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Oka, Quebec

Oka is a small village on the northern bank of the Ottawa River (Rivière des Outaouais in French), northwest of Montreal, Quebec, Canada. Located in the Laurentians valley on Lake of Two Mountains, where the Ottawa has its confluence with the St. Lawrence River, the town is connected via Quebec Route 344. It is located 50 km (30 miles) west of Montreal.

The area was first established in 1721 by Roman Catholic Sulpician Fathers as a mission to serve the needs of Mohawk, Algonquin and Nipissing converts as well as of French settlers. In 1730, the mission site was moved about 1.5 km west along the shore to Pointe d'Oka (Oka's Point) close to where the first stone church was built in 1733, and around which church evolved the village that eventually became known as Oka. The Mohawks had been assigned to a west-side village that eventually became known as Kanesatake whereas the Algonquins and Nipissings had been assigned to an adjacent east-side village. Kanesatake's status is as interim land base within the meaning of the Constitution Act, 1867, and not as indian reserve within the meaning of the Indian Act.

Pre-crisis habitation
Oka is historically connected to the Sulpicians' two first Amerindian missions on Montreal island, the initial mission of La Montagne established in 1676 that was moved to Sault-au-Récollet mission established in 1696. This culminated in the King of France in 1718 granting the Sulpicians a concession named seigneurie du Lac-des-Deux-Montagnes with the view to relocating the Sault-au-Récollet Amerindian converts to new mission facilities located east of the Ottawa River at the confluence with Lac-des-Deux-Montagnes.

The move from the Sault-au-Récollet mission to the Lac-des-Deux-Montagnes mission started in 1721 and was completed the next year. The Sulpicians' seigneurie du Lac-des-Deux-Montagnes was conditional on the Indians not abandoning the lands, failing which they would revert to the Crown, and was subject to the Sulpicians being indemnified by being given proprietorship of the land because of the need to construct a fort. Final concession was given in 1718. In 1721, under the direction of the Sulpician Maurice Quéré de Tréguron, Algonquin and Iroquois groups relocated on the north shore of Lac-des-Deux-Montagnes and on the east shore of the Ottawa River. The chosen site was advantageous for the conversion of the Indians and for their defense and that of the colony. To prepare their installation, the Sulpician M. Robert-Michel Gay, who was the mission Lac des Deux-Montagnes' first superior, made some clearing and built various buildings including a chapel, the missionary's house, a school intended for the Sisters of the Congregation Notre-Dame as well as cabins for the Indians. The Indian population increased considerably around 1727 when the Nipissings and the Algonquins of the Île-aux-Tourtes mission also came to the Lac-des-Deux-Montagnes mission site with their missionary Pierre-Élie Déperet.

Between 1740 and 1742, seven oratories were established which are witness to the famous Calvary of Oka, work of the Sulpicians Hamon Guen and François Picquet. Francois dit Belleville (who arrived in Nouvelle-France in 1754) was the author of the bas-relief of the chapels of this pilgrimage site, which is the oldest in America. The missionary Louis-Urgel Lafontaine (1895–1930) was the last to preach in Iroquois [language] at the location. The Calvary was built by the Indians and only for them in order to evangelize them.

The church, the presbytery and the outbuildings of Oka were set on fire on 15 June 1877.

The present church was built between 1878 and 1883 according to the plans of the architects Perrault and Mesnard.

Oka crisis
In 1990, the small community gained international attention in what became known as the Oka Crisis. It had approved development of a private golf course, to add nine holes and nearby luxury housing. The Mohawk of Kanesatake opposed this.

Several years before the crisis the Mohawks had initiated and lost a land claim court case in which they tried to gain control of what they termed "historic land", land known from the early mission settlement as commons land, which including pasture land, "The Pines" forested land and a nearby aboriginal cemetery. The most recent claim had been in part rejected by the Federal courts. The Mohawks of Kanesatake initial response to the golf course development decision was to blockade a dirt road leading to "The Pines" land.

The conflict escalated from there with the Sûreté du Québec enforcing on July 11 a Provincial injunction to dismantle this first barricade, which resulted in shots being fired on both sides, one SQ officer being killed, the SQ abandoning several of their vehicles and the Mohawk Warriors erecting a new barricade this time across Route 344, the main road access to Oka and Kanesatake.

By the end of July, there were at least 1,000 people assembled in Oka's Paul-Sauvé Provincial Park in support for the Mohawks. Among them were delegations represented by Cree, Innus, Hurons-Wendats, Algonquins, Ojibwes, Micmacs and other Canadian First Nations people.

During the confrontation lasting 78 days, the Quebec Police Force (Surete du Quebec) (and later Canadian Army units) confronted members of the Mohawk nation of the adjacent community of Kanesatake. The latter were joined by other Mohawk and First Nations and Native American tribes. In addition, for several weeks, Mohawk at Kahnawake blockaded the approach to the Mercier Bridge, a route that ran through their land. Negotiations finally led to the Mohawk re-opening the road. An SQ officer Corporal Lemay was shot and killed at a period of heightened tensions. Numerous people were arrested. The disputed area was acquired by the Canadian Federal Government as had been planned before the crisis. Development was stopped.

Some in the media reported the Mohawks' opposition being based on the expansion of the golf course over what the Mohawks considered sacred land. This however was never the premise of contention. Instead, the dispute laid over persistent claims dating back to land right issues between the Sulpicians and Mohawks that related not to burial grounds or cemeteries, but to i) what was known as the commons lands including part of "The Pines", and, ii) that commenced at the onset of the British conquest, iii) the Mohawks allegiance to them, and, iv) the definite change of attitude of the Mohawks towards the French.

Geography
Oka is located 50 kilometers west of Montreal, at the confluence of the Ottawa River and the lake of Two Mountains. Its territory covers a total area of 85.90 km2 of which 56.99 km2 is land and 28.91 km2 is water, mainly corresponding to lake of Two Mountains.

The boundaries of the municipality are very complex, because it encloses the Mohawk community of Kanesatake whose reserve is made up of several scattered lands, sometimes isolated from each other, within Oka itself.

The municipality is completely crossed by route 344. From east to west, the road bears the names Chemin d'Oka, Rue Notre-Dame, Rue Saint-Michel and Rang Sainte-Philomène. The Rang de l’Annonciation, which crosses Oka from north to south, leads to the Oka-Hudson crossing. This allows to cross the lake of Two Mountains to reach the town of Hudson and the Vaudreuil-Soulanges peninsula.

The relief is marked by the Oka hills, which are part of the Monteregian Hills. Rather flat to the south, the center and north of Oka are characterized by hilly terrain. Its minimum altitude is 20 meters (at the lake of Two Mountains) while its maximum altitude reaches 238 meters (at the mount Bleu). The subsoil contains a niobium deposit. The land, for its part, is mainly occupied by large woodlands as well as agriculture.

The Petite Rivière crosses the northeastern tip of the municipality towards the west. The territory is watered by the Serpents River and several other streams (Varin, Girard, Rousse). The Grande Baie marsh constitutes a wetland with ecological value. Around fifteen species of migratory birds in decline stay there.

Climate
Oka has a humid continental climate (Dfb) with warm, rainy summers and long, cold winters.

Demographics
Population trend:
 * Population in 2021: 3,968 (2016 to 2021 population change: 3.8%)
 * Population in 2016: 3,824
 * Population in 2011: 3,969
 * Population in 2006: 3,300
 * Population in 2001: 3,194
 * Population in 1996: 1,514
 * Population in 1991: 1,658
 * Population in 1986: 1,532
 * Population in 1981: 1,541
 * Population in 1976: 1,483
 * Population in 1971: 1,446
 * Population in 1966: 1,369
 * Population in 1961: 1,375
 * Population in 1956: 1,243
 * Population in 1951: 1,110
 * Population in 1941: 825
 * Population in 1931: 664
 * Population in 1921: 1,617
 * Population in 1911: 1,601
 * Population in 1901: 1,571
 * Population in 1891: 1,466
 * Population in 1881: 1,342

Private dwellings occupied by usual residents: 1,616 (total dwellings: 1,683)

Mother tongue:
 * French as first language: 90.0%
 * English as first language: 5.2%
 * English and French as first language: 1.7%
 * Other as first language: 2.3%

Attractions
In the summertime, the community's long stretch of beachfront along the Ottawa River and the Lake of Two Mountains, and its marina draw people to the area from Montreal and neighboring cities. Another attraction is the Abbey of Notre-Dame du Lac, famous for its Oka cheese produced by Trappists monks there.

A small ferry service operates between Oka and the town of Hudson across the Ottawa River. During the winter months, a toll ice bridge provides access on this route. There is also connection with the AMT Deux-Montagnes Train to and from Montreal's Central Station and Deux-Montagnes Station by the Oka Express minibus.

Infrastructure
Highway 344 passes through Oka. The Oka Express goes to the Deux-Montagnes station and Terminus Saint-Eustache

Education
The Commission scolaire de la Seigneurie-des-Mille-Îles (CSSMI) operates French language public schools.

Schools include: Some portions are instead zoned to École de l'Amitié in Saint-Placide.
 * École secondaire d'Oka
 * École des Pins

The Sir Wilfrid Laurier School Board (SWLSB) operates Anglophone public schools. Mountainview Elementary School and Saint Jude Elementary School, both in Deux-Montagnes, serve this community. Lake of Two Mountains High School in Deux-Montagnes is the zoned high school.

title: Oka, Quebec


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Neuros Technology

Neuros Technology was a Chicago, Illinois–based company that produced a number of audio and video devices under the brand name Neuros. Founded by Joe Born in 2001 as a division of Digital Innovations, it previously operated under the name Neuros Audio. Like Digital Innovations, Neuros distinguished itself by its use of open-innovation and crowdsourcing techniques to bring products to market, as well as by its prominent use of open-source software and open-source hardware. In its development model, end users were involved throughout the product development process from reviewing initial concepts to beta testing initial product releases.

Neuros LINK
The Neuros LINK is an open set-top device designed to bring Internet television and other video to the television, it comes pre-installed with XBMC Media Center. The Neuros LINK supports the web's open standards allows Internet television sites to be viewed directly on a television set. The Neuros LINK is positioned between three categories, a HTPC (Home Theater PC), digital media receiver (a.k.a. media extender), and the standard Internet-to-television devices or set-top-boxes. It is open and allows access to all content sources using software and hardware components from a PC, but positioned as a lower cost, smaller consumer electronics device designed to be connected to a television set.

Neuros OSD
The Neuros OSD is a device to archive, organize, and play video content. It can record from any source with composite output including DVD players, VCRs and others. The video input is converted to MP4 format and then output to a user-installed device, either a memory card, a USB flash drive or USB hard drive, or to a network-connected PC. The resulting MP4 files can then be played back by the OSD on a TV, or by other devices with MP4 playback capability such as Sony's PlayStation Portable, Apple's iPod, Neuros 442 and other portable devices.

OSD stands for "open-source device", because the device runs Linux and a variety of open-source software, as well as to reflect the vision that the device will become a general purpose device for linking computer and electronic devices. The successor to the Neuros MPEG 4 Recorder, the Neuros OSD was released to initial production on September 20, 2006.

The development platform uses a Texas Instruments DM320 system on chip, comprising an ARM926EJ core and a TI C54x DSP core. This design will support the DM320's High Performance mode of operation. The maximum ARM clock is then specified as 203 MHz. The DSP is rated in excess of 120 MHz. In addition, the SoC also contains dedicated hardware acceleration for video encoding and decoding, making it capable of achieving DVD-like quality playback as well as high quality, D1 resolution video encoding using MPEG-4.

Neuros digital audio computer
The Neuros Digital Audio Computer (Neuros DAC) is a portable audio unit designed for playback and recording of audio in MP3, Ogg Vorbis, WAV, and DRM-free WMA formats.

Neuros DACs feature a two-piece design, separating the unit into a player and a "backpack". The player unit is upgradeable by firmware upgrade from the website. The intended purpose of these design decisions are that you can upgrade your player/backpack without paying for an entirely new model. There are currently two types of backpacks. The smaller backpack contains just a pair of NiMH batteries, so the player depends on the internal flash memory to store music. The larger backpack contains a LiIon battery and a 2.5-inch hard drive. The main difference between Neuros1 and Neuros2 players is the backpack on Neuros2 has a USB 2.0 connector allowing it to be used, independently of the player unit, as a stand-alone USB hard drive. The Neuros DAC is not currently in production, but a third version of the product has been rumored.

Neuros MPEG 4 recorder
The Neuros MPEG 4 Recorder is a flash-based digital recorder that works like a miniature VCR (sans TV tuner card), allowing users to record live TV from an analog video sources (for example a DVD player or camcorder), have it encoded in real-time and stored onto a flash memory card. It is capable of recording and playing back MPEG-4 and has several unique consumer benefits like ignoring Macrovision's automatic gain control copy protection.

The Recorder was first released to the public on February 9, 2005 in woot.com's first product launch. That launch was part of Neuros Technology's gamma test program and consisted of 850 units sold at $119.99 (US) each.

The Neuros MPEG4 Recorder was revised as version 2 with an enhanced processor and higher resolution recording (480p) for use with devices such as the iPod, PlayStation Portable, smartphones and other handhelds.

Open innovation
Neuros uses many of the tools of the open-source world to not only collaborate on building the software but to get user feedback on features and implementation as well. Products are typically launched explicitly before the software is complete under Beta and Gamma programs. As community members develop new features, often in response to posted bounties, Neuros promotes that work to other users and gauges reaction. This crowdsourced process has been viewed by some as a hybrid between traditional market research and open-source collaboration.

Open-source software and hardware
A distinguishing feature of the Neuros devices is that they use open-source software and use open-source methods in the development of their devices; open-source software is regularly used as well as the release of documentation surrounding the hardware. This also allows the devices to be adapted for other purposes than the advertised uses, such as including home and auto-security recording.

In addition the company advocates consumers digital rights by making products that assert those rights, allowing unfettered recording from a variety of sources. In December 2007, Neuros developed an "Unlocked Media" name and logo for DRM-free media to allow companies to brand their products in such a way that consumers know they're getting a fully portable file.

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

title: Neuros Technology


text:
John Arbuthnott, 10th Viscount of Arbuthnott

John Arbuthnott, 10th Viscount of Arbuthnott DL (b. Kincardineshire 20 July 1843 – d. Arbuthnott House 30 November 1895) was the son of John Arbuthnott, 9th Viscount of Arbuthnott whom he succeeded in 1891. Lt. 49th Foot Regiment. He was Deputy Lieutenant for Kincardineshire.

Married Anna Harriet Allen (born London 1852/3, died at Arbuthnott House 23 April 1892) at the home of her uncle, Inchmartine House, Inchture (Errol), 20 April 1871). Anna Harriet Allen was the only daughter of Edmund Allen of Strathmartin.

John Arbuthnott, 10th Viscount of Arbuthnott was succeeded by his brother David Arbuthnott, 11th Viscount of Arbuthnott.


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Coefficient of determination

In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.

There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. When only an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the observed outcomes and the observed predictor values. If additional regressors are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination normally ranges from 0 to 1.

There are cases where R2 can yield negative values. This can arise when the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data. Even if a model-fitting procedure has been used, R2 may still be negative, for example when linear regression is conducted without including an intercept, or when a non-linear function is used to fit the data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.

The coefficient of determination can be more (intuitively) informative than MAE, MAPE, MSE, and RMSE in regression analysis evaluation, as the former can be expressed as a percentage, whereas the latter measures have arbitrary ranges. It also proved more robust for poor fits compared to SMAPE on the test datasets in the article.

When evaluating the goodness-of-fit of simulated (Ypred) vs. measured (Yobs) values, it is not appropriate to base this on the R2 of the linear regression (i.e., Yobs= m·Ypred + b). The R2 quantifies the degree of any linear correlation between Yobs and Ypred, while for the goodness-of-fit evaluation only one specific linear correlation should be taken into consideration: Yobs = 1·Ypred + 0 (i.e., the 1:1 line).

Definitions

A data set has n values marked y1, ..., yn (collectively known as yi or as a vector y = [y1, ..., yn]T), each associated with a fitted (or modeled, or predicted) value f1, ..., fn (known as fi, or sometimes ŷi, as a vector f).

Define the residuals as ei = yi − fi (forming a vector e).

If $$\bar{y}$$ is the mean of the observed data: $$\bar{y}=\frac{1}{n}\sum_{i=1}^n y_i $$ then the variability of the data set can be measured with two sums of squares formulas:
 * The sum of squares of residuals, also called the residual sum of squares: $$SS_\text{res}=\sum_i (y_i - f_i)^2=\sum_i e_i^2\,$$
 * The total sum of squares (proportional to the variance of the data): $$SS_\text{tot}=\sum_i (y_i - \bar{y})^2$$

The most general definition of the coefficient of determination is $$R^2 = 1 - {SS_{\rm res}\over SS_{\rm tot}} $$

In the best case, the modeled values exactly match the observed values, which results in $$SS_\text{res}=0$$ and R2 = 1. A baseline model, which always predicts $\overline{y}$, will have R2 = 0.

Relation to unexplained variance
In a general form, R2 can be seen to be related to the fraction of variance unexplained (FVU), since the second term compares the unexplained variance (variance of the model's errors) with the total variance (of the data): $$R^2 = 1 - \text{FVU}$$

As explained variance
A larger value of R2 implies a more successful regression model. Suppose R2 = 0.49. This implies that 49% of the variability of the dependent variable in the data set has been accounted for, and the remaining 51% of the variability is still unaccounted for. For regression models, the regression sum of squares, also called the explained sum of squares, is defined as
 * $$SS_\text{reg}=\sum_i (f_i -\bar{y})^2$$

In some cases, as in simple linear regression, the total sum of squares equals the sum of the two other sums of squares defined above:
 * $$SS_\text{res}+SS_\text{reg}=SS_\text{tot}$$

See Partitioning in the general OLS model for a derivation of this result for one case where the relation holds. When this relation does hold, the above definition of R2 is equivalent to
 * $$R^2 = \frac{SS_\text{reg}}{SS_\text{tot}} = \frac{SS_\text{reg}/n}{SS_\text{tot}/n}$$

where n is the number of observations (cases) on the variables.

In this form R2 is expressed as the ratio of the explained variance (variance of the model's predictions, which is SSreg / n) to the total variance (sample variance of the dependent variable, which is SStot / n).

This partition of the sum of squares holds for instance when the model values ƒi have been obtained by linear regression. A milder sufficient condition reads as follows: The model has the form
 * $$f_i=\widehat\alpha+\widehat\beta q_i$$

where the qi are arbitrary values that may or may not depend on i or on other free parameters (the common choice qi = xi is just one special case), and the coefficient estimates $$\widehat\alpha$$ and $$\widehat\beta$$ are obtained by minimizing the residual sum of squares.

This set of conditions is an important one and it has a number of implications for the properties of the fitted residuals and the modelled values. In particular, under these conditions:
 * $$\bar{f}=\bar{y}.\,$$

As squared correlation coefficient
In linear least squares multiple regression with an estimated intercept term, R2 equals the square of the Pearson correlation coefficient between the observed $$y$$ and modeled (predicted) $$f$$ data values of the dependent variable.

In a linear least squares regression with a single explanator but without an intercept term, this is also equal to the squared Pearson correlation coefficient of the dependent variable $$y$$ and explanatory variable $$x.$$

It should not be confused with the correlation coefficient between two explanatory variables, defined as
 * $$\rho_{\widehat\alpha,\widehat\beta} = {\operatorname{cov}\left(\widehat\alpha,\widehat\beta\right) \over \sigma_{\widehat\alpha} \sigma_{\widehat\beta}},$$

where the covariance between two coefficient estimates, as well as their standard deviations, are obtained from the covariance matrix of the coefficient estimates, $$(X^T X)^{-1}$$.

Under more general modeling conditions, where the predicted values might be generated from a model different from linear least squares regression, an R2 value can be calculated as the square of the correlation coefficient between the original $$y$$ and modeled $$f$$ data values. In this case, the value is not directly a measure of how good the modeled values are, but rather a measure of how good a predictor might be constructed from the modeled values (by creating a revised predictor of the form α + βƒi). According to Everitt, this usage is specifically the definition of the term "coefficient of determination": the square of the correlation between two (general) variables.

Interpretation
R2 is a measure of the goodness of fit of a model. In regression, the R2 coefficient of determination is a statistical measure of how well the regression predictions approximate the real data points. An R2 of 1 indicates that the regression predictions perfectly fit the data.

Values of R2 outside the range 0 to 1 occur when the model fits the data worse than the worst possible least-squares predictor (equivalent to a horizontal hyperplane at a height equal to the mean of the observed data). This occurs when a wrong model was chosen, or nonsensical constraints were applied by mistake. If equation 1 of Kvålseth is used (this is the equation used most often), R2 can be less than zero. If equation 2 of Kvålseth is used, R2 can be greater than one.

In all instances where R2 is used, the predictors are calculated by ordinary least-squares regression: that is, by minimizing SSres. In this case, R2 increases as the number of variables in the model is increased (R2 is monotone increasing with the number of variables included—it will never decrease). This illustrates a drawback to one possible use of R2, where one might keep adding variables (kitchen sink regression) to increase the R2 value. For example, if one is trying to predict the sales of a model of car from the car's gas mileage, price, and engine power, one can include probably irrelevant factors such as the first letter of the model's name or the height of the lead engineer designing the car because the R2 will never decrease as variables are added and will likely experience an increase due to chance alone.

This leads to the alternative approach of looking at the adjusted R2. The explanation of this statistic is almost the same as R2 but it penalizes the statistic as extra variables are included in the model. For cases other than fitting by ordinary least squares, the R2 statistic can be calculated as above and may still be a useful measure. If fitting is by weighted least squares or generalized least squares, alternative versions of R2 can be calculated appropriate to those statistical frameworks, while the "raw" R2 may still be useful if it is more easily interpreted. Values for R2 can be calculated for any type of predictive model, which need not have a statistical basis.

In a multiple linear model
Consider a linear model with more than a single explanatory variable, of the form
 * $$Y_i = \beta_0 + \sum_{j=1}^p \beta_j X_{i,j} + \varepsilon_i,$$

where, for the ith case, $${Y_i}$$ is the response variable, $$X_{i,1},\dots,X_{i,p}$$ are p regressors, and $$\varepsilon_i$$ is a mean zero error term. The quantities $$\beta_0,\dots,\beta_p$$ are unknown coefficients, whose values are estimated by least squares. The coefficient of determination R2 is a measure of the global fit of the model. Specifically, R2 is an element of [0, 1] and represents the proportion of variability in Yi that may be attributed to some linear combination of the regressors (explanatory variables) in X.

R2 is often interpreted as the proportion of response variation "explained" by the regressors in the model. Thus, R2 = 1 indicates that the fitted model explains all variability in $$y$$, while R2 = 0 indicates no 'linear' relationship (for straight line regression, this means that the straight line model is a constant line (slope = 0, intercept = $$\bar{y}$$) between the response variable and regressors). An interior value such as R2 = 0.7 may be interpreted as follows: "Seventy percent of the variance in the response variable can be explained by the explanatory variables. The remaining thirty percent can be attributed to unknown, lurking variables or inherent variability."

A caution that applies to R2, as to other statistical descriptions of correlation and association is that "correlation does not imply causation." In other words, while correlations may sometimes provide valuable clues in uncovering causal relationships among variables, a non-zero estimated correlation between two variables is not, on its own, evidence that changing the value of one variable would result in changes in the values of other variables. For example, the practice of carrying matches (or a lighter) is correlated with incidence of lung cancer, but carrying matches does not cause cancer (in the standard sense of "cause").

In case of a single regressor, fitted by least squares, R2 is the square of the Pearson product-moment correlation coefficient relating the regressor and the response variable. More generally, R2 is the square of the correlation between the constructed predictor and the response variable. With more than one regressor, the R2 can be referred to as the coefficient of multiple determination.

Inflation of R2
In least squares regression using typical data, R2 is at least weakly increasing with an increase in number of regressors in the model. Because increases in the number of regressors increase the value of R2, R2 alone cannot be used as a meaningful comparison of models with very different numbers of independent variables. For a meaningful comparison between two models, an F-test can be performed on the residual sum of squares, similar to the F-tests in Granger causality, though this is not always appropriate. As a reminder of this, some authors denote R2 by Rq2, where q is the number of columns in X (the number of explanators including the constant).

To demonstrate this property, first recall that the objective of least squares linear regression is
 * $$\min_b SS_\text{res}(b) \Rightarrow \min_b \sum_i (y_i - X_ib)^2\,$$

where Xi is a row vector of values of explanatory variables for case i and b is a column vector of coefficients of the respective elements of Xi.

The optimal value of the objective is weakly smaller as more explanatory variables are added and hence additional columns of $$X$$ (the explanatory data matrix whose ith row is Xi) are added, by the fact that less constrained minimization leads to an optimal cost which is weakly smaller than more constrained minimization does. Given the previous conclusion and noting that $$SS_{tot}$$ depends only on y, the non-decreasing property of R2 follows directly from the definition above.

The intuitive reason that using an additional explanatory variable cannot lower the R2 is this: Minimizing $$SS_\text{res}$$ is equivalent to maximizing R2. When the extra variable is included, the data always have the option of giving it an estimated coefficient of zero, leaving the predicted values and the R2 unchanged. The only way that the optimization problem will give a non-zero coefficient is if doing so improves the R2.

The above gives an analytical explanation of the inflation of R2. Next, an example based on ordinary least square from a geometric perspective is shown below.

A simple case to be considered first:
 * $$Y=\beta_0+\beta_1\cdot X_1+\varepsilon\,$$

This equation describes the ordinary least squares regression model with one regressor. The prediction is shown as the red vector in the figure on the right. Geometrically, it is the projection of true value onto a model space in $$\mathbb{R}$$ (without intercept). The residual is shown as the red line.
 * $$Y=\beta_0+\beta_1\cdot X_1+\beta_2\cdot X_2 + \varepsilon\,$$

This equation corresponds to the ordinary least squares regression model with two regressors. The prediction is shown as the blue vector in the figure on the right. Geometrically, it is the projection of true value onto a larger model space in $$\mathbb{R}^2$$ (without intercept). Noticeably, the values of $$\beta_0$$ and $$\beta_0$$ are not the same as in the equation for smaller model space as long as $$X_1$$ and $$X_2$$ are not zero vectors. Therefore, the equations are expected to yield different predictions (i.e., the blue vector is expected to be different from the red vector). The least squares regression criterion ensures that the residual is minimized. In the figure, the blue line representing the residual is orthogonal to the model space in $$\mathbb{R}^2$$, giving the minimal distance from the space.

The smaller model space is a subspace of the larger one, and thereby the residual of the smaller model is guaranteed to be larger. Comparing the red and blue lines in the figure, the blue line is orthogonal to the space, and any other line would be larger than the blue one. Considering the calculation for R2, a smaller value of $$SS_{tot}$$ will lead to a larger value of R2, meaning that adding regressors will result in inflation of R2.

Caveats
R2 does not indicate whether:
 * the independent variables are a cause of the changes in the dependent variable;
 * omitted-variable bias exists;
 * the correct regression was used;
 * the most appropriate set of independent variables has been chosen;
 * there is collinearity present in the data on the explanatory variables;
 * the model might be improved by using transformed versions of the existing set of independent variables;
 * there are enough data points to make a solid conclusion.

Adjusted R2
The use of an adjusted R2 (one common notation is $$\bar R^2$$, pronounced "R bar squared"; another is $$R^2_{\text{a}}$$ or $$R^2_{\text{adj}}$$) is an attempt to account for the phenomenon of the R2 automatically increasing when extra explanatory variables are added to the model. There are many different ways of adjusting. By far the most used one, to the point that it is typically just referred to as adjusted R, is the correction proposed by Mordecai Ezekiel. The adjusted R2 is defined as
 * $$\bar R^2 = {1-{SS_\text{res}/\text{df}_\text{res} \over SS_\text{tot}/\text{df}_\text{tot}}}$$

where dfres is the degrees of freedom of the estimate of the population variance around the model, and dftot is the degrees of freedom of the estimate of the population variance around the mean. dfres is given in terms of the sample size n and the number of variables p in the model, dfres = n − p − 1. dftot is given in the same way, but with p being unity for the mean, i.e. dftot = n − 1.

Inserting the degrees of freedom and using the definition of R2, it can be rewritten as:
 * $$\bar R^2 = 1-(1-R^2){n-1 \over n-p-1}$$

where p is the total number of explanatory variables in the model, and n is the sample size.

The adjusted R2 can be negative, and its value will always be less than or equal to that of R2. Unlike R2, the adjusted R2 increases only when the increase in R2 (due to the inclusion of a new explanatory variable) is more than one would expect to see by chance. If a set of explanatory variables with a predetermined hierarchy of importance are introduced into a regression one at a time, with the adjusted R2 computed each time, the level at which adjusted R2 reaches a maximum, and decreases afterward, would be the regression with the ideal combination of having the best fit without excess/unnecessary terms.

The adjusted R2 can be interpreted as an instance of the bias-variance tradeoff. When we consider the performance of a model, a lower error represents a better performance. When the model becomes more complex, the variance will increase whereas the square of bias will decrease, and these two metrices add up to be the total error. Combining these two trends, the bias-variance tradeoff describes a relationship between the performance of the model and its complexity, which is shown as a u-shape curve on the right. For the adjusted R2 specifically, the model complexity (i.e. number of parameters) affects the R2 and the term / frac and thereby captures their attributes in the overall performance of the model.

R2 can be interpreted as the variance of the model, which is influenced by the model complexity. A high R2 indicates a lower bias error because the model can better explain the change of Y with predictors. For this reason, we make fewer (erroneous) assumptions, and this results in a lower bias error. Meanwhile, to accommodate fewer assumptions, the model tends to be more complex. Based on bias-variance tradeoff, a higher complexity will lead to a decrease in bias and a better performance (below the optimal line). In $\overline{R}$2, the term (1 − R2) will be lower with high complexity and resulting in a higher $\overline{R}$2, consistently indicating a better performance.

On the other hand, the term/frac term is reversely affected by the model complexity. The term/frac will increase when adding regressors (i.e. increased model complexity) and lead to worse performance. Based on bias-variance tradeoff, a higher model complexity (beyond the optimal line) leads to increasing errors and a worse performance.

Considering the calculation of $\overline{R}$2, more parameters will increase the R2 and lead to an increase in $\overline{R}$2. Nevertheless, adding more parameters will increase the term/frac and thus decrease $\overline{R}$2. These two trends construct a reverse u-shape relationship between model complexity and $\overline{R}$2, which is in consistent with the u-shape trend of model complexity vs. overall performance. Unlike R2, which will always increase when model complexity increases, $\overline{R}$2 will increase only when the bias eliminated by the added regressor is greater than the variance introduced simultaneously. Using $\overline{R}$2 instead of R2 could thereby prevent overfitting.

Following the same logic, adjusted R2 can be interpreted as a less biased estimator of the population R2, whereas the observed sample R2 is a positively biased estimate of the population value. Adjusted R2 is more appropriate when evaluating model fit (the variance in the dependent variable accounted for by the independent variables) and in comparing alternative models in the feature selection stage of model building.

The principle behind the adjusted R2 statistic can be seen by rewriting the ordinary R2 as
 * $$R^2 = {1-{\text{VAR}_\text{res} \over \text{VAR}_\text{tot}}}$$

where $$\text{VAR}_\text{res} = SS_\text{res}/n$$ and $$\text{VAR}_\text{tot} = SS_\text{tot}/n$$ are the sample variances of the estimated residuals and the dependent variable respectively, which can be seen as biased estimates of the population variances of the errors and of the dependent variable. These estimates are replaced by statistically unbiased versions: $$\text{VAR}_\text{res} = SS_\text{res}/(n-p)$$ and $$\text{VAR}_\text{tot} = SS_\text{tot}/(n-1)$$.

Despite using unbiased estimators for the population variances of the error and the dependent variable, adjusted R2 is not an unbiased estimator of the population R2, which results by using the population variances of the errors and the dependent variable instead of estimating them. Ingram Olkin and John W. Pratt derived the minimum-variance unbiased estimator for the population R2, which is known as Olkin–Pratt estimator. Comparisons of different approaches for adjusting R2 concluded that in most situations either an approximate version of the Olkin–Pratt estimator or the exact Olkin–Pratt estimator should be preferred over (Ezekiel) adjusted R2.

Coefficient of partial determination
The coefficient of partial determination can be defined as the proportion of variation that cannot be explained in a reduced model, but can be explained by the predictors specified in a full(er) model. This coefficient is used to provide insight into whether or not one or more additional predictors may be useful in a more fully specified regression model.

The calculation for the partial R2 is relatively straightforward after estimating two models and generating the ANOVA tables for them. The calculation for the partial R2 is
 * $$\frac{SS_\text{ res, reduced} - SS_\text{ res, full}}{SS_\text{ res, reduced}},$$

which is analogous to the usual coefficient of determination:
 * $$\frac{SS_\text{tot} - SS_\text{res}}{SS_\text{tot}}.$$

Generalizing and decomposing R2
As explained above, model selection heuristics such as the adjusted R2 criterion and the F-test examine whether the total R2 sufficiently increases to determine if a new regressor should be added to the model. If a regressor is added to the model that is highly correlated with other regressors which have already been included, then the total R2 will hardly increase, even if the new regressor is of relevance. As a result, the above-mentioned heuristics will ignore relevant regressors when cross-correlations are high.

Alternatively, one can decompose a generalized version of R2 to quantify the relevance of deviating from a hypothesis. As Hoornweg (2018) shows, several shrinkage estimators – such as Bayesian linear regression, ridge regression, and the (adaptive) lasso – make use of this decomposition of R2 when they gradually shrink parameters from the unrestricted OLS solutions towards the hypothesized values. Let us first define the linear regression model as
 * $$y=X\beta+\varepsilon.$$

It is assumed that the matrix X is standardized with Z-scores and that the column vector $$y$$ is centered to have a mean of zero. Let the column vector $$\beta_0$$ refer to the hypothesized regression parameters and let the column vector $$b$$ denote the estimated parameters. We can then define
 * $$R^2=1-\frac{(y-Xb)'(y-Xb)}{(y-X\beta_0)'(y-X\beta_0)}.$$

An R2 of 75% means that the in-sample accuracy improves by 75% if the data-optimized b solutions are used instead of the hypothesized $$\beta_0$$ values. In the special case that $$\beta_0$$ is a vector of zeros, we obtain the traditional R2 again.

The individual effect on R2 of deviating from a hypothesis can be computed with $$R^\otimes$$ ('R-outer'). This $$p$$ times $$p$$ matrix is given by
 * $$R^{\otimes}=(X'\tilde y_0)(X'\tilde y_0)' (X'X)^{-1}(\tilde y_0'\tilde y_0)^{-1},$$

where $$\tilde y_0=y-X\beta_0$$. The diagonal elements of $$R^\otimes$$ exactly add up to R2. If regressors are uncorrelated and $$\beta_0$$ is a vector of zeros, then the $$j^\text{th}$$ diagonal element of $$R^{\otimes}$$ simply corresponds to the r2 value between $$x_j$$ and $$y$$. When regressors $$x_i$$ and $$x_j$$ are correlated, $$R^\otimes_{ii}$$ might increase at the cost of a decrease in $$R^{\otimes}_{jj}$$. As a result, the diagonal elements of $$R^{\otimes}$$ may be smaller than 0 and, in more exceptional cases, larger than 1. To deal with such uncertainties, several shrinkage estimators implicitly take a weighted average of the diagonal elements of $$R^{\otimes}$$ to quantify the relevance of deviating from a hypothesized value. Click on the lasso for an example.

R2 in logistic regression
In the case of logistic regression, usually fit by maximum likelihood, there are several choices of pseudo-R2.

One is the generalized R2 originally proposed by Cox & Snell, and independently by Magee:
 * $$R^2 = 1 - \left({ \mathcal{L}(0) \over \mathcal{L}(\widehat{\theta}) }\right)^{2/n}$$

where $$\mathcal{L}(0)$$ is the likelihood of the model with only the intercept, $${\mathcal{L}(\widehat{\theta})}$$ is the likelihood of the estimated model (i.e., the model with a given set of parameter estimates) and n is the sample size. It is easily rewritten to:
 * $$R^2 = 1 - e^{\frac{2}{n} (\ln(\mathcal{L}(0)) - \ln(\mathcal{L}(\widehat{\theta}))} = 1 - e^{-D/n}$$

where D is the test statistic of the likelihood ratio test.

Nico Nagelkerke noted that it had the following properties:
 * 1) It is consistent with the classical coefficient of determination when both can be computed;
 * 2) Its value is maximised by the maximum likelihood estimation of a model;
 * 3) It is asymptotically independent of the sample size;
 * 4) The interpretation is the proportion of the variation explained by the model;
 * 5) The values are between 0 and 1, with 0 denoting that model does not explain any variation and 1 denoting that it perfectly explains the observed variation;
 * 6) It does not have any unit.

However, in the case of a logistic model, where $$\mathcal{L}(\widehat{\theta})$$ cannot be greater than 1, R2 is between 0 and $$ R^2_\max = 1- (\mathcal{L}(0))^{2/n} $$: thus, Nagelkerke suggested the possibility to define a scaled R2 as R2/R2max.

Comparison with norm of residuals
Occasionally, the norm of residuals is used for indicating goodness of fit. This term is calculated as the square-root of the sum of squares of residuals:
 * $$\text{norm of residuals} = \sqrt{SS_\text{res}} = \| e \|. $$

Both R2 and the norm of residuals have their relative merits. For least squares analysis R2 varies between 0 and 1, with larger numbers indicating better fits and 1 representing a perfect fit. The norm of residuals varies from 0 to infinity with smaller numbers indicating better fits and zero indicating a perfect fit. One advantage and disadvantage of R2 is the $$SS_\text{tot}$$ term acts to normalize the value. If the yi values are all multiplied by a constant, the norm of residuals will also change by that constant but R2 will stay the same. As a basic example, for the linear least squares fit to the set of data:
 * {| class="wikitable"

! x ! y
 * 1 || 2 || 3 || 4 || 5
 * 1.9 || 3.7 || 5.8 || 8.0 || 9.6
 * }

R2 = 0.998, and norm of residuals = 0.302. If all values of y are multiplied by 1000 (for example, in an SI prefix change), then R2 remains the same, but norm of residuals = 302.

Another single-parameter indicator of fit is the RMSE of the residuals, or standard deviation of the residuals. This would have a value of 0.135 for the above example given that the fit was linear with an unforced intercept.

History
The creation of the coefficient of determination has been attributed to the geneticist Sewall Wright and was first published in 1921.

title: Coefficient of determination


text:
USS Tortuga (LSD-46)

USS Tortuga (LSD-46) is a Whidbey Island-class dock landing ship of the United States Navy. She was the second Navy ship to be named for the Dry Tortugas, a group of desert coral islets 60 mi west of Key West, Florida.

Tortuga was laid down on 23 March 1987, by the Avondale Shipyards, New Orleans, Louisiana. The threat of Hurricane Gilbert in the Gulf of Mexico forced an early launching of the ship, as a precautionary measure, on 15 September 1988. On 19 November 1988, Mrs. Rosemary Parker Schoultz, the ship's sponsor, presided over the christening ceremony, breaking the traditional bottle of champagne over the bow of the ship. Tortuga was commissioned on 17 November 1990.

History
In 1997, Tortuga was commanded by Commander Kenneth M. Rome, and made a Mediterranean deployment from 1 July 1998 until 8 December 1998. In 1999, Commander J.M. Burdon assumed command and commanded the ship until his retirement in late 2000. He was succeeded by Commander James P. Driscoll, and in October 2000, the ship was ordered on an emergency deployment to support UNITAS 2000 in the South Pacific after USS La Moure County (LST-1194) had run aground on an underwater mountain off the coast of Valparaíso, Chile suffering catastrophic damage. After successfully finishing the UNITAS deployment, Tortuga returned to Little Creek. In January 2001, Tortuga was assigned as flagship to the Standing Naval Forces Atlantic a NATO peacekeeping/quick reaction force.

In August 2002, Tortuga departed North Carolina with marines and sailors from BLT 2/2. During this time she was the home to Echo Company, CAAT Red and a section of CAAT Green, as well as LAR and AmTracks. The group was on what was supposed to be a six-month deployment. In September and October 2002, Tortuga was in Thessaloniki, Greece in support of BLT 2/2 operations in Kosovo. In November Tortuga transited the Suez Canal with the rest of her ARG. They were then assigned to the U.S. Fifth Fleet. In mid-November, Tortuga put Marines from BLT 2/2 ashore in Djibouti. She then proceeded south of the equator to the Seychelle Islands. In March 2003, Tortuga proceeded to the Persian Gulf to deploy her Marines and sailors in support of Operation Iraqi Freedom. The Marines and sailors returned to Tortuga in April and returned to the United States on 27 May 2003 after a 9-month deployment.

In 2005, Tortuga was commanded by Commander Mark H. Scovill, homeported at NAB Little Creek, Virginia, and assigned to Amphibious Group 2 of the Atlantic Fleet.

On 25 August 2005 Tortuga and her crew were pulled from a training exercise and sent to New Orleans to become part of Joint Task Force Katrina. She was the first Navy warship to sail up the Mississippi River following the hurricane and berthed on the West Bank of New Orleans Naval Station. The ship instantly became a major rally point for the scattered military and civilian forces across New Orleans. Tortuga's crew conducted rescue missions in the flooded Ninth Ward and assisted local officials from St. Bernard Parish. Crew members employed combat rubber raiding craft (CRRCs) which allowed them to search flooded areas with many underwater obstacles. As the crew rescued people from neighborhoods they were brought back to Tortuga with 7 and 11 meter RHIBs. Evacuees were processed, received medical attention, and were forwarded to their next destination at the earliest possible time. As water receded in New Orleans and the CRRCs became useless, the ship served as the headquarters for the 618th ESC "Nasty", and the 307th Engineer Battalion, 82nd Airborne Division U.S. Army camped out on the Naval Station, while they worked in conjunction with U.S. Navy units to conduct rescue and clean up missions.

On 14 October 2005, the U.S. Navy announced that Tortuga would be forward-deployed to Sasebo, Japan to replace USS Fort McHenry (LSD-43). Tortuga arrived in Sasebo 31 March 2006 for turnover and assignment as part of the U.S. Navy's Forward Deployed Naval Forces (FDNF). On 12 April the crews of the two ships completed an exchange-of-command process. Conducted in 12 days, the hull swap between Fort McHenry and Tortuga was the quickest in the history of the U.S. Navy. Tortuga's former crew departed Sasebo on 13 April 2006 to return to Little Creek on board Fort McHenry.

On 15 May, only a month following the swap, Tortuga departed for a three-month deployment. The deployment was centered around an annual exercise called Cooperation Afloat Readiness and Training (CARAT) 2006. Tortuga joined a newly established task group (TG 73.5) reporting directly to Commander, Logistics Group WESTPAC out of Singapore. The group consisted of five ships, Tortuga, the destroyer USS Hopper (DDG-70), the frigate USS Crommelin (FFG-37), the supply ship USNS Salvor (T-ARS-52), and the United States Coast Guard vessel USCGC Sherman (WHEC-720). The group visited and operated with the navies of Singapore, Thailand, Indonesia, Malaysia, Brunei, and Philippines.

USS Tortuga visited the island of Iwo Jima in March 2008 and March 2010 to celebrate the anniversary of the World War II battle fought there.

In 2011, Tortuga participated in disaster relief after the 2011 Tōhoku earthquake and tsunami. As part of Operation Tomodachi, the ship transported Japanese Self-Defense Force personnel and equipment from Hokkaido to Honshu island. Divers from the ship helped map and clear debris from the ports of Hachinohe, Aomori and Miyako, Iwate, facilitating both ports to reopen to ship traffic.

From 5 to 7 April 2013, Tortuga participated in a joint annual military exercises together with the Armed Forces of the Philippines to enhance regional cooperation and effectiveness in the region. More than 8,000 personnel will be conducted the Balikatan exercises.

In August 2013, Tortuga completed a hull swap with USS Ashland (LSD-48) and then returned to her new homeport of Little Creek, Virginia.

title: USS Tortuga (LSD-46)


text:
Grupo Abril

Grupo Abril (simply also known as Abril) is a Brazilian media conglomerate headquartered in São Paulo. The company is the holding company of Editora Abril, which publishes the weekly newsmagazine Veja.

History and profile
Victor Civita, an Italian businessman, founded Editora Abril in 1950, which was the first publisher of Walt Disney comics in Brazil. He also published other comics and books. Gradually, he developed magazines for specific markets. In the 1960s, with his son, Roberto Civita, he expanded to publishing several market-specific magazines, such as Quatro Rodas, Claudia, and others. Veja, the weekly newsmagazine, was founded in 1968 and led by the younger Civita.

Abril had several partnerships in the emerging Brazilian pay TV market during the 90s, including local franchises of American channels such as Disney Channel, MTV and ESPN. The company also launched a cable TV provider service called TVA (Televisão Abril), which by 1999 was considered one of the top two cable carriers in Brazil, along with Globocabo (Grupo Globo).

With exception of MTV and TVA, all properties were sold at the beginning of the 2000s. In 2007, Fiz TV and Ideal channels were launched in an effort to return to the pay TV market, but the project was a failure and it was discontinued in 2009. Later in 2012, TVA was completely sold to Telefónica, the MTV brand returned to Viacom, and in late 2013 its broadcast network was sold to Grupo Spring, which publishes the Brazilian edition of Rolling Stone.

Grupo Abril also is the owner of Fernando Chinaglia, the largest distributor of publications in Latin America (acquired in 2007).

In addition to its media interests, the group has entered the education market with assets that produce many curriculum and instructional materials, including internet applications.

Today, the group is chaired by Fábio Barbosa following the death of Roberto Civita on May 26, 2013.

Magazines

 * Veja
 * Superinteressante (Brazilian version of Muy Interesante)
 * Mundo Estranho (Superinteressante spinoff)
 * Aventuras na História (Superinteressante spinoff)
 * Exame
 * Guia do Estudante
 * VIP
 * Placar
 * Capricho
 * Quatro Rodas
 * Nova (Brazilian version of Cosmopolitan)
 * Estilo de Vida (Brazilian version of InStyle)
 * Elle
 * Claudia
 * Manequim
 * Boa Forma
 * Saúde! É Vital
 * Caras
 * Casa Claudia
 * Arquitetura & Construção
 * Minha Casa
 * Recreio
 * Vida Simples

Out-of-home advertising

 * Rede Elemídia

Publishers

 * Editora Ática

Teaching systems

 * Sistema Anglo de Ensino
 * Ser Educacional
 * GEO
 * Sistema Farias Brito

Courses and colleges

 * Anglo Vestibulares
 * Colégio pH
 * Alfacon Concursos
 * Colegio Sigma

English

 * Red Balloon

New business

 * Sistema de Ensino Técnico ETB
 * Ei Você
 * Edumobi
 * Alfacon - Preparatório para Concursos
 * Escola Satélite

Past assets

 * MTV Brasil Ltda. (before named MTV Brasil, in 2005 change by enterprise Abril Radiodifusão)
 * MTV Brasil (was replaced after the business was returned to Viacom in October 2013)
 * Listel (sold to BellSouth, now part of Carvajal-owned Publicar)
 * Disney Channel (stake sold to Disney Channel Brazil)
 * UOL (with operations to Grupo Folha)
 * Abril Vídeo (closed in 1999, replaced with Brazilian subsidiaries of Walt Disney Studios Home Entertainment and 20th Century Studios Home Entertainment)
 * Abril Music (record label operated between 1998 and 2003, ceased operations due to competition from major record companies)
 * Bravo Brasil (a joint venture with the American cable network Bravo, currently Pramer-owned Film&Arts)
 * CMT Brasil (the Brazilian version of Country Music Television, a joint venture with the network closed in 2002)
 * Fiz (closed due to low carriage and viewership, concept lives on through Fiz na MTV)
 * BRZ (between 2012 and 2014)
 * TVA (between 1991 and 2012)
 * IdealTV (Aquidied to Grupo Spring in 2015)

Abril Vídeo
Abril Vídeo was Grupo Abril's home video division that distributed TV shows, videos and movies on VHS. It began operation on April 6, 1983 and closed in 1999. It distributed films from The Walt Disney Company and 20th Century Fox on VHS for the Brazilian market.

title: Grupo Abril


text:
User:Brookie/Tecky Stuff

Please don't monkey about with joke edits. We try hard here to produce something we can all be proud of, and silliness doesn't help. Articles are monitored, and users messing around could be blocked. Why not try contributing something useful instead? Have a look at the links below and get stuck in properly:
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Thanks – and enjoy yourself as a responsible Wikipedian! >

Facebook WikiProject Notability ✅ ✅ Template messages/Cleanup

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 * THIS PAGE IS AN ALMIGHTY MESS SINCE I DUMPED A LOAD OF STUFF IN IT FROM MY OLD USER PAGE - IT WILL BE TIDIED ...

Reason ~  ~

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 * Example requests for permission
 * ~ : [[Image:Stop x nuvola with clock.svg|40px|left]] You have been blocked temporarily from editing for abuse of editing privileges. Once the block has expired, you are welcome to make useful contributions. If you would like to be unblocked, you may appeal this block by adding the text   below this notice, but you should read the guide to appealing blocks first.

 Join the Discussion!

Counting thing

EASYy, AUTOMATED checking for copyright violations

Lets be honest, finding/proving a copyright vio is a complete bitch. You copy random strings from the article into Google, put some quotes around them, and spin the dice. No longer! Break free from your chains of boring grunt work!

I have come across many admins and users who don't know about this site, and I think they should.

Vio Finder

Copy in a wikipedia page's URL, hit search and in around 5 seconds it will find any copyright violations! Just a few minutes ago I used it here. Have fun, and lets get the copyrighted crap out of Wikipedia =D (remember that wikipedia mirrors are no violations, heh) --mboverload@ 08:07, 11 July 2006 (UTC)

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~

Protected titles
Hi, I noticed that you were trying to protect the title of AlpInvest Partners, so I deleted the page again (CSD G6) and added it to the protected titles page. Since was TfD'd, we should be using the protected title cascade-protect solution now. There are instructions on the page at WP:PT, and if you need any further help just [ give me a call]. Cheers, Nihiltres ( t .l ) 15:40, 21 September 2007 (UTC)

 * Yes. The way that it works is that the protected page lists are cascade-full-protected, so any page transcluded into them is automatically protected from editing, even though its nonexistence may prevent it from being directly protected. The template transcludes the empty page in a nice orderly way so that the list doesn't get messy. This is generally better than  was, because it means links to the page are red, the history is hidden, and all the usual advantages of deletion. :)  Nihiltres ( t .l ) 15:49, 21 September 2007 (UTC)

Closing AfD
The templates are at and ab (or, "afd top" and "afd bottom"). They are used like this:

delete/keep/speedy delete... (possibly add an explanation here) ~

discussion

They are to be used at the very begin and end of the subpage. For RfD, MfD, etc. there are other templates with similar names. Tizio, Caio, Sempronio 12:14, 4 October 2006 (UTC)

Brookie aide memoire
Tags for images link Votes for deletion [Boilerplate] [Barnstar] WP:CG Admin stuff

Stub types

<nowiki< The article you created at original article title was listed for deletion as An article about a real person that does not assert the importance or significance of its subject, criterion 7 in the criteria for speedy deletion, Articles section. Since it appears to be about you I've moved it to your user space instead, which was probably what you intended in the first place (a common mistake among newcomers!).

If you really did mean to create an article about yourself please be aware that this is often considered a bad idea, even if you do meet the guidelines for inclusion of biographies.

I know Wikipedia can be confusing for new editors, so you are welcome to ask for help at the help desk or on my talk page.

Tag
Brookie\talk
 * Brookie\talk =

Brookie\ talk

For preferences:

Brookie :) - a will o' the wisp ! (Talk!) Brookie :) - a will o' the wisp ! (Talk!)

New category

~

Undelete
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Byway
On Byway I have created a new category Rights of way - but having done that I don'tknow what to do to make it a useful category - the link shows as red and nneds a new page creating for it - what do I do now? Help! Brookie:the wind in the grass 19:19, 10 May 2005 (UTC)

 * To create the category page click the red link and create the category description page as you would an article.
 * If it is a subcategory of something else then just add it to that category as if it were an article (e.g. if it is a sub-category of Category:Streets and roads then just put at the end of the description.
 * Once you have save the category page, you can add other articles to it like you can to any other category. Thryduulf 20:53, 10 May 2005 (UTC)

Searching:

 * Just in case you don't know already: a good tip for searching is to use google, restricting your hits to wikipedia by adding site:en.wikipedia.org to your search query. I'm sure you've noticed already that wikipedia's own search is rather slow and doesn't necessarily give you what you're looking for when searching for 'teh'. A disadvantage with google search is that your changes won't be visible immediately to the search engine, so you'll have to learn to ignore hits relating to mistakes that you've recently corrected.

Sample:

Pop-up Installation

You have to create (or edit) a file in your userspace. For most people, this file is called User:Brookie/monobook.js where you should replace USERNAME with your username. Note that the case is important here: it's monobook.js and not Monobook.js.

(People using the classic skin should edit standard.js instead, and people using other skins probably know which file to edit :-) Edit that page and paste in these lines:

// User:Lupin/popups.js - please include this line

document.write(' ');

Clear your browser's cache and when you hover the mouse over internal links, popups should appear.

To install the script on the commons or wiktionary, repeat this procedure on those wikis.

To uninstall the script, just delete those lines from your javascript file and clear your browser's cache.

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How to provide a link to a specific Google Search

Especially on talk pages, and sometimes in External link sections, links to Google searches can be useful. The Wikimedia software that powers Wikipedia lets you make links to Google by including google: as the prefix for the link, like this:

 * Tipster.

Which looks like this:

 * google:Tipster

Note: It is important not to use spaces in the search. To add more parameters to the search, separate them by a plus sign, +. For a phrase search, use a hyphen (minus sign), -, between each word. E.g. to search for "Tip of the day", use Tip-of-the-day.

To provide a link to a Wikipedia-specific search, include in the google-link +site:en.wikipedia.org (no spaces before or after), like this:

 * google:Tip-of-the-day+site:en.wikipedia.org.

Which looks like this:

 * google:Tip-of-the-day+site:en.wikipedia.org

To clean up the link so that only the part you want to show is presented, use the pipe, like this:

 * "Tip of the day"

Which makes it look like this:

 * "Tip of the day"

Other watch
http://en.wikipedia.org/w/index.php?title=Special:Contributions&target=Ashley%20Pomeroy

title: User:Brookie/Tecky Stuff


text:
User:Improv/watched users

 * User:Unrepentant Stalinoid (hist)
 * User:213.132.184.2 (hist)
 * User:216.56.81.98 (hist) - Not a vandal, but needs work on style of contributions
 * User:198.208.159.14 (hist)
 * User:MarkSop (hist)
 * User:Merrick (hist)
 * User:AndyCapp (hist)
 * User:198.150.117.254 (hist)
 * User:Xed (hist)
 * User:Paradigmbuff (hist)
 * User:Blueflyer (hist)
 * User:152.163.100.68 (hist)

title: User:Improv/watched users