely proportional properties: wavelength and frequency. Wavelength is the distance from one wave peak to the next, which can be measured in meters. Frequency is the number of wave peaks observed in a given point during a second. The unit for frequency is hertz.

Since wavelength and frequency are inversely related, their product (multiplication) always equals a constant — specifically, 3.0 x 108 m/sec represented by the letter c, which is better known as the speed of light. This relationship is written mathematically as $$c = \lambda f$$, with the greek letter λ (lambda) representing wavelength and the letter $$f $$ representing frequency.

The wavelength and frequency of any specific occurrence of ER determine its position on the electromagnetic spectrum.

As you can see, visible light is only a tiny fraction of the spectrum.

The energy of a single particle of an electromagnetic wave (called a photon) is given by $$E = hf$$, where $$h$$ is Plank's constant and $$f$$ is the frequency. Energy is directly proportional to frequency — doubling the frequency will double the energy.

The Discovery of the Quantum
So far we have only discussed the wave characteristics of energy. However, the wave model cannot account for something known as the photoelectric effect. This effect is observed when light focused on certain metals apparently causes electrons to be emitted. (Photoelectric or solar panels work on this principle.)

For each metal it was found that there is a minimum frequency of electromagnetic radiation that is needed to be shone on it in order for it to emit electrons. This conflicted with the earlier thought that the energy of light was linked only to its intensity. Under that theory, the effect of light should be cumulative - dim light should add up, little by little, until it causes electrons to be emitted. Instead, there is a clear-cut minimum of the frequency of light that triggers the electron emissions.

The implication of this is that the energy of light is tied to frequency, and furthermore that it is quantized, meaning that it carries "packets" of energy in discrete amounts. These packets are more commonly referred to as photons. This observation led to the discovery of the minimum amount of energy that could be gained or lost by an atom. Max Planck named this minimum amount the quantum, plural "quanta", meaning "how much". One photon of light carries exactly one quantum of energy.


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Talk:General Chemistry/Introduction to Quantum Theory

picture of electromagnetic spectrum
i couldn't find a good picture of the ES. could someone find, or make one? thanks. Hoogli 15:52, 14 May 2007 (UTC)

Since this is a general chem book, and introductory, explanations of the symbology used on the charts, diagrams, and illustrations really is necessary. Whoever posted the image should also caption it. Aramis1250 (talk) 04:48, 8 October 2008 (UTC)

More Evidence for a Particle Theory of Energy
Maybe it's just me, but this paragraph made little sense to me.

“If light acted only as a wave, then there should have been a continuous rainbow created by the prism. Instead, there were definite lines created by different wavelengths. This is because electrons release specific wavelengths of light when moving from an excited state to a ground state.”

The first sentence talks about a property of light, suggesting what would be seen if light acted as a wave. The second sentence explains that the definite lines in the spectrum implies light is not a wave. The third sentence then attributes the definite lines to a completely different phenomena, the fact that electrons can only occupy specific orbits and that they may emit photons when changing orbits. It seems to me that the connection between quanta of light and changing electron orbits needs to be expanded.

The phenomenon described in this section is due to the quantized nature of electron orbitals, not the wave/particle nature of light. Perhaps the author was thinking of the slit experiments? Unless someone has other evidence that actually corresponds to light, this section should be deleted or moved. Clockworkcity (discuss • contribs) 19:51, 5 July 2017 (UTC)

went ahead and deleted it, will be covered in the electron section anyway. Clockworkcity (discuss • contribs) 15:51, 6 July 2017 (UTC)

Wave Mechanics
Since a discussion of wavelengths is included on this page, it might be helpful to note that quantum mechanics is also known as wave mechanics. https://archive.org/details/prenticehallchem00henr/page/336


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General Chemistry/The Quantum Model

Uncertainty
It turns out that photons are not the only thing that act like waves and particles. Electrons, too, have this characteristic, known as wave-particle duality. Electrons can be thought of as waves of a certain length, thus they would only be able to form a circle around the nucleus at certain distances that are multiples of the wavelength. Of course, this brings up a problem: are electrons particles in a specific location, or waves in a general area? Werner Heisenberg tried using photons to locate electrons. Of course, when photons reach electrons, the electrons change velocity, and move to an excited state. As a result, it is impossible to precisely measure the velocity and location of an electron at the same time. This is known as the observer effect. This is frequently confused with the Heisenberg Uncertainty Principle, which goes even further, saying that there are limits to the degree to which both the position and momentum of a particle can even be known. This is due to the fact that electrons cannot exhibit both their wave and particle properties at the same time when being observed to interact with their surroundings. The momentum of an electron is proportional to its velocity, but based on its wave properties; its position is based on its particle position in space. The Heisenberg uncertainty principle is a kind of scientific dilemma: the more you know about something's velocity, the less you know about its position; and the more you know about its position, the less you know about its velocity. The significance of this uncertainty is that you can never know exactly where an atom's electrons are, only where they are most likely to be.

On the tiny scale of an atom, the particle model of an electron does not accurately describe its properties. An electron tends to act more like a water wave than a billiard ball. At any one moment in time the ball is in some definite place; it is also moving in some definite direction at a definite speed. This is certainly not true for waves or electrons in general. The Heisenberg uncertainty principle states that the exact position and momentum of an electron cannot be simultaneously determined. This is because electrons simply don't have a definite position, and direction of motion, at the same time!

One way to try to understand this is to think of an electron not as a particle but as a wave. Think of dropping a stone into a pond. The ripples start to spread out from that point. We can answer the question "Where is the wave?" with "It's where you plonked the stone in". But we can't answer the question "What direction is the wave moving?" because it's moving in all directions. It's spreading out. Now think of a wave at the seaside. We know the direction of motion. It's straight in towards the beach. But where is the wave? We can't pinpoint an exact location. It's all along the water.

The Wave Function
If we can never know exactly where an electron is, then how do we know about the way they orbit atoms? Erwin Schrödinger developed the Quantum Mechanical model, which describes the electron's behavior in a given system. It can be used to calculate the probability of an electron being found at a given position. You don't know exactly where the electron is, but you know where it is most likely and least likely to be found. In an atom, the wave function can be used to model a shape, called an orbital, which contains the area an electron is almost certain to be found inside.

Orbitals
In the following sections, we will learn about the shells, subshells, and orbitals that the electrons are in. Try not to get confused; it can be difficult. Understanding this information will help you to learn about bonding, which is very important.

Each electron orbiting in an atom has a set of four numbers that describe it. Those four numbers, called quantum numbers, describe the electron's orbit around the nucleus. Each electron in an atom has a unique set of numbers, and the numbers will change if the electron's orbit is altered. Examples are if bonding occurs, or an electron is energized into a higher-energy orbit. In the next chapter, we will learn the meaning of those four values.

title: General Chemistry/The Quantum Model


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Talk:The Poetry of Gaius Valerius Catullus

 * '''Type: Reference, Self-study, Textbook, Leisure
 * '''Audience: Latin scholars and people interested in or studying Latin literary works. General age-group is 18 years upwards.
 * Provide all of the works of Catullus - thoroughly annotated.
 * Vocabulary for difficult language - including cross-links to Wiktionary.
 * Uncensored translations and detailed textual analysis for students of Latin literature.
 * Detailed references to Roman life and explanations relevant to understanding the literature.

Instructions
Please use this table format for the poems.


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General Chemistry/Reaction Rates

Introduction
Reaction rates of a chemical system provide the underpinnings of many theories in thermodynamics and chemical equilibria.

Elementary reactions are one-step processes in which the reactants become the products without any intermediate steps. The reactions are unimolecular (A → products) or bimolecular (A + B → products). Very rarely, they could be trimolecular (A + B + C → products), but this is not common due to the rarity of three molecules colliding at the same time.

A complex reaction is made up of several elementary reactions, with the products of one reaction becoming the reactants of the next until the overall reaction is complete.

Rate Equation
Note [A] is raised to the power of m, its coefficient, just like an equilibrium expression. The rate of the reaction may rely on the molar coefficients of the reactant species, but it might not. However, for an elementary reaction, the concentrations of the species A and B are always raised to their molar coefficients. This only applies to elementary reactions, which is a very important distinction to make.

Order
The order of an equation is what the concentration of a substance is raised to in the rate equation. The greater the number, the greater effect it will have on rate. For example, zero order equations do not effect the rate. To find the order, you must alter one concentration and keep the rest the same, Dividing gives an equation which can be used to solve for the order. To find overall order, simply add all orders together.

Zero-Order Equations
Zero-order equations do not depend on the concentrations of the reactants.

First-Order Equations
First-order equations depend on the concentration of a unimolecular reaction.

Second-Order Equations
Second-order equations depend on the two concentrations of a bimolecular reaction.

Equilibrium
Equilibrium will occur when the forward and reverse rates are equal. As you may have already noticed, the equilibrium expression of a reaction is equal to the rate equations divided.

Understanding kinetics explains various concepts of equilibrium. Now it should make sense why increasing the reactant concentration will make more products. The forward rate increases, which uses up reactants, which decreases the forward rate. At the same time, products are made, which increases the reverse reaction, until both reaction rates are equal again.

Arrhenius Equation
The Arrhenius equation determines a rate coefficient based on temperature and activation energy. It is surprisingly accurate and very useful. The Arrhenius equation is:

$$k = Ae^{-E_a / RT}$$

$$E_a$$ is the activation energy for the reaction, in joules per mole. $$R$$ is the Universal Gas Constant, $$T$$ is the temperature (in kelvin), and $$A$$ is the prefactor. Prefactors are usually determined experimentally.

title: General Chemistry/Reaction Rates


text:
Talk:C Programming/Operators and type casting

So you can't use modulus with non-integers? - 212.149.219.87 18:01, 14 May 2007 (UTC)

Nope. How would it work? There is an fmod function in  that approximates floating-point modulus. -- 76.99.235.121 (talk) 22:54, 19 March 2009 (UTC)

Clarity
I found this page a little difficult to understand. Examples for each operator would be very helpful. Also, I know this book is not intended for programming newbs, but I have programmed a bit with python and don't know what an "identifier" is. I couldn't find it in the last few pages either. hypergeek14 (discuss • contribs) 17:55, 7 June 2011 (UTC)
 * I agree; I have a degree in Computer Science and I had to read this page a couple of times in multiple sittings. Finsternish (discuss • contribs) 13:20, 24 August 2015 (UTC)

Agreed. This section of the book assumes much more background on the reader's part than the rest. The section heading 'simple math' is misleading. Suggest renaming to something more appropriate and moving to a different section.

I agree as well. I don't really think changing the title would be useful. To be useful to a larger audience, this entire entry needs to be rewritten to be less academic and more like the sections leading up to it.

Linux issues
conio.h is Windows/DOS specific, this program won't compile under GNU/Linux.

Shift and rotate
The syntax of the shift left and shift right operators is not clear from this text, which is frustrating if you're trying to test things out as you read them. I had to look that up separately, and was confused about "x >> shift" as there was no variable declared called shift. I think you should make it clear that "shift" refers to the amount by which you are shifting. Finsternish (discuss • contribs) 13:17, 24 August 2015 (UTC) Edited Finsternish (discuss • contribs) 13:18, 24 August 2015 (UTC)


text:
Genetic Information/Introduction to Genetic Information

Introduction There is a growing interest in mathematical study of succession of letters that represent the basic structure of DNA strand. In molecular genetics, these letters are T, G, C and A where they represent thymine, guanine, cytosine and adenine. DNA is an information molecule that codes sequence of deoxyribonucleotides. Where as messenger RNA (mRNA) is an information molecule coding by sequence of ribonucleotides. In the structure of mRNA is a single stranded molecule where uracil, lettered U takes the place of thymine found in double stranded DNA.

The letters in the information molecules are normally written without gaps and when they are taken three at a time, they are called codons. They produce 64 different combinations of bases. It is this coded sequence in the letters, for example, GATCTAC that we will refer to here as genetic information.

Information in this case is about genes. It may also be information about gene products or our understanding of combinations of inherited family traits.

Modern study of genetic information includes information that regards carrier status and information that is derived from science laboratory tests. The science laboratory tests are often intended to identify mutations or changes in specific genes or chromosomes. There are three ways in which genetic information is gathered in a scientific study. One is usually done by series of physical medical examinations while the other is by taking family histories and, lastly, by engaging direct analysis of genes or chromosomes through detailed biometric processes.

The end result in the flow of genetic information is protein, that is, a functional molecule consisting of sequence of amino acids.

<>

Table of Contents


text:
General Chemistry/Periodicity and Electron Configurations

Blocks of the Periodic Table
The Periodic Table does more than just list the elements. The word periodic means that in each row, or period, there is a pattern of characteristics in the elements. This is because the elements are listed in part by their electron configuration. The Alkali metals and Alkaline earth metals have one and two valence electrons (electrons in the outer shell) respectively. These elements lose electrons to form bonds easily, and are thus very reactive. These elements are the s-block of the periodic table. The p-block, on the right, contains common non-metals such as chlorine and helium. The noble gases, in the column on the right, almost never react, since they have eight valence electrons, which makes it very stable. The halogens, directly to the left of the noble gases, readily gain electrons and react with metals. The s and p blocks make up the main-group elements, also known as representative elements. The d-block, which is the largest, consists of transition metals such as copper, iron, and gold. The f-block, on the bottom, contains rarer metals including uranium. Elements in the same Group or Family have the same configuration of valence electrons, making them behave in chemically similar ways.

Causes for Trends
There are certain phenomena that cause the periodic trends to occur. You must understand them before learning the trends.

Effective Nuclear Charge
The effective nuclear charge is the amount of positive charge acting on an electron. It is the number of protons in the nucleus minus the number of electrons in between the nucleus and the electron in question. The nucleus attracts the electron, but other electrons in lower shells repel it (opposites attract, likes repel).

Shielding Effect
The shielding (or screening) effect is similar to effective nuclear charge. The core electrons repel the valence electrons to some degree. The more electron shells there are (a new shell for each row in the periodic table), the greater the shielding effect is. Essentially, the core electrons shield the valence electrons from the positive charge of the nucleus.

Electron-Electron Repulsions
When two electrons are in the same shell, they will repel each other slightly. This effect is mostly canceled out due to the strong attraction to the nucleus, but it does cause electrons in the same shell to spread out a little bit. Lower shells experience this effect more because they are smaller and allow the electrons to interact more.

Coulomb's Law
Coulomb's law is an equation that determines the amount of force with which two charged particles attract or repel each other. It is $$F = \frac{k Q_1 Q_2}{r^2}$$, where $$Q$$ is the amount of charge, $$r$$ is the distance between charges, and $$k$$ is a constant. For atoms, the charges are typically described as integer multiples (positive for protons, negative for electrons) of the elementary charge $$e$$, which is 1.6022 x 10-19 coulombs. You can see that doubling the distance would quarter the force.

Trends in the Periodic table
Most of the elements occur naturally on Earth. However, all elements beyond uranium (number 92) are called trans-uranium elements and never occur outside of a laboratory. Most of the elements occur as solids or gases at STP. STP is standard temperature and pressure, which is 0° C and 1 atmosphere of pressure. There are only two elements that occur as liquids at STP: mercury (Hg) and bromine (Br).

Bismuth (Bi) is the last stable element on the chart. All elements after bismuth are radioactive and decay into more stable elements. Some elements before bismuth are radioactive, however.

Atomic Radius
Leaving out the noble gases, atomic radii are larger on the left side of the periodic chart and are progressively smaller as you move to the right across the period. As you move down the group, radii increase.

Atomic radii decrease along a period due to greater effective nuclear charge. Atomic radii increase down a group due to the shielding effect of the additional core electrons, and the presence of another electron shell.

Ionic Radius
For nonmetals, ions are bigger than atoms, as the ions have extra electrons. For metals, it is the opposite.

Extra electrons (negative ions, called anions) cause additional electron-electron repulsions, making them spread out farther. Fewer electrons (positive ions, called cations) cause fewer repulsions, allowing them to be closer.

Ionization Energy
Ionization energy is also a periodic trend within the periodic table organization. Moving left to right within a period or upward within a group, the first ionization energy generally increases. As the atomic radius decreases, it becomes harder to remove an electron that is closer to a more positively charged nucleus.

Ionization energy decreases going left across a period because there is a lower effective nuclear charge keeping the electrons attracted to the nucleus, so less energy is needed to pull one out. It decreases going down a group due to the shielding effect. Remember Coulomb's Law: as the distance between the nucleus and electrons increases, the force decreases at a quadratic rate.

It is considered a measure of the tendency of an atom or ion to surrender an electron, or the strength of the electron binding; the greater the ionization energy, the more difficult it is to remove an electron. The ionization energy may be an indicator of the reactivity of an element. Elements with a low ionization energy tend to be reducing agents and form cations, which in turn combine with anions to form salts.

Electron Affinity
Electron affinity is highest in the upper left, lowest on the bottom right. However, electron affinity is actually negative for the noble gasses. They already have a complete valence shell, so there is no room in their orbitals for another electron. Adding an electron would require creating a whole new shell, which takes energy instead of releasing it. Several other elements have extremely low electron affinities because they are already in a stable configuration, and adding an electron would decrease stability.

Electron affinity occurs due to the same reasons as ionization energy.

Electronegativity
Electronegativity is how much an atom attracts electrons within a bond. It is measured on a scale with fluorine at 4.0 and francium at 0.7. Electronegativity decreases from upper right to lower left.

Electronegativity decreases because of atomic radius, shielding effect, and effective nuclear charge in the same manner that ionization energy decreases.

Metallic Character
Metallic elements are shiny, usually gray or silver colored, and good conductors of heat and electricity. They are malleable (can be hammered into thin sheets), and ductile (can be stretched into wires). Some metals, like sodium, are soft and can be cut with a knife. Others, like iron, are very hard. Non-metallic atoms are dull, usually colorful or colorless, and poor conductors. They are brittle when solid, and many are gases at STP. Metals give away their valence electrons when bonding, whereas non-metals take electrons.

The metals are towards the left and center of the periodic table—in the s-block, d-block, and f-block. Poor metals and metalloids (somewhat metal, somewhat non-metal) are in the lower left of the p-block. Non-metals are on the right of the table.

Metallic character increases from right to left and top to bottom. Non-metallic character is just the opposite. This is because of the other trends: ionization energy, electron affinity, and electronegativity.


text:
Finite Model Theory/Character

Prerequisites

 * Basic knowledge about syntax and semantics of mathematical logic, like the concepts of Alphabet, first order language, Structure, Interpretation and Model. (see Structure)
 * The concept of axiom systems, like the axioms of real numbers, what group theory is about etc.(see Axiom system)

Definition
Model Theory is the branch of Mathematical Logic that deals with the relation among formal expressions (syntax) of a logic and their meaning (semantics). This relation is established via the existence of an Interpretation of the expressions that obeys their meaning. Such an Interpretation is said to be a Model for these expressions.

Usually mathematical subjects are about a single axiom system, whereas Model Theory is on characteristic properties of axiom systems, e.g. the theory of real numbers is not axiomatisable in first order language (just in case you ever have wondered about their 10th axiom) or the theory of rational numbers is not negation-complete whereas the theory of the reals is (what makes the latter so important).

Interpretations contain a Structure that in turn contains the Universe , that is the set of 'entities' (like {0, 1, 2, 3, ...}) based on which the Interpretation is performed (e.g. the successor-relation or the addition-function is defined). Model Theory when restricted to Interpretations over a finite Universe is said to be Finite Model Theory (FMT).

title: Finite Model Theory/Character


text:
The Poetry of Gaius Valerius Catullus/Authors

Alakazam38
Is a university student studying Ancient History & Archaeology. He has studied Catullus' Carmina in detail, along with many other Latin texts. Other examples include Virgil's Aeneid, Horace's Odes and passages from Livy. Outside of school he has read Propertius and Herodotus as well as Sophocles' Antigone and Medea. He decided to start this WikiBook after encouragement from several members of Wikipedia. This Wikibook was set up with the help of Uncle G.

Storeye
Is also a secondary student. She studies the NSW Latin and Ancient History preliminary courses. She has studied a few of the works of Catullus and Caesar, and has read Pliny, Cicero, and Valerius Maximus amongst others, in classes outside of school. Her interests also spread to ancient Greece and Britain though she hasn't studied them in great depth. She is also at the English Wikipedia under the same username.

Ronnye
Is a high school senior in Fort Wayne IN USA who has been studying Latin since the 7th grade. She took the AP Vergil course last year and is currently enrolled in AP Latin Literature (Catullus and Cicero). Besides her interests in the Classics, Ronnye has done independent research in biology and plans to be pre-med in college next fall ('08).

Christopher
Is also a high school senior in Fort Wayne. This is his first year in AP and we are expecting greatness! He has been our wiki-guru as we try to convert our knowledge of the poems of Catullus into the correct format. As well as his work on things wiki, Christopher has worked for human rights as a long-time member of Amnesty International. Yay!


text:
Korean/RWP/Lesson 6

Help complete it by reading Korean/Writing lessons plan or clicking "edit".

Welcome back! This is the sixth lesson of "Learn to read, write and pronounce Korean". This lesson covers the remaining jamo, all just variations on the letters you already learned.

The diphthong ㅘ (wa)
The vowel ㅘ is pronounced like the wa in swan (IPA: [ wɑ ]) and transcribed as wa.

Exercise
The following Korean words contain ㅘ (wa). Guess what the words mean. Click "Show" to check your answers.

The diphthong ㅝ (weo)
The vowel ㅝ is pronounced like the wa in was (IPA: [ wʌ ]) and transcribed as weo.

Exercise
The following Korean words contain ㅝ (weo). Guess what the words mean. Click "Show" to check your answers.

The diphthong ㅙ (wae)
The vowel ㅙ (wae) consists of the vowel ㅗ (o) and the vowel ㅐ (ae), so the vowel ㅙ (wae) is pronounced similarly like the wa in wagon (IPA: [ wæ ]) and transcribed as wae.

Exercise
The following Korean words contain ㅙ (wae). Guess what the words mean. Click "Show" to check your answers.

The diphthong ㅞ (we)
The vowel ㅞ (we) consists of the vowel ㅜ (u) and the vowel ㅔ (e), so the vowel ㅞ (we) is pronounced the whe in when (IPA: [ we ]) and transcribed as we.

Exercise
The following Korean words contain ㅞ (we). Guess what the words mean. Click "Show" to check your answers.

The Tense Consonants
It is very important for Korean language to recognize the difference between basic sound, aspirated sound, and tense sound. It is not aspirated, but is pronounced strong and tense. Tense consonants are derived from their basic form by doubling the basic letter. The difference of the consonants' three aspects can distinguish the meanings of words: For example, 달dal[tal] means 'the moon', 탈tal[tʰal] means 'a mask', and 딸ttal[t͈al] means 'a daughter'.

Exercise
The following Korean words contain the tensed consonants. Guess what the words mean.

The diphthong ㅚ (oe)
The vowel ㅚ is pronounced like the ö in the German word schön (IPA: [ ø ]) or the wea in weather (IPA: [ we ]), and transcribed as oe. Its appearance shows that it was originally a diphthong of ㅗ (o) and ㅣ (i) - but you can just consider that ㅣ acts as the umlaut mark (¨) of German language.

Exercise
The following Korean words contain ㅚ (oe). Guess what the words mean. Click "Show" to check your answers.

The diphthong ㅟ (wi)
The vowel ㅟ is pronounced like the whi in whiskey (IPA: [ wi ]) or the u in the French word chute (IPA: [ y ]), and transcribed as wi.

Exercise
The following Korean words contain ㅟ (wi). Guess what the words mean. Click "Show" to check your answers, as usual.

You can check your answers by clicking at "Show" above, as usual.

The diphthong ㅢ (ui)
The vowel ㅢ consists of the vowel ㅡ (eu) and the vowel ㅣ (i), so when you pronounce this vowel ㅢ, pronounce ㅡ and move your tongue to pronounce ㅣ softly (IPA: [ ɯi ]). The vowel ㅢ transcribed as ui because of efficiency. Sometimes this is pronounced simply [ e ].

Exercise
The following Korean words contain ㅢ (ui). Guess what the words mean. Click "Show" to check your answers.

End of lesson 6
Congratulations! You have now learned the whole Korean alphabet! If you'd like to review or brush up, visit the course summary.

title: Korean/RWP/Lesson 6


text: The Poetry of Gaius Valerius Catullus/94


text:
The Poetry of Gaius Valerius Catullus/94/94A

Text & Translation
Meter – Elegiac couplet

 * Note: "mentula" means penis, but to give it a greater sense of the original Latin, i.e. vulgar common slang "cock" has been used.

Line 1

 * Mentula moechatur - Mentula/a cock is an adulterer.

This play on the man's nickname and the word for 'penis' is deliberately implemented to catch our attention. The word order is then reversed to further emphasise it, with the capital 'M' taken away to show that Catullus is only talking about a penis this time.

 * Certe. - Certainly.

This abrupt use of the word imprints the statement to the reader.

Line 2

 * dicunt - they say

"They" is understood here as a saying, i.e. "you know what they say..." and is designed to represent a universal agreement to the statement above, adding further to Catullus' mockery of Mentula.

Scansion Note
The elision at the end of Line 2 rushes through the end of the poem. Combined with the alliteration of olera olla it is a striking way to end the poem. The use of two dactyls also adds to the speed at which the poem ends.

 * Syllaba anceps has been marked with a circumflex (â).


text:
Korean/RWP/Lesson 5

Welcome back! This is the fifth lesson of "Learn to read, write and pronounce Korean". This lesson covers some variations on the letters you already learned.

The consonant ㅋ (kieuk)
The first new letter is the consonant ㅋ (kieuk):

The first new letter to learn is ㅋ (kieuk). It looks and similar to ㄱ (giyeok) but has an additional horizontal line, which indicates that ㅋ is aspirated. That is, ㅋ is pronounced with a burst of air. As you may recall, the difference between aspirated and unaspirated sounds is easily demonstrated by putting a hand or a lit candle in front of your mouth and saying "can" ([kʰæn]) and then "scan" ([skæn]). You should either feel a puff of air or see a flicker of the candle flame with "can" that does not appear with "scan". So, ㅋ (kieuk) is pronounced like the c in can.

Exercise
Try writing the following words in Korean:

Try guessing the meaning of following Korean words:

The vowel ㅑ (ya)
The first new vowel to learn is ㅑ (ya):

The first vowel to learn is ㅑ (ya). It looks and sounds similar to ㅏ (a). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅑ (ya) is pronounced like the ya in yahoo, the German word ja, and the Russian word я (IPA: [ja]). In the terminology of phonetics, ㅑ (ya) and the other four vowels in this lesson are the iotized versions of vowels you already learned.

Exercise
Try to guess the meaning of the following Korean words:

The consonant ㅌ (tieut)
The next new letter is the consonant ㅌ (tieut):

ㅌ (tieut) looks and similar to ㄷ (digeut) and but has an additional horizontal line, which indicates that ㅌ is aspirated. That is, ㅌ is pronounced with a burst of air. Again, to demonstrate the aspiration, put a hand or a lit candle in front of your mouth and say "tone" ([tʰəʊn]) and then "stone" ([stəʊn]). You should either feel a puff of air or see a flicker of the candle flame with "tone" that does appear with "stone". So, ㅌ (tieut) is pronounced like the t in tone.

Exercise
Try writing the following words in Korean:

Try guessing the meaning of the following Korean words:

The consonant ㅍ (pieup)
The next new letter is the consonant ㅍ (pieup):

ㅍ (pieup) looks and sounds similar to ㅂ (bieup) and but has an additional horizontal line, which indicates that ㅍ is aspirated. That is, ㅍ is pronounced with a burst of air. Again, to demonstrate the aspiration, put a hand or a lit candle in front of your mouth and say "pin" ([pʰɪn]) and then "spin" ([spɪn]). You should either feel a puff of air or see a flicker of the candle flame with "pin" that does appear with "spin". So, ㅍ (pieup) is pronounced like the p in pin.

Exercise
Try writing the following words in Korean:

Try guessing the meaning of the following Korean words and names:

The consonant ㅊ (chieut)
The next new letter is the consonant ㅊ (chieut):

ㅊ (chieut) looks and sounds similar to ㅈ (jieut) and but has an additional horizontal line, which indicates that ㅊ is aspirated. That is, ㅊ is pronounced with a burst of air. Again, to demonstrate the aspiration, put a hand or a lit candle in front of your mouth and say "chin" ([tʃʰɪn]) and then "gin" ([dʒɪn]). Ignoring the difference in use of the vocal cords, you should either feel a puff of air or see a flicker of the candle flame with "chin" that does appear with "gin". So, although there is no precise English sound for ㅊ (chieut), it is pronounced similar to the ch in chin.

Exercise
Try writing the following words in Korean:

Try guessing the meaning of the following Korean words:

The vowel ㅕ (yeo)
The next new vowel to learn is ㅕ (yeo):

The vowel ㅕ (yeo) looks and sounds similar to ㅓ (eo). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅕ (yeo) is pronounced like the you in young.

Exercise
Try to guess the meaning of the following Korean words:

Try to write the following in Korean:

The vowel ㅛ (yo)
The next new vowel to learn is ㅛ (yo):

The vowel ㅛ (yo) looks and sounds similar to ㅗ (o). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅛ (yo) is pronounced like the yo in yoga.

Exercise
Try to guess the meaning of the following Korean words:

Try to write the following in Korean:

The vowel ㅠ (yu)
The next new vowel to learn is ㅠ (yu):

The vowel ㅠ (yu) looks and sounds similar to ㅜ (u). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅠ (yu) is pronounced like the English word you.

Exercise
Try to guess the meaning of the following Korean words and names:

Try to write the following in Korean:

The vowel ㅖ (ye)
The next new vowel to learn is ㅖ (ye):

The vowel ㅖ (ye) looks and sounds similar to ㅔ (e). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅖ (ye) is pronounced like the Ya in the English word Yale.

Exercise
Try to guess the meaning of the following Korean words and names:

Try to write the following in Korean:

The vowel ㅒ (yae)
The next new vowel to learn is ㅒ (yae):

The vowel ㅒ (yae) looks and sounds similar to ㅐ (ae). The additional short line indicates that the pronunciation begins with a short "y" sound (IPA: [j]). So, ㅒ (yae) is pronounced similar to the ye in the English word yet.

Exercise
Try to guess the meaning of the following Korean words and names:

Try to write the following in Korean:

End of lesson 5
Very good! You have now learned almost all of the Korean letters. When you are ready, continue to the last lesson, Lesson 6, to learn the last of the letters: the "tense" consonants and other digraphs. (It will be easy because they are all based on letters you already know.)

title: Korean/RWP/Lesson 5


text: Korean/stroke order and sound

title: Korean/stroke order and sound


text:
English for B2 Students/Unit 9

= I want to be a scientist =

Aim of this lesson: Identify and use verb patterns. Verb patterns describe the form of verb which follows the first verb. The second verb can be in the infinitive, gerund or bare infinitive (without 'to'). Also you will learn to talk about jobs: job titles and routines.

Speaking about jobs
''Work with a partner, if you can, and ask/answer these questions. If you're on your own you should think about these questions and write your answers instead.''

 * Do you work or study?
 * How long have you been working / studying? (note. use the Present Perfect when answering this question)
 * What job did you want to do when you were 10 years old?
 * Are there any jobs you would hate doing? Why? (note. use the 2nd Conditional when answering this question)
 * In your job, what do you enjoy doing?

Grammar - Verb Patterns
In English when we use two verbs together we have to decide what form the second verb should take. We can choose between infinitive (to eat), gerund (eating) or infinitive without to (eat). The form of the second verb depends on the first verb. This table shows some common verbs and the form that the second verb should take.

Examples of Verb Patterns

 * Roger suggested going to the cinema (suggest + ing)
 * What do you want to eat? (want + infinitive)
 * I'm sorry but I must go now. (must + infinitive without 'to')
 * She hates swimming at 12:00pm ( hate + ing )

Vocabulary - Jobs
Look at the table below. Do you know the correct definitions for these jobs? Click on the 'show' button on the right for the correct answers.

Exercises
Try these questions to see if you know your verb patterns and vocabulary about jobs. The answers can be found here.

Complete the following sentences using the correct modal verb:

 * 1) When I am older I would like to live / living in the USA.
 * 2) Have you decided what to buy / buying / buy yet?
 * 3) It's a secret.  You mustn't to tell / telling / tell anybody!
 * 4) I suggest to go / going / go to the seaside at the weekend.
 * 5) Please don't forget to close / closing / close the window before you leave.

Grammar Reference
For further information about verb patterns and the use of infinitive, gerund or bare infinitive see ../Using Verbs Together/.

title: English for B2 Students/Unit 9


text:
English for B2 Students/CEF

The Common European Framework of Reference for Languages: Learning, Teaching, Assessment is a guideline to measure achievements in European languages. For information about the background to the CEF see this Wikipedia article.

Levels

 * A Basic User
 * A1 Breakthrough (often known as Elementary)
 * A2 Waystage (often known as Pre-Intermediate)
 * B Independent User
 * B1 Threshold (often known as Intermediate)
 * B2 Vantage (often known as Upper Intermediate)
 * C Proficient User
 * C1 Effective Operational Proficiency (often known as Advanced)
 * C2 Mastery (often known as Proficiency)

title: English for B2 Students/CEF


text:
Dutch/Lesson 18

=More word order=

We have seen that word order depends on quite a few factors in Dutch:

 * 1) inversion
 * 2) in questions
 * 3) for emphasis
 * 4) separation
 * 5) of compound verbs
 * 6) auxiliary
 * 7) rest of the verbal cluster
 * 8) of prefixed verbs
 * 9) of pronominal adverbs
 * 10) subordination

Separation and subordination
What happens when the above factors are combined, for example if a separable verb is put in a subordinate clause? Notice what happens to the persoonvorm : that part of the verb that carries the ending:

 * Inseparable vertrekken:

 * Direct:
 * Hij vertrekt morgen naar Berlijn
 * Indirect:
 * Ik zeg dat hij morgen naar Berlijn vertrekt

 * Separable aankomen:

 * Direct:
 * Hij komt morgen in Berlijn aan
 * Hij komt morgen aan in Berlijn
 * Indirect:
 * Ik geloof dat hij morgen in Berlijn aan komt

As you see in the subordinate clause the verb is put at the end and is no longer separated. At least this is true for the present and simple past tense. If we use the future tense the situation is somewhat different:

 * Inseparable vertrekken:

 * Direct:
 * Hij zal morgen vertrekken
 * Indirect:
 * Ik betwijfel of hij morgen vertrekken zal
 * Ik betwijfel of hij morgen zal vertrekken

 * Separable aankomen:

 * Direct:
 * Hij zal morgen aankomen
 * Indirect:
 * Ik zeg dat hij morgen aan zal komen
 * Ik zeg dat hij morgen aankomen zal
 * Ik zeg dat hij morgen zal aankomen

There is considerable variation in word order possible, some with separation, some without and usage varies from region to region and person to person.

In general we can say that all parts of the verb like to be at the end of the sentence, except the persoonsvorm of a direct phrase. When there are many bits and pieces at the end they tends compete for last place.

There are a few restrictions to the latter in the case of modal verbs:

 * ik weet dat hij nog komen moet
 * ik weet dat hij nog moet komen

Both are fine in Dutch, but if we add another auxiliary:

 * ik denk dat hij nog zal moeten komen
 * ik denk dat hij nog komen moeten zal
 * ik denk dat hij nog moeten komen zal

The first is fine, the second rather awkward, the third is not acceptable.

Separable versus inseparable in dependent clauses
Recall that some verbs occur in both a separable and an inseparable form, e.g. doorlopen.

 * Ik loop de school door - takes five minutes to walk through the school physically
 * Ik doorloop de school - takes five years and ends in graduation

In a dependent clause the only difference between the two is in the stress pattern, so that in written language the following sentence can have two pronunciations and two meanings:

 * Ik geloof dat hij de school doorloopt

In such cases of ambiguity Dutch spelling allows the addition of stress marks:
 * Ik geloof dat hij de school dóórloopt - takes five minutes
 * Ik geloof dat hij de school doorlóópt - takes a number of years

Separable infinitives and te
Infinitives are at times used in extended form with te, much like in English they are with to:

 * Dat is moeilijk te lezen - that is hard to read

In such cases separable verbs do separate:

 * Dat is onmogelijk op te schrijven

Such infinitives can express an action that must be performed (as in English something to do). They can even used in an adjectival construction:

 * De onmogelijk op te schrijven tekst werd ter zijde geschoven
 * The text that was impossible to write down, was pushed aside.

title: Dutch/Lesson 18


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English for B2 Students/Aims

= Aims of the units of English for B2 students =

title: English for B2 Students/Aims


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Talk:Computer Programming/Aspect oriented programming

But Aspect Oriented is effectively defines pre-processor! It is one of the strength of Java, lucking of pre-processor. With pre-processor you don't know what exactly is performed. alexsmail 22:02, 14 May 2007 (UTC)


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User talk:74.64.0.2

Please take care not to replace Wikibooks pages with blank content. Blank pages are harmful to Wikibooks because they have a tendency to confuse readers. If it is a duplicate article, please redirect it to an appropriate existing page. If the page has been vandalised, please revert it to the last legitimate version. If you feel that the content of a page is inappropriate, please edit the page and replace it with appropriate content. If you believe there is no hope for the page, please see the deletion policy for how to proceed. If page is no longer useful, insert delete to its top. Thanks for contributing to Wikibooks!

Xania talk 22:13, 14 May 2007 (UTC)

title: User talk:74.64.0.2


text:
English for B2 Students/Introduction

This book will be a text book designed for use by learners of English as a second language. It will be primarily aimed at B2 students. B2 denotes the 4th (of 6) stage of the Common European Framework, an internationally used standard for learning languages. This level is often referred to as Upper Intermediate or Pre-Advanced and students at this level often take the University of Cambridge FCE examination. For more information about CEF levels see here

This text book will be similar to many paper text books currently available such as English File, Inside Out and Language to Go. It will present topics in a step-by-step pattern focusing on key vocabulary, grammar, writing skills, spoken interaction and communication, listening skills and examination practice in preparation for the FCE examination. The book will emphasise the use of everyday English and use standards of English which are familiar to speakers of English around the world - basically this means that vocabulary and grammar of British/Irish and American English may be used although the emphasis will often be directed to the 'easier' standard or the standard that is considered most acceptable for the University of Cambridge examinations.

Contents of English for B2 students
This book focuses on using English in everyday situations. Each unit of the book specialises in a particular part of grammar and a vocabulary topic. Units in this book make use of a variety of reading and listening texts. Each unit ends with practice exercises which should be attempted before the next unit is attempted. At the end of every 5 units there is a 'progress test' which revises grammar and vocabulary taught in the previous units. This book is designed to be read from start to finish however students may prefer to concentrate on a particular area of grammar or vocabulary. Please see the Aims page for detailed information about the contents of each unit.

The English language
The flags on the Contents page display countries where English is the main, or one of the main, language(s). The flags, from left to right, represent the following countries: The United Kingdom (original country of the English language), USA (largest English-speaking country), Canada (first language with French), South Africa (one of more than 10 official languages), Jamaica, Australia (largest English-speaking country in the southern hemisphere), Ireland, New Zealand.

As a native language English is spoken by over 350 million people but if all English speakers are included in this figure it totals well over a billion people across the globe. English is the language of business, one of the most common languages for music and films and one of the official languages of the United Nations and the European Union.

title: English for B2 Students/Introduction


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English for B2 Students/Unit 3

=I've visited Venice=

Aim of this lesson: Talk about hobbies and your family and practice using the present perfect to talk about experiences.

Chloe and Michael are students at university in Manchester. They're talking about places that they went to in the past.

Dialogue
([[Media:B2 unit3.ogg|listen]])

Grammar Focus - Present Perfect
Do we know when'' I visited Venice? Do we know how many times I visited Venice?''
 * I have visited Venice.

The Present Perfect is a very commonly used form in English. It is very important to learn how this form is used.

Vocabulary - Family
The picture of Frodo Baggins' family tree (from the Lord of the Rings films) shows his family.

Speaking Practice
''Try to practice asking and answering these questions in pairs. If you're working on your own then write your answers.''

 * How big is your family? Do you have many brothers, sisters, aunts, or uncles?
 * Have you ever been on vacation with your family? Did you like it?
 * Have you ever been to see a film at the cinema more than once? When?  What film was it?
 * Whom are you closest to in your family? How often do you see that person?

Exercises
Try these questions to see if you can use the present perfect correctly. The answers can be found here.

Complete the following sentences using the present perfect or past simple:

 * 1) Have you ever eaten / Did you ever eat curry?
 * 2) Last year I went / have been to Poland.
 * No, I have never seen / never have seen The Lord of the Rings.
 * 1) My brother says that he haven't eaten / hasn't eaten curry.
 * 2) Did you go on vacation last summer? Yes, I have been / went to Croatia.

Grammar Reference
For further information about present perfect and past simple see ../Tenses and Forms/.

This is the test page - the answers are not to be inserted here, but to be found here

title: English for B2 Students/Unit 3


text: Template:Code:Interpreted

title: Template:Code:Interpreted


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General Chemistry/Intermolecular bonds

Dipoles
Covalent bonds can be polar or non-polar, and so can the overall compound depending on its shape. When a bond is polar, it creates a dipole, a pair of charges (one positive and one negative). If they are arranged in a symmetrical shape, so that they point in opposite directions, they will cancel each other. For example, since the four hydrogens in methane (CH4) are facing away from each other, there is no overall dipole and the molecule is non-polar. In ammonia (NH3), however, there is a negative dipole at the nitrogen, due to the asymmetry caused by the non-bonding electron pair. The polarity of a compound determines its intermolecular bonding abilities.

Polar and Non-Polar Shapes
When a molecule has a linear, trigonal planar, tetrahedral, trigonal bipyramidal, or octahedral shape, it will be non-polar. These are the shapes that do not have non-bonding lone pairs. (e.g. Methane, CH4) But if some bonds are polar while others are not, there will be an overall dipole, and the molecule will be polar (e.g. Chloroform, CHCl3).

The other shapes (with non-bonding pairs) will be polar. (e.g. Water, H2O) Unless, of course, all the covalent bonds are non-polar, in which case there would be no dipoles to begin with.

Dipole-Dipole Bonds
When two polar molecules are near each other, they will arrange themselves so that the negative and positive sides line up. There will be an attractive force holding the two molecules together, but it is not nearly as strong a force as the intramolecular bonds. This is how many types of molecules bond together to form large solids or liquids.

Hydrogen Bonding
Certain chemicals with hydrogen in their chemical formula have a special type of intermolecular bond, called hydrogen bonds. Hydrogen bonds will occur when a hydrogen atom is attached to an oxygen, nitrogen, or fluorine atom. This is because there is a large electronegativity difference between hydrogen and fluorine, oxygen, and nitrogen. Thus, molecules such as $$HF$$, $$H_2O$$, $$NH_3$$ are extremely polar molecules with very strong dipole-dipole forces. As a result of the high electronegativities of fluorine, oxygen, and nitrogen, these elements will pull the electrons almost completely away from the hydrogen. The hydrogen becomes a bare proton sticking out from the molecule, and it will be strongly attracted to the negative side of any other polar molecules. Hydrogen bonding is an extreme type of dipole-dipole bonding. These forces are weaker than intramolecular bonds, but are much stronger than other intermolecular forces, causing these compounds to have high boiling points.

Covalent Networks
Silicon dioxide forms a covalent network. Unlike carbon dioxide (with double bonds), silicon dioxide forms only single covalent bonds. As a result, the individual molecules covalently bond into a large network. These bonds are very strong (being covalent) and there is no distinction between individual molecules and the overall network. Covalent networks hold diamonds together. Diamonds are made of nothing but carbon, and so is soot. Unlike soot, diamonds have covalent networks, making them very hard and crystalline.

Van der Waals forces
Van der Waals, or London dispersion forces are caused by temporary dipoles created when electron locations are lopsided. The electrons are constantly orbiting the nucleus, and by chance they could end up close together. The uneven concentration of electrons could make one side of the atom more negatively-charged than the other, creating a temporary dipole. As there are more electrons in an atom, and the shells are farther away from the nucleus, these forces become stronger.

Van der Waals forces explain how nitrogen can be liquified. Nitrogen gas is diatomic; its equation is N2. Since both atoms have the same electronegativity, there is no dipole and the molecule is non-polar. If there are no dipoles, what would make the nitrogen atoms stick together to form a liquid? Van der Waals forces are the answer. They allow otherwise non-polar molecules to have attractive forces. These are by far the weakest forces that hold molecules together.

Melting and Boiling Points
When comparing two substances, their melting and boiling points may be questioned. To determine which substance has the higher melting or boiling point, you must decide which one has the strongest intermolecular force. Metallic bonds, ionic bonds, and covalent networks are very strong, as they are actually intramolecular forces. These substances have the highest melting and boiling points because they only separate into individual molecules when the powerful bonds have been broken. Breaking these intramolecular forces requires great amounts of heat energy.

Substances with hydrogen bonding, an intermolecular force, will have much higher melting and boiling points than those that have ordinary dipole-dipole intramolecular forces. Non-polar molecules have the lowest melting and boiling points, because they are held together by the weak van der Waals forces.

If you need to compare the boiling points of two metals, the metal with the larger atomic radius will have weaker bonding, due to the lower concentration of charge. When comparing boiling points of the non-polar gases, like the noble gases, the gas with the largest radius will have the highest points because it has the most potential for van der Waals forces.

Ionic compounds can be compared using Coulomb's Law. Look for substances with high ionic charges and low ionic radii.

title: General Chemistry/Intermolecular bonds


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User talk:218.185.72.226

Xania talk 23:27, 14 May 2007 (UTC)

title: User talk:218.185.72.226


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User talk:24.236.229.159

Xania talk 23:28, 14 May 2007 (UTC)

title: User talk:24.236.229.159


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IB Biology/Option C - Cells and Energy

For standard level, the same as topics 6.5 (Proteins), 6.6 (Enzymes), 7.1 (Cell Respiration) and 7.2 (Photosynthesis) in your syllabus.

title: IB Biology/Option C - Cells and Energy


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User:Kassie

hi

title: User:Kassie


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Korean/RWP/Summary

This lesson summary is incomplete. Per the lesson plan, it should have a "table showing all letters, summary of all rules, extra words for practice".

title: Korean/RWP/Summary


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User:Whiteknight/Common Mistakes

This page is going to list some common mistakes that new users might make, and for which the solution isn't obvious.

Creating new books/pages

 * I created a new page, but somebody said i made it incorrectly! How do I make a page part of a book? How do I make a page in a book?:Pages on wikibooks are named using what's called the "forward slash convention". To put a page into this convention, we need to write "Book/Page", instead of simply "page". When we make a link, we have to write " Book/Page ", and not " Page " For instance, if we want to make a page for "Lions" in the "Animals" book, we can create a link to " Animals/Lions ".

 * I created a page by accident, Or I created a page with the wrong name, or I created a page but I dont need it anymore, what do I do?:This happens all the time, and there are a group of staff members here called "administrators" who can delete unneeded pages. If you want us to come delete a page you created, write " " at the top of the page.

 * I want to start writing a new book, how do I do it, and where can I go for help?:There is a good guide for writing new books: Whiteknight's New Book Guide. If you want to start writing a new book, do a search first to make sure that there aren't already any books on your topic. If there already is a book, you should try contributing to the old book before starting a new book. When you start a new book, or if you just want to bounce the idea off some people, make an announcement about it at the New Books Staff Lounge.

 * I created a new book or a new page, and there is some kind of message or a banner at the top! What do I do?:We've developed several pre-made messages, called "templates" here at Wikibooks that can help us to communicate common ideas to new editors. Any message that we post on your book is designed to help you, not to frighten you or punish you. Some of these messages will ask you a question, like "what is this page about?", and some will give you a suggestion, such as "you should fix the formatting". If you can't follow the suggestion, or if you don't understand, you can ask about it at the Books and Projects Staff Lounge.

 * Can I write any kind of book I want?:The short answer is no. Wikibooks is for textbooks and instructional guides only, not for all types of books. For more information about what kinds of things we allow and what we don't allow, take a look at our complete inclusion criteria.

 * I have previously written a book, and I would like to donate it to Wikibooks. Is this possible?:It is possible to donate books to wikibooks, but it isn't always recommended. Books that are donated must satisfy our inclusion criteria. Also, all work on Wikibooks must be released under the GFDL license, and anybody must be allowed to edit it. If you don't want other people to edit your work, or if you don't want to lose editorial control, then don't donate your book.

Formatting

 * When I write text, a gray box with a dotted-blue line appears around it, and the text flows off the edge of the screen!:This is an effect known as "preformatting", and occurs whenever you start a line of text with a space or a tab. Unlike normal text editing, you should not indent new paragraphs. Luckily, if you do have this problem, the solution is as easy as editing the page, deleting the spaces at the front of a paragraph, and clicking "save page".

 * I copied and pasted some text from a word-processor, and when I look at it on the wiki, it's wrong!:Wikibooks uses a special markup language that is similar to HTML, but is not the same. You cannot copy and paste text directly from a word processor such as Microsoft Word, because word processors like this do not use the Wiki language. If you would like to write some content in an external editor, consider a plain-text editor, such as notepad.

Content and Collaboration

 * HELP! I had put some content on this page, and when I came back later, it was different or even missing entirely! What happened? How did my stuff get changed?:First and foremost, wikibooks is a collaborative community, and there is nothing here that is your "stuff". Because this is a community effort, other people that you don't know, people from around the world, will see your page and make edits if they want. You can't stop it, and you shouldn't want to either, because more hands make lighter work. If people want to help fix your mistakes, or add more content to your page, you should be happy to have the help.

 * Another person is editing this page that I'm working on, and he keeps removing or changing what I write!:When two people go back and forth, changing each other's edit, it's called an edit war. Users should never edit war, and if another user makes an edit that you don't agree with, you should talk to them. You can post your concerns on the talk page of the page you are editing, or even directly onto that person's user talk page. Always try to discuss your disputes and reach a compromise. If you need help or intervention (as an absolute last resort) you can ask at the Staff lounge.

 * Something is wrong, my page contains nonsense, or bad words, or bad images. What happened, and how do I fix it?:Unfortunately, anybody can edit wikibooks, and that means that people who are immature can edit those pages as well. When people  delete the content on a page without a reason ("page blanking"), or adds obscenity to the page, we call that "vandalism". If you have a problem with vandalism, post a message about it at Administrators Noticeboard. Make sure to include the name of the page that was vandalized.

 * Where is the best place to talk about a page?:Every page has an associated "talk page". At the top of your screen you should see a number of tabs, and the second one from the right will say something like "talk" or "discussion". If you click on this tab, you will be taken to the associated talk page, where you can leave messages, ask questions, and so on. If you want to talk about an entire book, a collection of books, or some other topic entirely, you can post your message at the Staff Lounge instead.

Bookshelves

 * How do I add a new book to a bookshelf? I can't figure it out!:Bookshelves are pages just like any other book or discussion page, and you can edit them to add links to your books by clicking the "Edit this page" link at the top of the page, or any of the "[edit]" links on the side of the page. However, bookshelves are protected against being edited anonymously, and they are also protected against being edited by very new users (fewer then 20 edits) this helps to protect against vandalism. If you have fewer then 20 edits, and want to put your new book on a bookshelf, you can ask at the Staff lounge and one of our staff members will be happy to assist you.

 * Which bookshelf should I add my new book to? Can I list my book on multiple bookshelves?:The entire list of bookshelves is available at All bookshelves. Pick the shelf or shelves that seem most appropriate to you, and add your books there. If you don't know where to add your book, ask at the Staff lounge. You can add you book to multiple shelves, but make sure that they are appropriate: even a link to a book can be considered spam if you post it in inappropriate places.

New Accounts

 * Where is the best place to say "hello"?:If you want to say hello, meet some new people, and ask a few questions, the best place is the New Users Staff Lounge, a discussion room devoted to new users and new projects.

 * How do I change my signature to be something fancy? For that matter, how do I sign a message that I write?:To sign a message that you write, you can use four tildes: ~ . When you save the page, Wikibooks will replace that symbol with your signature. To change your signature, open up your Preferences page. On the "User profile" tab, check the box for "Raw Signatures", and write the code for your new signature in the box above it. You should always sign your messages on a talk or discussion page. You should never sign your name on a book page or other content page.

General

 * I don't like this, or that. I think this needs to change. What are you guys, lazy or stupid or something?:If you see some content that needs to be changed, you are generally welcome to change it. If there are disagreements, you should talk them over on the relevant talk or discussion pages first. Some important things can be changed by special users called "Administrators", if you ask nicely at the Administrator's Noticeboard. Some other things are just part of the software, and if you want to change those you will need to get community support at the Technical Chat Staff Lounge. Finally, there are a few things that cannot be changed, and that will not be changed. There is no sense complaining about these things, because nobody wants to hear it. Also, calling people "stupid" is not nice.

 * I've got this great advertisement for a company or a website and...:Please keep advertisements to yourself. Wikibooks is a place to write books, not to share weblinks or plug new products. Even the best-natured advertisements are considered to be "spam" and are deleted. If you post too many advertisements, you could be blocked from editing Wikibooks entirely.

title: User:Whiteknight/Common Mistakes


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Template talk:Code/doc

I've added a Code:Interpreted template. Just a thought, but some of these templates could be combined into one, or distinguished using parameters. --Yan 11:51, 16 May 2007 (UTC)

title: Template talk:Code/doc


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Talk:Genetic Information/Introduction to Genetic Information

Why Advanced Genome Analysis
While I have made this introduction a short elementary reading, I want to appeal to my fellow wikipedians to consider the needs of graduate students, post-doctoral readers and practicing molecular biologists alike.

For this reason, I further appeal that we condense in this book the most widely used concepts in genetic information, detailing their derivations and applications, mixing highly theoretical aspects of the field and solving major mathematical aspects encountered in molecular genetics of our time. Not just to make the reader be able to locate critical formulas, but let us also make the reader here to be in a position to review mathematical details in the field.

If we set one of our goals to help those students preparing for comprehensive examinations so that they can develop and retain broad base of knowledge and in-depth understanding of the fundamental principles in genome analysis, we should not omit key steps in crucial derivations in genomics.

Let us make this one source of genome analysis a highly accessible format with information for professionals in their careers. Odhiambo 02:31, 15 May 2007 (UTC)

 * Since this book (i) does not seem to be under active development, and (ii) is currently listed with a somewhat niche and confusing name, I suggest that it be merged with the existing Bioinformatics book. (That book also needs some love, but the title is at least appropriate.) --Etal (talk) 16:36, 8 December 2010 (UTC)


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General Chemistry/Naming Substances

Some compounds have common names, like water for H2O. However, there are thousands of other compounds that are uncommon or have multiple names. Also, the common name is usually not recognized internationally. What looks like water to you might look like agua or vatten to someone else. To allow chemists to communicate without confusion, there are naming conventions to determine the systematic name of a chemical.

Naming Ions and Ionic Compounds
Ions are atoms that have lost or gained electrons. Note that in a polyatomic ion, the ion itself is held together by covalent bonds. Monoatomic cations (positive) are named the same way as their element, and they come first when naming a compound. Monoatomic anions (negative) have the suffix -ide and come at the end of the compound's name.

Notice that there is no need to write how many ions there are. Between the periodic table and our knowledge of ionic bonding, we can determine the number of ions, based on which elements are used.

Polyatomic Ions
Polyatomic ions have special names. Many of them contain oxygen and are called oxyanions. When different oxyanions are made of the same element, but have a different number of oxygen atoms, then prefixes and suffixes are used to tell them apart. The chlorine family of ions is an excellent example.

The -ate suffix is used on the most common oxyanion (like sulfate SO42- or nitrate NO3-). The -ite suffix is used on the oxyanion with one oxygen atom fewer (like sulfite SO32- or nitrite NO2-). Sometimes there can be a hypo- prefix, meaning one oxygen atom fewer than for -ite. There is also a per- prefix, meaning one more oxygen atom than an -ate molecule has.

One last prefix you may find is thio-. It means an oxygen has been replaced with a sulfur within the oxyanion. Cyanate is OCN-, and thiocyanate is SCN-.

In the last example, copper had a roman numeral 2 after its name because most of the transition metals can have more than one charge. The charge on the ion must be known, so it is written out for ions that have more than one common charge. Silver always has a charge of 1+, so it isn't necessary (but not wrong) to name its charge. Zinc always has a charge of 2+, so you don't have to name its charge either. Aluminum will always have a charge of +3. All other metals (except the Group 1 and 2 elements) must have roman numerals to show their charge.

Common polyatomic ions that you should know are listed in the following table

Further explanation of the roman numerals is in order. Many atoms (especially the transition metals) are capable of ionizing in more than one way. The name of an ionic compound must make it very clear what the exact chemical formula is. If you wrote "copper chloride", it could be CuCl or CuCl2 because copper can lose one or two electrons when it forms an ion. The charge must be balanced, so there would be one or two chloride ions to accept the electrons. To be correct, you must write "copper(II) chloride" if you want CuCl2 and "copper (I) chloride" if you want CuCl. Keep in mind that the roman numerals refer to the charge of the cation, not how many anions are attached.

Common metal ions are listed below and should be learned:

Naming molecules in chemistry
There are two systems of naming molecular compounds. The first uses prefixes to indicate the number of atoms of an element that are in the compound. If the substance is binary (containing only two elements), the suffix -ide is added to the second element. Thus water is dihydrogen monoxide. A prefix is not necessary for the first element if there is only one, so SF6 is 'sulfur hexafluoride'. The prefix system is used when both elements are non-metallic.

The second system, the stock system, uses oxidation numbers to represent how the electrons are distributed through the compound. This is essentially the roman numeral system that has already been explained, but it applies to non-ionic compounds as well. The most electronegative component of the molecule has a negative oxidation number that depends on the number of pairs of electrons it shares. The less electronegative part is assigned a positive number. In the stock system, only the cation's number is written, and in Roman numerals. The stock system is used when there is a metallic element in the compound. In the case of V2O5, it could also be called vanadium(V) oxide. Knowing that oxygen's charge is always -2, we could determine that there were five oxygens and two vanadiums, if we were given the name without the formula.

Naming acids
If an acid is a binary compound, it is named as hydro[element]ic acid. If it contains a polyatomic ion, then it is named [ion name]ic acid if the ion ends in -ate. If the ion ends in -ite then the acid will end in -ous. These examples should help.

title: General Chemistry/Naming Substances