text:
Farmland, Indiana

Farmland is a town in Monroe Township, Randolph County, in the U.S. state of Indiana. The population was 1,333 at the 2010 census.

History
The Randolph County location has rolling hills, glacial features, and the Cabin Creek Raised Bog. Farmland was platted in 1852 when the railroad was extended to that point. The town most likely was so named on account of their fertile soil. A post office has been in operation at Farmland since 1853. Farmland was incorporated as a town in 1867.

The Farmland Downtown Historic District was listed on the National Register of Historic Places in 1994.

A sign posted by the state highway department welcomes people to Farmland and notes that it is "The Home of Ansel Toney, The Kite Man".

Geography
According to the 2010 census, Farmland has a total area of 0.53 sqmi, all land.

2010 census
As of the census of 2010, there were 1,333 people, 543 households, and 375 families living in the town. The population density was 2515.1 PD/sqmi. There were 612 housing units at an average density of 1154.7 /sqmi. The racial makeup of the town was 97.3% White, 0.2% African American, 1.4% Native American, 0.1% Asian, 0.2% from other races, and 1.0% from two or more races. Hispanic or Latino of any race were 1.1% of the population.

There were 543 households, of which 32.6% had children under the age of 18 living with them, 52.5% were married couples living together, 13.3% had a female householder with no husband present, 3.3% had a male householder with no wife present, and 30.9% were non-families. 26.3% of all households were made up of individuals, and 12.2% had someone living alone who was 65 years of age or older. The average household size was 2.45 and the average family size was 2.94.

The median age in the town was 39.3 years. 24.4% of residents were under the age of 18; 8.1% were between the ages of 18 and 24; 24.8% were from 25 to 44; 27.1% were from 45 to 64; and 15.8% were 65 years of age or older. The gender makeup of the town was 48.0% male and 52.0% female.

2000 census
As of the census of 2000, there were 1,456 people, 573 households, and 414 families living in the town. The population density was 2,898.1 PD/sqmi. There were 608 housing units at an average density of 1,210.2 /sqmi. The racial makeup of the town was 98.49% White, 0.48% Native American, 0.07% Asian, 0.07% from other races, and 0.89% from two or more races. Hispanic or Latino of any race were 0.21% of the population.

There were 573 households, out of which 34.4% had children under the age of 18 living with them, 58.3% were married couples living together, 10.5% had a female householder with no husband present, and 27.7% were non-families. 23.7% of all households were made up of individuals, and 11.2% had someone living alone who was 65 years of age or older. The average household size was 2.54 and the average family size was 3.03.

In the town, the population was spread out, with 28.0% under the age of 18, 6.7% from 18 to 24, 28.4% from 25 to 44, 21.0% from 45 to 64, and 15.9% who were 65 years of age or older. The median age was 36 years. For every 100 females, there were 92.3 males. For every 100 females age 18 and over, there were 86.0 males.

The median income for a household in the town was $36,250, and the median income for a family was $45,000. Males had a median income of $31,795 versus $21,750 for females. The per capita income for the town was $18,405. About 3.4% of families and 6.5% of the population were below the poverty line, including 7.4% of those under age 18 and 3.5% of those age 65 or over.

Education
The town has a lending library, the Farmland Public Library.

title: Farmland, Indiana


text:
Losantville, Indiana

Losantville is a town in Union Township, Randolph County, in the U.S. state of Indiana. The population was 237 at the 2010 census.

History
Losantville was first known as Hunts Cross Roads, and under the latter name was platted in 1851. The present name is derived from Losantiville, an early name for Cincinnati, Ohio. A post office called Losantville has been in operation since 1854.

Geography
According to the 2010 census, Losantville has a total area of 0.19 sqmi, all land.

2010 census
As of the census of 2010, there were 237 people, 105 households, and 63 families living in the town. The population density was 1247.4 PD/sqmi. There were 124 housing units at an average density of 652.6 /sqmi. The racial makeup of the town was 97.0% White, 0.4% Native American, and 2.5% from two or more races.

There were 105 households, of which 29.5% had children under the age of 18 living with them, 47.6% were married couples living together, 8.6% had a female householder with no husband present, 3.8% had a male householder with no wife present, and 40.0% were non-families. 31.4% of all households were made up of individuals, and 15.2% had someone living alone who was 65 years of age or older. The average household size was 2.26 and the average family size was 2.87.

The median age in the town was 37.4 years. 23.2% of residents were under the age of 18; 8% were between the ages of 18 and 24; 29.5% were from 25 to 44; 22.8% were from 45 to 64; and 16.5% were 65 years of age or older. The gender makeup of the town was 49.8% male and 50.2% female.

2000 census
As of the census of 2000, there were 280 people, 115 households, and 76 families living in the town. The population density was 1,584.8 PD/sqmi. There were 124 housing units at an average density of 701.9 /sqmi. The racial makeup of the town was 100.00% White.

There were 115 households, out of which 32.2% had children under the age of 18 living with them, 53.0% were married couples living together, 9.6% had a female householder with no husband present, and 33.9% were non-families. 28.7% of all households were made up of individuals, and 14.8% had someone living alone who was 65 years of age or older. The average household size was 2.43 and the average family size was 3.03.

In the town, the population was spread out, with 29.6% under the age of 18, 7.5% from 18 to 24, 26.1% from 25 to 44, 20.7% from 45 to 64, and 16.1% who were 65 years of age or older. The median age was 34 years. For every 100 females, there were 91.8 males. For every 100 females age 18 and over, there were 87.6 males.

The median income for a household in the town was $30,000, and the median income for a family was $36,250. Males had a median income of $32,222 versus $21,607 for females. The per capita income for the town was $12,885. About 16.0% of families and 15.8% of the population were below the poverty line, including 24.1% of those under the age of eighteen and 12.5% of those 65 or over.

Notable people
Losantville is the birthplace of former major league baseball player Claude Berry, a catcher for the Chicago White Sox, Philadelphia A's and Pittsburgh Rebels from 1904 through 1915. He had a lifetime batting average of .219.

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

title: Losantville, Indiana


text:
Lynn, Indiana

Lynn is a town in Washington Township, Randolph County, in the U.S. state of Indiana. The population was 1,097 at the 2010 census.

History
A post office was established at Lynn in 1838. The town plat was recorded in 1850.

Geography
Lynn is located at 40.0475°N, -84.94222°W (40.047590, -84.942173).

According to the 2010 census, Lynn has a total area of 0.56 sqmi, all land.

2010 census
As of the census of 2010, there were 1,097 people, 439 households, and 302 families living in the town. The population density was 1958.9 PD/sqmi. There were 498 housing units at an average density of 889.3 /sqmi. The racial makeup of the town was 97.7% White, 0.5% African American, 0.1% Native American, 0.7% Asian, 0.2% from other races, and 0.8% from two or more races. Hispanic or Latino of any race were 1.9% of the population.

There were 439 households, of which 35.1% had children under the age of 18 living with them, 49.0% were married couples living together, 13.4% had a female householder with no husband present, 6.4% had a male householder with no wife present, and 31.2% were non-families. 26.2% of all households were made up of individuals, and 14.8% had someone living alone who was 65 years of age or older. The average household size was 2.50 and the average family size was 2.97.

The median age in the town was 37.9 years. 25.7% of residents were under the age of 18; 8.7% were between the ages of 18 and 24; 24.6% were from 25 to 44; 23% were from 45 to 64; and 17.9% were 65 years of age or older. The gender makeup of the town was 48.3% male and 51.7% female.

2000 census
As of the census of 2000, there were 1,143 people, 468 households, and 313 families living in the town. The population density was 2,027.3 PD/sqmi. There were 508 housing units at an average density of 901.0 /sqmi. The racial makeup of the town was 96.50% White, 0.35% African American, 0.17% Native American, 0.44% Asian, 0.35% Pacific Islander, 0.26% from other races, and 1.92% from two or more races. Hispanic or Latino of any race were 0.44% of the population.

There were 468 households, out of which 32.1% had children under the age of 18 living with them, 53.2% were married couples living together, 9.4% had a female householder with no husband present, and 33.1% were non-families. 28.6% of all households were made up of individuals, and 14.7% had someone living alone who was 65 years of age or older. The average household size was 2.44 and the average family size was 2.99.

In the town, the population was spread out, with 26.3% under the age of 18, 9.2% from 18 to 24, 26.5% from 25 to 44, 22.4% from 45 to 64, and 15.6% who were 65 years of age or older. The median age was 36 years. For every 100 females, there were 92.4 males. For every 100 females age 18 and over, there were 91.4 males.

The median income for a household in the town was $32,368, and the median income for a family was $38,636. Males had a median income of $27,917 versus $20,438 for females. The per capita income for the town was $15,148. About 7.9% of families and 10.2% of the population were below the poverty line, including 11.1% of those under age 18 and 12.3% of those age 65 or over.

Education
The town has a lending library, the Lynn-Washington Township Public Library.

Notable person

 * Jim Jones grew up in the area. He would later lead the Peoples Temple cult murder at Jonestown.

title: Lynn, Indiana


text:
Modoc, Indiana

Modoc is a town in Union Township, Randolph County, in the U.S. state of Indiana. The population was 196 at the 2010 census.

History
There are several legends to how the town of Modoc got its name. One of them said that a man travelling on the train threw out a cigar box containing the name Modoc, and that Henry Conley, one of the first settlers of the town, picked it up and suggested to bystanders that it would be a good name for the town. Another theory is that Modoc was named after the Modoc people who were traditionally located in northern California. They became noted for their resistance to the US Army during the Modoc War, ending in 1873.

A post office has been in operation at Modoc since 1883.

2010 census
As of the census of 2010, there were 196 people, 72 households, and 50 families residing in the town. The population density was 1960.0 PD/sqmi. There were 87 housing units at an average density of 870.0 /sqmi. The racial makeup of the town was 94.9% White, 0.5% African American, 2.0% Pacific Islander, 0.5% from other races, and 2.0% from two or more races. Hispanic or Latino of any race were 1.5% of the population.

There were 72 households, of which 40.3% had children under the age of 18 living with them, 41.7% were married couples living together, 19.4% had a female householder with no husband present, 8.3% had a male householder with no wife present, and 30.6% were non-families. 23.6% of all households were made up of individuals, and 11.1% had someone living alone who was 65 years of age or older. The average household size was 2.72 and the average family size was 3.22.

The median age in the town was 33 years. 30.1% of residents were under the age of 18; 9.2% were between the ages of 18 and 24; 25% were from 25 to 44; 21.4% were from 45 to 64; and 14.3% were 65 years of age or older. The gender makeup of the town was 54.1% male and 45.9% female.

2000 census
As of the census of 2000, there were 225 people, 84 households, and 63 families residing in the town. The population density was 1,908.1 PD/sqmi. There were 90 housing units at an average density of 763.2 /sqmi. The racial makeup of the town was 96.00% White, 1.33% African American, 0.44% Pacific Islander, and 2.22% from two or more races. Hispanic or Latino of any race were 0.89% of the population.

There were 84 households, out of which 39.3% had children under the age of 18 living with them, 57.1% were married couples living together, 14.3% had a female householder with no husband present, and 25.0% were non-families. 22.6% of all households were made up of individuals, and 8.3% had someone living alone who was 65 years of age or older. The average household size was 2.68 and the average family size was 3.06.

In the town, the population was spread out, with 28.9% under the age of 18, 9.3% from 18 to 24, 31.6% from 25 to 44, 19.1% from 45 to 64, and 11.1% who were 65 years of age or older. The median age was 34 years. For every 100 females, there were 108.3 males. For every 100 females age 18 and over, there were 97.5 males.

The median income for a household in the town was $28,333, and the median income for a family was $32,708. Males had a median income of $31,250 versus $24,375 for females. The per capita income for the town was $13,230. About 12.3% of families and 14.6% of the population were below the poverty line, including 14.6% of those under the age of eighteen and 21.9% of those 65 or over.

Geography
Modoc is located about 25 miles southeast of Muncie, Indiana, and is about 25 miles northwest of Richmond, Indiana and 60 miles from the cities of Indianapolis, Indiana, Dayton, Ohio, and Cincinnati.

According to the 2010 census, Modoc has a total area of 0.1 sqmi, all land.

Highways
Major highways and roads that serve Modoc include Indiana 1 and U.S. 36

Randolph Farms Landfill
Randolph Farms Landfill is a 120-acre (486,000 m2) landfill owned by the Balkema family of Kalamazoo, Michigan, located in Randolph County, Indiana. In addition to Randolph County, it serves Delaware, Madison, Jay, Wayne, and Henry Counties in Indiana, as well as Darke and Miami Counties in Ohio. Randolph Farms was built on a limestone hill, directly over sand and gravel aquifers, near the drainage basins of White River and Whitewater River.

Education
Modoc's high school is Union High School. The mascot of the school is the Rocket. Their colors are royal blue and gold.

The I. B. & W. Railroad
Modoc, like many other small towns, probably would not have been built if the I. B. & W. Railroad had not made its way across southern Randolph County, in 1878. The location was poorly drained, but it had the advantage of being rather flat. It was plagued, for many years, by typhoid fever outbreaks. The railroad was built by many contractors. The railroad came through Modoc from the west, and met the contractors, who were building the next section east, in what was then a strip of timber about one-half mile east of where the depot was located.

title: Modoc, Indiana


text:
Parker City, Indiana

Parker City is a town in Monroe Township, Randolph County, in the U.S. state of Indiana. The population was 1,419 at the 2010 census.

History
Parker City was originally called Morristown, and under the latter name was platted in 1851. A post office has been in operation at Parker City since 1853.

Geography
According to the 2010 census, Parker City has a total area of 0.57 sqmi, all land. It is located at an elevation of 1025 feet.

2010 census
As of the census of 2010, there were 1,419 people, 544 households, and 372 families living in the town. The population density was 2489.5 PD/sqmi. There were 605 housing units at an average density of 1061.4 /sqmi. The racial makeup of the town was 97.7% White, 0.5% African American, 0.4% Native American, 0.1% from other races, and 1.4% from two or more races. Hispanic or Latino of any race were 0.4% of the population.

There were 544 households, of which 35.1% had children under the age of 18 living with them, 48.3% were married couples living together, 15.6% had a female householder with no husband present, 4.4% had a male householder with no wife present, and 31.6% were non-families. 27.6% of all households were made up of individuals, and 12.3% had someone living alone who was 65 years of age or older. The average household size was 2.46 and the average family size was 2.98.

The median age in the town was 40.5 years. 25.3% of residents were under the age of 18; 7.2% were between the ages of 18 and 24; 23.1% were from 25 to 44; 24.9% were from 45 to 64; and 19.4% were 65 years of age or older. The gender makeup of the town was 46.4% male and 53.6% female.

2000 census
As of the census of 2000, there were 1,416 people, 549 households, and 390 families living in the town. The population density was 2,444.0 PD/sqmi. There were 595 housing units at an average density of 1,026.9 /sqmi. The racial makeup of the town was 99.15% White, 0.14% African American, 0.14% Native American, 0.07% Asian, 0.07% Pacific Islander, 0.07% from other races, and 0.35% from two or more races. Hispanic or Latino of any race were 0.35% of the population.

There were 549 households, out of which 33.7% had children under the age of 18 living with them, 54.5% were married couples living together, 13.5% had a female householder with no husband present, and 28.8% were non-families. 24.2% of all households were made up of individuals, and 10.4% had someone living alone who was 65 years of age or older. The average household size was 2.45 and the average family size was 2.91.

In the town, the population was spread out, with 24.9% under the age of 18, 8.1% from 18 to 24, 28.4% from 25 to 44, 21.8% from 45 to 64, and 16.9% who were 65 years of age or older. The median age was 38 years. For every 100 females, there were 88.0 males. For every 100 females age 18 and over, there were 80.3 males.

The median income for a household in the town was $34,500, and the median income for a family was $38,984. Males had a median income of $31,875 versus $20,909 for females. The per capita income for the town was $16,552. About 9.9% of families and 12.8% of the population were below the poverty line, including 26.5% of those under age 18 and 6.9% of those age 65 or over.

title: Parker City, Indiana


text:
Ridgeville, Indiana

Ridgeville is a town in Franklin Township, Randolph County, Indiana, United States, along the Mississinewa River. The population was 688 at the 2020 census.

History
A post office called Ridgeville has been in operation since 1851. The town was named for its lofty elevation. Joab Ward, an old Settler to Ridgeville since 1819, originally laid out the village of Ridgeville, Randolph County, Indiana in 1854.

Geography
According to the 2010 census, Ridgeville has a total area of 0.55 sqmi, all land.

2010 census
As of the census of 2010, there were 803 people, 323 households, and 219 families living in the town. The population density was 1433.9 PD/sqmi. There were 371 housing units at an average density of 662.5 /sqmi. The racial makeup of the town was 97.4% White, 0.4% Native American, 0.4% Asian, 0.2% from other races, and 1.6% from two or more races. Hispanic or Latino of any race were 0.6% of the population.

There were 323 households, of which 35.6% had children under the age of 18 living with them, 46.7% were married couples living together, 15.8% had a female householder with no husband present, 5.3% had a male householder with no wife present, and 32.2% were non-families. 28.2% of all households were made up of individuals, and 12.1% had someone living alone who was 65 years of age or older. The average household size was 2.49 and the average family size was 3.01.

The median age in the town was 35.1 years. 28.1% of residents were under the age of 18; 8.5% were between the ages of 18 and 24; 26.2% were from 25 to 44; 22.9% were from 45 to 64; and 14.3% were 65 years of age or older. The gender makeup of the town was 50.8% male and 49.2% female.

2000 census
As of the census of 2000, there were 843 people, 340 households, and 234 families living in the town. The population density was 1,429.4 PD/sqmi. There were 368 housing units at an average density of 624.0 /sqmi. The racial makeup of the town was 99.76% White, 0.12% Native American, and 0.12% from two or more races. Hispanic or Latino of any race were 0.24% of the population.

There were 340 households, out of which 32.4% had children under the age of 18 living with them, 52.9% were married couples living together, 10.9% had a female householder with no husband present, and 30.9% were non-families. 25.9% of all households were made up of individuals, and 11.5% had someone living alone who was 65 years of age or older. The average household size was 2.48 and the average family size was 2.97.

In the town, the population was spread out, with 26.1% under the age of 18, 10.6% from 18 to 24, 27.9% from 25 to 44, 23.0% from 45 to 64, and 12.5% who were 65 years of age or older. The median age was 34 years. For every 100 females, there were 99.8 males. For every 100 females age 18 and over, there were 97.8 males.

The median income for a household in the town was $33,819, and the median income for a family was $40,547. Males had a median income of $27,596 compared to $24,107 for females. The per capita income for the town was $14,547. About 7.4% of families and 8.3% of the population were below the poverty line, including 7.9% of those under the age of 18 and 9.2% of those 65 and older.

Education
The town has a lending library, the Ridgeville Public Library.

Notable person

 * Wendell Meredith Stanley (1904–1971), co-recipient of the 1946 Nobel Prize in Chemistry, was born in Ridgeville.

title: Ridgeville, Indiana


text:
Saratoga, Indiana

Saratoga is a town in Ward Township, Randolph County, in the U.S. state of Indiana. The population was 254 at the 2010 census.

History
A post office has been in operation at Saratoga since 1867. Saratoga was platted in 1875.

Geography
According to the 2010 census, Saratoga has a total area of 0.26 sqmi, all land.

2010 census
As of the census of 2010, there were 254 people, 102 households, and 66 families living in the town. The population density was 940.7 PD/sqmi. There were 124 housing units at an average density of 459.3 /sqmi. The racial makeup of the town was 98.8% White, 0.8% from other races, and 0.4% from two or more races. Hispanic or Latino of any race were 2.0% of the population.

There were 102 households, of which 37.3% had children under the age of 18 living with them, 51.0% were married couples living together, 8.8% had a female householder with no husband present, 4.9% had a male householder with no wife present, and 35.3% were non-families. 30.4% of all households were made up of individuals, and 15.7% had someone living alone who was 65 years of age or older. The average household size was 2.49 and the average family size was 3.14.

The median age in the town was 36.5 years. 28.7% of residents were under the age of 18; 11.1% were between the ages of 18 and 24; 24% were from 25 to 44; 21.6% were from 45 to 64; and 14.6% were 65 years of age or older. The gender makeup of the town was 45.7% male and 54.3% female.

2000 census
As of the census of 2000, there were 288 people, 118 households, and 77 families living in the town. The population density was 1,090.9 PD/sqmi. There were 130 housing units at an average density of 492.4 /sqmi. The racial makeup of the town was 98.26% White, 0.69% African American, 1.04% from other races. Hispanic or Latino of any race were 3.12% of the population.

There were 118 households, out of which 30.5% had children under the age of 18 living with them, 55.9% were married couples living together, 8.5% had a female householder with no husband present, and 33.9% were non-families. 29.7% of all households were made up of individuals, and 12.7% had someone living alone who was 65 years of age or older. The average household size was 2.44 and the average family size was 3.08.

In the town, the population was spread out, with 26.0% under the age of 18, 10.1% from 18 to 24, 26.0% from 25 to 44, 20.5% from 45 to 64, and 17.4% who were 65 years of age or older. The median age was 35 years. For every 100 females, there were 93.3 males. For every 100 females age 18 and over, there were 86.8 males.

The median income for a household in the town was $30,000, and the median income for a family was $39,688. Males had a median income of $29,167 versus $13,750 for females. The per capita income for the town was $14,669. About 5.3% of families and 6.9% of the population were below the poverty line, including 7.9% of those under the age of eighteen and 7.0% of those 65 or over.

Education
Saratoga residents may obtain a free library card from the Winchester Community Public Library in Winchester.

title: Saratoga, Indiana


text:
Union City, Indiana

Union City is a city in Wayne Township, Randolph County, Indiana, United States. As of the 2020 census, the city had a population of 3,454.

Union City was a stop along the Pittsburgh, Cincinnati, Chicago and St. Louis Railroad, a forerunner of the Pennsylvania Railroad that connected Pittsburgh to Chicago and St. Louis. The Cleveland, Cincinnati, Chicago and St. Louis Railway (also known as the "Big Four", a predecessor of the New York Central) had the east–west route connecting Indianapolis, Anderson and Muncie, Indiana with Sidney, Bellefontaine and Columbus, Ohio. It is believed by some area residents that the town got its nickname "The Hub City" because the two railroads intersected near the Ohio and Indiana state line.

History
Union City was platted in 1849, eleven years after its twin city of Union City, Ohio. A post office has been in operation at Union City, Indiana since 1852.

The William Kerr House, Raphael Kirshbaum Building, Union City Commercial Historic District, Union City Passenger Depot, Union City Public Library, and Union City School are listed on the National Register of Historic Places.

Geography
According to the 2010 census, Union City has a total area of 2.209 sqmi, of which 2.2 sqmi (or 99.59%) is land and 0.009 sqmi (or 0.41%) is water.

2010 census
As of the census of 2010, there were 3,584 people, 1,477 households, and 922 families residing in the city. The population density was 1629.1 PD/sqmi. There were 1,733 housing units at an average density of 787.7 /sqmi. The racial makeup of the city was 87.6% White, 1.1% African American, 0.1% Native American, 0.1% Asian, 9.2% from other races, and 1.9% from two or more races. Hispanic or Latino of any race were 12.8% of the population.

There were 1,477 households, of which 33.0% had children under the age of 18 living with them, 41.4% were married couples living together, 15.2% had a female householder with no husband present, 5.8% had a male householder with no wife present, and 37.6% were non-families. 32.8% of all households were made up of individuals, and 17% had someone living alone who was 65 years of age or older. The average household size was 2.43 and the average family size was 3.04.

The median age in the city was 35.4 years. 28.3% of residents were under the age of 18; 8.7% were between the ages of 18 and 24; 24.7% were from 25 to 44; 21.9% were from 45 to 64; and 16.5% were 65 years of age or older. The gender makeup of the city was 48.6% male and 51.4% female.

2000 census
As of the census of 2000, there were 3,622 people, 1,569 households, and 969 families residing in the city. The population density was 1,995.1 PD/sqmi. There were 1,738 housing units at an average density of 957.3 /sqmi. The racial makeup of the city was 94.04% White, 1.02% African American, 0.17% Native American, 0.22% Asian, 0.08% Pacific Islander, 3.12% from other races, and 1.35% from two or more races. Hispanic or Latino of any race were 4.53% of the population.

There were 1,569 households, of which 29.3% had children under the age of 18 living with them, 46.3% were married couples living together, 11.7% had a female householder with no husband present, and 38.2% were non-families. 34.4% of all households were made up of individuals, and 17.8% had someone living alone who was 65 years of age or older. The average household size was 2.29 and the average family size was 2.91.

In the city, the population was 25.2% under the age of 18, 9.2% from 18 to 24, 25.9% from 25 to 44, 21.5% from 45 to 64, and 18.2% who were 65 years of age or older. The median age was 37 years. For every 100 females, there were 90.4 males. For every 100 females age 18 and over, there were 88.3 males.

The median income for a household in the city was $26,526, and the median income for a family was $34,250. Males had a median income of $27,877 versus $17,850 for females. The per capita income for the city was $13,981. About 14.5% of families and 19.7% of the population were below the poverty line, including 31.4% of those under age 18 and 14.1% of those age 65 or over.

Education
The Randolph Eastern School Corporation serves Union City through two schools: North Side Elementary School and Union City Junior Senior High School. They are housed in adjoining buildings on the northwest side of the town.

The town has a lending library, the Union City Public Library.

Media
UnionCityNow.com is a locally owned online media outlet that focuses on current events and news stories in the community. The site also contains discussion forums focused on Union City history and local genealogy.

KISS TV is a local-access television station run by the school corporation. It broadcasts on the local Time Warner Cable feed, as well as online. The school's DECA chapter also runs two school businesses: "The TeePee", a school store where students and staff can buy snacks during lunch periods, and "Apache Design", a T-shirt printing business where students record and produce orders for school functions and for organizations in the community and surrounding area.

WJYW 88.9 "Joy FM" broadcasts a Contemporary Christian music format.

WTGR 97.5 FM "Tiger Country" is licensed to neighboring Union City, Ohio, with a studio in Greenville and a new studio to be built in Winchester.

Flag
The flag of Union City was adopted on January 20, 2021, by vote of the people of Union City.

The design features a red background with a blue stripe, surrounding a white skinny stripe. The colors represent the United States. On the center is a big white star on the center of the state outlines of Indiana and Ohio, which represent the city's location and ties to both states.

Mayor Chad E. Spence said in January 2021 that he supported the flag's design.

Notable people

 * Donald R. Atkinson, educator and writer
 * Rick Derringer, guitarist and founding member of The McCoys.
 * Isaac P. Gray, 18th and 20th governor of Indiana and Minister to Mexico in the second Cleveland administration.
 * Chris Hawkey, country music artist, producer of the Power Trip Morning Show on KFAN Radio in Minneapolis/St. Paul, MN.
 * Earle Raymond Hedrick, mathematician and vice president of University of California.
 * Randy Jo Hobbs (1948–1993), Bassist and founding member of The McCoys.
 * Henry Jackson (1861–1932), born in Union City, was a major-league baseball player for the Indianapolis Hoosiers in 1887.
 * Haleloke Kahauolopua, a Hawaiian-born singer who retired to Union City.

title: Union City, Indiana


text:
Winchester, Indiana

Winchester is a city in White River Township, Randolph County, in the U.S. state of Indiana. The city is the county seat of Randolph County. The population was 4,935 at the 2010 census. It is the home of Winchester Speedway.

History
Winchester was laid out in 1818 as the county seat. A post office has been in operation at Winchester since 1820.

With the discovery of oil in the late 1800s, Winchester became a center for the glass industry.

The Gen. Asahel Stone Mansion, Winchester Courthouse Square Historic District, and Winchester Residential Historic District are listed on the National Register of Historic Places.

2024 tornado
At 7:58pm on March 14, 2024, a quarter-mile wide EF3 tornado hit Winchester during a severe weather outbreak. Severe damage was dealt to the Goodwill, The Lodge at Summers Pointe, and Strip Mall. The Freedom Life Church and Taco Bell were flattened. Thirty-eight were injured and three were left critically injured; one of the three died 5 weeks later.

As of June 2024, 3 months after the tornado’s touchdown, the remainder of the Strip Mall has been demolished as a result of its ultimate destruction from the tornado; and the Goodwill, Taco Bell and the Lodge at Summers Pointe are currently undergoing construction and repairs.

Geography
According to the 2010 census, Winchester has a total area of 3.343 sqmi, of which 3.33 sqmi (or 99.61%) is land and 0.013 sqmi (or 0.39%) is water.

2010 census
At the 2010 census there were 4,935 people, 2,051 households, and 1,281 families living in the city. The population density was 1482.0 PD/sqmi. There were 2,349 housing units at an average density of 705.4 /sqmi. The racial makeup of the city was 96.1% White, 0.5% African American, 0.5% Native American, 0.5% Asian, 1.3% from other races, and 1.1% from two or more races. Hispanic or Latino of any race were 2.6%.

Of the 2,051 households 30.2% had children under the age of 18 living with them, 43.1% were married couples living together, 14.3% had a female householder with no husband present, 5.0% had a male householder with no wife present, and 37.5% were non-families. 32.2% of households were one person and 14.9% were one person aged 65 or older. The average household size was 2.32 and the average family size was 2.89.

The median age was 40.2 years. 23.6% of residents were under the age of 18; 8.3% were between the ages of 18 and 24; 23.8% were from 25 to 44; 25.4% were from 45 to 64; and 18.9% were 65 or older. The gender makeup of the city was 47.6% male and 52.4% female.

2000 census
At the 2000 census there were 5,037 people, 2,171 households, and 1,350 families living in the city. The population density was 1,623.8 PD/sqmi. There were 2,377 housing units at an average density of 766.3 /sqmi. The racial makeup of the city was 98.31% White, 0.24% African American, 0.26% Native American, 0.30% Asian, 0.32% from other races, and 0.58% from two or more races. Hispanic or Latino of any race were 1.41%.

Of the 2,171 households 27.4% had children under the age of 18 living with them, 48.1% were married couples living together, 10.5% had a female householder with no husband present, and 37.8% were non-families. 33.7% of households were one person, and 16.2% were one person aged 65 or older. The average household size was 2.23 and the average family size was 2.83.

In the city the population was spread out, with 22.2% under the age of 18, 8.9% from 18 to 24, 27.4% from 25 to 44, 22.0% from 45 to 64, and 19.6% 65 or older. The median age was 40 years. For every 100 females, there were 88.7 males. For every 100 females age 18 and over, there were 84.9 males.

The median household income was $28,500 and the median family income was $37,607. Males had a median income of $28,947 versus $22,226 for females. The per capita income for the city was $17,753. About 10.9% of families and 15.0% of the population were below the poverty line, including 20.2% of those under age 18 and 6.9% of those age 65 or over.

Education
The town has a lending library, the Winchester Community Public Library.

Schools, all part of the Randolph Central School Corporation public school district, include:

 * O.R. Baker Elementary School
 * Willard Elementary School
 * Driver Middle School
 * Winchester Community High School

Notable people

 * Thomas McLelland Browne, lawyer, prosecutor, Union Army officer, and politician
 * Harold Falls (1909–2006), ophthalmologic geneticist, was born in Winchester
 * James P. Goodrich, Governor of Indiana, 1917–1921, born in Winchester
 * Randy Jo Hobbs, musician born in Winchester
 * I. Marlene King, creator of the ABC Family original series Pretty Little Liars
 * Greg Leffler, Indy car driver
 * Troy Puckett, pitcher for the Philadelphia Phillies in 1911, born in Winchester
 * James Eli Watson, U. S. Senator
 * Robert Wise, film director, born in Winchester

title: Winchester, Indiana


text:
Holton, Indiana

Holton is a town in Otter Creek Township, Ripley County, in the U.S. state of Indiana. The population was 480 at the 2010 census.

History
Holton was platted in 1854, when the Ohio and Mississippi Railway was extended to that point. The town derived its name from Jesse Holman, the original owner of the town site. A post office has been in operation at Holton since 1854.

On March 2, 2012, an EF3 tornado severely damaged the town of Holton and killed three people.

Geography
According to the 2010 census, Holton has a total area of 1.81 sqmi, all land.

2010 census
As of the census of 2010, there were 480 people, 171 households, and 119 families living in the town. The population density was 265.2 PD/sqmi. There were 199 housing units at an average density of 109.9 /sqmi. The racial makeup of the town was 96.3% White, 1.5% African American, 0.2% Native American, 0.4% from other races, and 1.7% from two or more races. Hispanic or Latino of any race were 0.4% of the population.

There were 171 households, of which 39.8% had children under the age of 18 living with them, 49.7% were married couples living together, 14.6% had a female householder with no husband present, 5.3% had a male householder with no wife present, and 30.4% were non-families. 23.4% of all households were made up of individuals, and 9.3% had someone living alone who was 65 years of age or older. The average household size was 2.81 and the average family size was 3.33.

The median age in the town was 32.5 years. 30.4% of residents were under the age of 18; 8% were between the ages of 18 and 24; 27.3% were from 25 to 44; 22.8% were from 45 to 64; and 11.7% were 65 years of age or older. The gender makeup of the town was 49.8% male and 50.2% female.

2000 census
As of the census of 2000, there were 407 people, 148 households, and 107 families living in the town. The population density was 226.9 PD/sqmi. There were 158 housing units at an average density of 88.1 /sqmi. The racial makeup of the town was 99.26% White, 0.25% Asian, 0.49% from other races. Hispanic or Latino of any race were 1.47% of the population.

There were 148 households, out of which 38.5% had children under the age of 18 living with them, 54.1% were married couples living together, 12.8% had a female householder with no husband present, and 27.7% were non-families. 25.0% of all households were made up of individuals, and 10.8% had someone living alone who was 65 years of age or older. The average household size was 2.75 and the average family size was 3.18.

In the town, the population was spread out, with 31.0% under the age of 18, 9.8% from 18 to 24, 30.0% from 25 to 44, 18.9% from 45 to 64, and 10.3% who were 65 years of age or older. The median age was 33 years. For every 100 females, there were 92.9 males. For every 100 females age 18 and over, there were 91.2 males.

The median income for a household in the town was $28,750, and the median income for a family was $33,438. Males had a median income of $29,219 versus $19,167 for females. The per capita income for the town was $12,357. About 14.5% of families and 22.6% of the population were below the poverty line, including 33.0% of those under age 18 and 10.5% of those age 65 or over.

title: Holton, Indiana


text:
Technical analysis

In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. As a type of active management, it stands in contradiction to much of modern portfolio theory. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results. It is distinguished from fundamental analysis, which considers a company's financial statements, health, and the overall state of the market and economy.

History
The principles of technical analysis are derived from hundreds of years of financial market data. Some aspects of technical analysis began to appear in Amsterdam-based merchant Joseph de la Vega's accounts of the Dutch financial markets in the 17th century. In Asia, technical analysis is said to be a method developed by Homma Munehisa during the early 18th century which evolved into the use of candlestick techniques, and is today a technical analysis charting tool.

Journalist Charles Dow (1851-1902) compiled and closely analyzed American stock market data, and published some of his conclusions in editorials for The Wall Street Journal. He believed patterns and business cycles could possibly be found in this data, a concept later known as "Dow theory". However, Dow himself never advocated using his ideas as a stock trading strategy.

In the 1920s and 1930s, Richard W. Schabacker published several books which continued the work of Charles Dow and William Peter Hamilton in their books Stock Market Theory and Practice and Technical Market Analysis. In 1948, Robert D. Edwards and John Magee published Technical Analysis of Stock Trends which is widely considered to be one of the seminal works of the discipline. It is exclusively concerned with trend analysis and chart patterns and remains in use to the present. Early technical analysis was almost exclusively the analysis of charts because the processing power of computers was not available for the modern degree of statistical analysis. Charles Dow reportedly originated a form of point and figure chart analysis. With the emergence of behavioral finance as a separate discipline in economics, Paul V. Azzopardi combined technical analysis with behavioral finance and coined the term "Behavioral Technical Analysis".

Other pioneers of analysis techniques include Ralph Nelson Elliott, William Delbert Gann, and Richard Wyckoff who developed their respective techniques in the early 20th century.

General description
Fundamental analysts examine earnings, dividends, assets, quality, ratios, new products, research and the like. Technicians employ many methods, tools and techniques as well, one of which is the use of charts. Using charts, technical analysts seek to identify price patterns and market trends in financial markets and attempt to exploit those patterns.

Technicians using charts search for archetypal price chart patterns, such as the well-known head and shoulders or double top/bottom reversal patterns, study technical indicators, moving averages and look for forms such as lines of support, resistance, channels and more obscure formations such as flags, pennants, balance days and cup and handle patterns.

Technical analysts also widely use market indicators of many sorts, some of which are mathematical transformations of price, often including up and down volume, advance/decline data and other inputs. These indicators are used to help assess whether an asset is trending, and if it is, the probability of its direction and of continuation. Technicians also look for relationships between price/volume indices and market indicators. Examples include the moving average, relative strength index and MACD. Other avenues of study include correlations between changes in Options (implied volatility) and put/call ratios with price. Also important are sentiment indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied Volatility, etc.

There are many techniques in technical analysis. Adherents of different techniques (for example: Candlestick analysis, the oldest form of technical analysis developed by a Japanese grain trader; Harmonics; Dow theory; and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one technique. Some technical analysts use subjective judgment to decide which pattern(s) a particular instrument reflects at a given time and what the interpretation of that pattern should be. Others employ a strictly mechanical or systematic approach to pattern identification and interpretation.

Comparison with fundamental analysis
Contrasting with technical analysis is fundamental analysis: the study of economic and other underlying factors that influence the way investors price financial markets. This may include regular corporate metrics like a company's recent EBITDA figures, the estimated impact of recent staffing changes to the board of directors, geopolitical considerations, and even scientific factors like the estimated future effects of global warming. Pure forms of technical analysis can hold that prices already reflect all the underlying fundamental factors. Uncovering future trends is what technical indicators are designed to do, although neither technical nor fundamental indicators are perfect. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.

Comparison with quantitative analysis
The contrast against quantitative analysis is less clear cut than the distinction with fundamental analysis. Some sources treat technical and quantitative analysis as more or less synonymous, while others draw a sharp distinction. For example, quantitative analysis expert Paul Wilmott suggests technical analysis is little more than 'charting' (making forecasts based on extrapolating graphical representations), and that technical analysis rarely has any predictive power.

Principles
A core principle of technical analysis is that a market's price reflects all relevant information impacting that market. A technical analyst therefore looks at the history of a security or commodity's trading pattern rather than external drivers such as economic, fundamental and news events. It is believed that price action tends to repeat itself due to the collective, patterned behavior of investors. Hence technical analysis focuses on identifiable price trends and conditions.

Market action discounts everything
Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is important to understand what investors think of that information, known and perceived.

Prices move in trends
Technical analysts believe that prices trend directionally, i.e., up, down, or sideways (flat) or some combination. The basic definition of a price trend was originally put forward by Dow theory.

An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the "zig-zag" movement in the price. The series of "lower highs" and "lower lows" is a tell tale sign of a stock in a down trend. In other words, each time the stock moved lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.

Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that does not pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.

History tends to repeat itself
Technical analysts believe that investors collectively repeat the behavior of the investors who preceded them. To a technician, the emotions in the market may be irrational, but they exist. Because investor behavior repeats itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart. Recognition of these patterns can allow the technician to select trades that have a higher probability of success.

Technical analysis is not limited to charting, but it always considers price trends. For example, many technicians monitor surveys of investor sentiment. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment. Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). And because most investors are bullish and invested, one assumes that few buyers remain. This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading.

Industry
The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a federation of regional and national organizations. In the United States, the industry is represented by both the CMT Association and the American Association of Professional Technical Analysts (AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). The STA was a founding member of IFTA, has recently celebrated its 50th anniversary and certifies analysts with the Diploma in Technical Analysis. In Canada the industry is represented by the Canadian Society of Technical Analysts. In Australia, the industry is represented by the Australian Technical Analysts Association (ATAA), (which is affiliated to IFTA) and the Australian Professional Technical Analysts (APTA) Inc.

Professional technical analysis societies have worked on creating a body of knowledge that describes the field of Technical Analysis. A body of knowledge is central to the field as a way of defining how and why technical analysis may work. It can then be used by academia, as well as regulatory bodies, in developing proper research and standards for the field. The CMT Association has published a body of knowledge, which is the structure for the Chartered Market Technician (CMT) exam.

Software
Technical analysis software automates the charting, analysis and reporting functions that support technical analysts in their review and prediction of financial markets (e.g. the stock market).

In addition to installable desktop-based software packages in the traditional sense, the industry has seen an emergence of cloud-based applications and application programming interfaces (APIs) that deliver technical indicators (e.g., MACD, Bollinger Bands) via RESTful HTTP or intranet protocols.

Modern technical analysis software is often available as a web or a smartphone application, without the need to download and install a software package. Some of them even offer an integrated programming language and automatic backtesting tools.

Neural networks
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.

While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders.

Backtesting/Hindcasting
Systematic trading is most often employed after testing an investment strategy on historic data. This is known as backtesting (or hindcasting). Backtesting is most often performed for technical indicators combined with volatility but can be applied to most investment strategies (e.g. fundamental analysis). While traditional backtesting was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, backtesting can be performed on entire exchanges over decades of historic data in very short amounts of time.

The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation, and are unsuitable for computer processing. Only technical indicators which are entirely algorithmic can be programmed for computerized automated backtesting.

Combination with other market forecast methods
John Murphy states that the principal sources of information available to technicians are price, volume and open interest. Other data, such as indicators and sentiment analysis, are considered secondary.

However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work. One advocate for this approach is John Bollinger, who coined the term rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis. Another such approach, fusion analysis, overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance.

Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships.

Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts.

Empirical evidence
Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power. Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult. Nonlinear prediction using neural networks occasionally produces statistically significant prediction results. A Federal Reserve working paper regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions", although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".

Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50%."

An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems in 1999; the sample covered by Brock et al. was robust to data snooping.

Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices." Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.

In a paper published in the Journal of Finance, Dr. Andrew W. Lo, director MIT Laboratory for Financial Engineering, working with Harry Mamaysky and Jiang Wang found that:

"Technical analysis, also known as 'charting', has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis – the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution – conditioned on specific technical indicators such as head-and-shoulders or double-bottoms – we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value."

In that same paper Dr. Lo wrote that "several academic studies suggest that ... technical analysis may well be an effective means for extracting useful information from market prices." Some techniques such as Drummond Geometry attempt to overcome the past data bias by projecting support and resistance levels from differing time frames into the near-term future and combining that with reversion to the mean techniques.

Efficient-market hypothesis
The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse."

However, because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices. They also point to research in the field of behavioral finance, specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes. Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis:

"By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies.... cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work."

EMH advocates reply that while individual market participants do not always act rationally (or have complete information), their aggregate decisions balance each other, resulting in a rational outcome (optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium). Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.

Random walk hypothesis
The random walk hypothesis may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements (but not necessarily other public information). In his book A Random Walk Down Wall Street, Princeton economist Burton Malkiel said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future." Malkiel has stated that while momentum may explain some stock price movements, there is not enough momentum to make excess profits. Malkiel has compared technical analysis to "astrology".

In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a 1999 response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH, which is an entirely separate concept from RWH. In a 2000 paper, Andrew Lo back-analyzed data from the U.S. from 1962 to 1996 and found that "several technical indicators do provide incremental information and may have some practical value". Burton Malkiel dismissed the irregularities mentioned by Lo and McKinlay as being too small to profit from.

Technicians say that the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves. Some signal processing researchers negate the random walk hypothesis that stock market prices resemble Wiener processes, because the statistical moments of such processes and real stock data vary significantly with respect to window size and similarity measure. They argue that feature transformations used for the description of audio and biosignals can also be used to predict stock market prices successfully which would contradict the random walk hypothesis.

The random walk index (RWI) is a technical indicator that attempts to determine if a stock's price movement is random in nature or a result of a statistically significant trend. The random walk index attempts to determine when the market is in a strong uptrend or downtrend by measuring price ranges over N and how it differs from what would be expected by a random walk (randomly going up or down). The greater the range suggests a stronger trend.

Applying Kahneman and Tversky's prospect theory to price movements, Paul V. Azzopardi provided a possible explanation why fear makes prices fall sharply while greed pushes up prices gradually. This commonly observed behaviour of securities prices is sharply at odds with random walk. By gauging greed and fear in the market, investors can better formulate long and short portfolio stances.

Scientific technical analysis
Caginalp and Balenovich in 1994 used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions. Some of the patterns such as a triangle continuation or reversal pattern can be generated with the assumption of two distinct groups of investors with different assessments of valuation. The major assumptions of the models are the finiteness of assets and the use of trend as well as valuation in decision making. Many of the patterns follow as mathematically logical consequences of these assumptions.

One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing.

Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend. Caginalp and Laurent were the first to perform a successful large scale test of patterns. A mathematically precise set of criteria were tested by first using a definition of a short-term trend by smoothing the data and allowing for one deviation in the smoothed trend. They then considered eight major three-day candlestick reversal patterns in a non-parametric manner and defined the patterns as a set of inequalities. The results were positive with an overwhelming statistical confidence for each of the patterns using the data set of all S&P 500 stocks daily for the five-year period 1992–1996.

Among the most basic ideas of conventional technical analysis is that a trend, once established, tends to continue. However, testing for this trend has often led researchers to conclude that stocks are a random walk. One study, performed by Poterba and Summers, found a small trend effect that was too small to be of trading value. As Fisher Black noted, "noise" in trading price data makes it difficult to test hypotheses.

One method for avoiding this noise was discovered in 1995 by Caginalp and Constantine who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation. A closed-end fund (unlike an open-end fund) trades independently of its net asset value and its shares cannot be redeemed, but only traded among investors as any other stock on the exchanges. In this study, the authors found that the best estimate of tomorrow's price is not yesterday's price (as the efficient-market hypothesis would indicate), nor is it the pure momentum price (namely, the same relative price change from yesterday to today continues from today to tomorrow). But rather it is almost exactly halfway between the two.

Starting from the characterization of the past time evolution of market prices in terms of price velocity and price acceleration, an attempt towards a general framework for technical analysis has been developed, with the goal of establishing a principled classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time translational invariant properties. The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. Using a renormalisation group approach, the probabilistic based scenario approach exhibits statistically significant predictive power in essentially all tested market phases.

A survey of modern studies by Park and Irwin showed that most found a positive result from technical analysis.

In 2011, Caginalp and DeSantis have used large data sets of closed-end funds, where comparison with valuation is possible, in order to determine quantitatively whether key aspects of technical analysis such as trend and resistance have scientific validity. Using data sets of over 100,000 points they demonstrate that trend has an effect that is at least half as important as valuation. The effects of volume and volatility, which are smaller, are also evident and statistically significant. An important aspect of their work involves the nonlinear effect of trend. Positive trends that occur within approximately 3.7 standard deviations have a positive effect. For stronger uptrends, there is a negative effect on returns, suggesting that profit taking occurs as the magnitude of the uptrend increases. For downtrends the situation is similar except that the "buying on dips" does not take place until the downtrend is a 4.6 standard deviation event. These methods can be used to examine investor behavior and compare the underlying strategies among different asset classes.

In 2013, Kim Man Lui and T Chong pointed out that the past findings on technical analysis mostly reported the profitability of specific trading rules for a given set of historical data. These past studies had not taken the human trader into consideration as no real-world trader would mechanically adopt signals from any technical analysis method. Therefore, to unveil the truth of technical analysis, we should get back to understand the performance between experienced and novice traders. If the market really walks randomly, there will be no difference between these two kinds of traders. However, it is found by experiment that traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.

Ticker-tape reading
Until the mid-1960s, tape reading was a popular form of technical analysis. It consisted of reading market information such as price, volume, order size, and so on from a paper strip which ran through a machine called a stock ticker. Market data was sent to brokerage houses and to the homes and offices of the most active speculators. This system fell into disuse with the advent of electronic information panels in the late 60's, and later computers, which allow for the easy preparation of charts.

Jesse Livermore, one of the most successful stock market operators of all time, was primarily concerned with ticker tape reading since a young age. He followed his own (mechanical) trading system (he called it the 'market key'), which did not need charts, but was relying solely on price data. He described his market key in detail in his 1940s book 'How to Trade in Stocks'. Livermore's system was determining market phases (trend, correction etc.) via past price data. He also made use of volume data (which he estimated from how stocks behaved and via 'market testing', a process of testing market liquidity via sending in small market orders), as described in his 1940s book.

Quotation board
Another form of technical analysis used so far was via interpretation of stock market data contained in quotation boards, that in the times before electronic screens, were huge chalkboards located in the stock exchanges, with data of the main financial assets listed on exchanges for analysis of their movements. It was manually updated with chalk, with the updates regarding some of these data being transmitted to environments outside of exchanges (such as brokerage houses, bucket shops, etc.) via the aforementioned tape, telegraph, telephone and later telex.

This analysis tool was used both, on the spot, mainly by market professionals, as well as by general public through the printed versions in newspapers showing the data of the negotiations of the previous day, for swing and position trades.

Concepts

 * Average true range – averaged daily trading range, adjusted for price gaps.
 * Breakout – the concept whereby prices forcefully penetrate an area of prior support or resistance, usually, but not always, accompanied by an increase in volume.
 * Chart pattern – distinctive pattern created by the movement of security or commodity prices on a chart
 * Cycles – time targets for potential change in price action (price only moves up, down, or sideways)
 * Dead cat bounce – the phenomenon whereby a spectacular decline in the price of a stock is immediately followed by a moderate and temporary rise before resuming its downward movement
 * Elliott wave principle and the golden ratio to calculate successive price movements and retracements
 * Fibonacci ratios – used as a guide to determine support and resistance and retracement percentages
 * Momentum – the rate of price change
 * Point and figure analysis – A priced-based analytical approach employing numerical filters which may incorporate time references, though ignores time entirely in its construction
 * Resistance – a price level that may prompt a net increase of selling activity
 * Support – a price level that may prompt a net increase of buying activity
 * Trending – the phenomenon by which price movement tends to persist in one direction for an extended period of time

Types of charts

 * Candlestick chart – Of Japanese origin and similar to OHLC, candlesticks widen and fill the interval between the open and close prices to emphasize the open/close relationship. In the West, often black or red candle bodies represent a close lower than the open, while white, green or blue candles represent a close higher than the open price.
 * Line chart – Connects the closing price values with line segments. You can also choose to draw the line chart using open, high or low price.
 * Open-high-low-close chart – OHLC charts, also known as bar charts, plot the span between the high and low prices of a trading period as a vertical line segment at the trading time, and the open and close prices with horizontal tick marks on the range line, usually a tick to the left for the open price and a tick to the right for the closing price.
 * Point and figure chart – a chart type employing numerical filters with only passing references to time, and which ignores time entirely in its construction.

Overlays
Overlays are generally superimposed over the main price chart.
 * Bollinger bands – a range of price volatility
 * Channel – a pair of parallel trend lines
 * Ichimoku kinko hyo – a moving average-based system that factors in time and the average point between a candle's high and low
 * Moving average – an average over a window of time before and after a given time point that is repeated at each time point in the given chart. A moving average can be thought of as a kind of dynamic trend-line.
 * Parabolic SAR – Wilder's trailing stop based on prices tending to stay within a parabolic curve during a strong trend
 * Pivot point – derived by calculating the numerical average of a particular currency's or stock's high, low and closing prices
 * Resistance – a price level that may act as a ceiling above price
 * Support – a price level that may act as a floor below price
 * Trend line – a sloping line described by at least two peaks or two troughs
 * Zig Zag – This chart overlay that shows filtered price movements that are greater than a given percentage.

Breadth indicators
These indicators are based on statistics derived from the broad market.
 * Advance–decline line – a popular indicator of market breadth.
 * McClellan Oscillator – a popular closed-form indicator of breadth.
 * McClellan Summation Index – a popular open-form indicator of breadth.

Price-based indicators
These indicators are generally shown below or above the main price chart.
 * Average directional index – a widely used indicator of trend strength.
 * Commodity channel index – identifies cyclical trends.
 * MACD – moving average convergence/divergence.
 * Momentum – the rate of price change.
 * Relative strength index (RSI) – oscillator showing price strength.
 * Relative Vigor Index (RVI) – oscillator measures the conviction of a recent price action and the likelihood that it will continue.
 * Stochastic oscillator – close position within recent trading range.
 * Trix – an oscillator showing the slope of a triple-smoothed exponential moving average.
 * Vortex Indicator – an indicator used to identify the existence, continuation, initiation or termination of trends.

Volume-based indicators

 * Accumulation/distribution index – based on the close within the day's range.
 * Money flow index – the amount of stock traded on days the price went up.
 * On-balance volume – the momentum of buying and selling stocks.

Trading with Mixing Indicators

 * MACD & Average directional index
 * MACD & Super Trend
 * MACD & Moving average
 * MACD & RSI
 * MACD & Moving Averages

title: Technical analysis


text:
Milan, Indiana

Milan is a town in Franklin and Washington townships, Ripley County, in the U.S. state of Indiana. The population was 1,899 at the 2010 census.

Milan High School won the Indiana state basketball championship against Muncie Central High School in 1954, the victory being significant as Milan was one of the smallest towns to win a state championship in the United States at that time. The 1986 film Hoosiers is based on the story of the 1954 Milan Team.

While it is often claimed that Milan is the subject of a volume of poetry, titled ''Pop. 359,'' written in 1941 by Carl Wilson under the pseudonym of Tramp Starr, that book is actually about the nearby town of Moores Hill.

History
Milan was laid out in 1854 when the railroad was extended to that point. The town, like its predecessor Old Milan, was named after Milan in Italy.

Milan Masonic Lodge No. 31 was listed on the National Register of Historic Places in 2013.

Geography
According to the 2010 census, Milan has a total area of 1.98 sqmi, of which 1.95 sqmi (or 98.48%) is land and 0.03 sqmi (or 1.52%) is water.

2010 census
As of the 2010 census, there were 1,899 people, 706 households and 476 families living in the town. The population density was 973.8 /sqmi. There were 795 housing units at an average density of 407.7 /sqmi. The racial make-up of the town was 97.7% White, 0.3% African American, 0.2% Native American, 0.1% Asian, 0.3% from other races and 1.4% from two or more races. Hispanic or Latino of any race were 0.8% of the population.

There were 706 households, of which 38.7% had children under the age of 18 living with them, 44.2% were married couples living together, 16.9% had a female householder with no husband present, 6.4% had a male householder with no wife present, and 32.6% were non-families. 28.3% of all households were made up of individuals, and 14.7% had someone living alone who was 65 years of age or older. The average household size was 2.53 and the average family size was 3.06.

The median age was 36.7 years. 28.1% of residents were under the age of 18, 6.9% were between the ages of 18 and 24, 25.7% were from 25 to 44, 21.5% were from 45 to 64 and 17.9% were 65 years of age or older. The sex make-up of the town was 46.6% male and 53.4% female.

2000 census
At the 2000 census, there were 1,816 people, 693 households and 460 families living in the town. The population density was 955.2 /sqmi. There were 768 housing units at an average density of 404.0 /sqmi. The racial make-up of the town was 98.51% White, 0.22% African American, 0.28% Native American, 0.33% Asian and 0.66% from two or more races. Hispanic or Latino of any race were 0.50% of the population.

There were 693 households, of which 33.3% had children under the age of 18 living with them, 50.5% were married couples living together, 10.2% had a female householder with no husband present, and 33.6% were non-families. 28.7% of all households were made up of individuals, and 16.5% had someone living alone who was 65 years of age or older. The average household size was 2.52 and the average family size was 3.13.

27.1% of the population were under the age of 18, 8.9% from 18 to 24, 28.3% from 25 to 44, 18.8% from 45 to 64 and 17.0% were 65 years of age or older. The median age was 34 years. For every 100 females, there were 91.2 males. For every 100 females age 18 and over, there were 85.4 males.

The median household income was $36,066 and the median family income was $41,435. Males had a median income of $31,512 and females $22,244. The per capita income was $16,191. About 7.9% of families and 9.0% of the population were below the poverty line, including 11.4% of those under age 18 and 9.0% of those age 65 or over.

Education
Milan Community School Corporation has three schools - Milan Elementary, Milan Middle and Milan High School. The corporation was a 2006 Indiana Department of Education Exemplary Progress School Corporation.

Milan has a public library, a branch of the Osgood Public Library.

Notable people

 * Jamie Johnson - member of the Grascals
 * William Jordan - actor and member of the 1954 Milan High School basketball team.
 * Bobby Plump - member of the 1954 Milan High School basketball team; author; restaurateur.
 * Gene White - member of the 1954 Milan High School Indiana State Championship basketball team.

title: Milan, Indiana


text:
Talk:Love Hina

Added Love Hina timeline
I hope everyone will help fill in the missing/erroneous details! —Preceding unsigned comment added by ZhangKhaiEn (talk • contribs) 04:27, December 27, 2005

Digital production?
I read somewhere that Love Hina was the first anime TV series produced soley in digital form- not using cameras or painted cels or any other methods used in traditional animation. —Preceding unsigned comment added by Bizzybody (talk • contribs) 02:53, June 30, 2007

External links modified
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About the influence of this manga series
Why this article can be a GA without mentioning its influence to manga, especially to a manga genre (harem)? Love Hina is considered as the origin of harem manga and anime, especially romantic comedy. -Hijk910 (talk) 16:54, 6 July 2020 (UTC)

Maybe because the GAN was in 2009? -Hijk910 (talk) 16:55, 6 July 2020 (UTC)

title: Talk:Love Hina


text:
Napoleon, Indiana

Napoleon is a town in Jackson Township, Ripley County, in the U.S. state of Indiana. The population was 234 at the 2010 census.

History
Napoleon was laid out in 1820. The town was named in honor of Napoleon (1769–1821), a French General and Emperor. A post office has been in operation at Napoleon since 1821.

The Central House and Elias Conwell House are listed on the National Register of Historic Places.

Geography
According to the 2010 census, Napoleon has a total area of 0.2 sqmi, all land.

2010 census
As of the census of 2010, there were 234 people, 111 households, and 66 families living in the town. The population density was 1170.0 PD/sqmi. There were 126 housing units at an average density of 630.0 /sqmi. The racial makeup of the town was 100.0% White. Hispanic or Latino of any race were 1.3% of the population.

There were 111 households, of which 23.4% had children under the age of 18 living with them, 44.1% were married couples living together, 7.2% had a female householder with no husband present, 8.1% had a male householder with no wife present, and 40.5% were non-families. 38.7% of all households were made up of individuals, and 16.2% had someone living alone who was 65 years of age or older. The average household size was 2.11 and the average family size was 2.70. The median age in the town was 45.3 years. 17.5% of residents were under the age of 18; 8.6% were between the ages of 18 and 24; 23% were from 25 to 44; 34.2% were from 45 to 64; and 16.7% were 65 years of age or older. The gender makeup of the town was 50.9% male and 49.1% female.

2000 census
As of the census of 2000, there were 238 people, 93 households, and 74 families living in the town. The population density was 1,266.1 PD/sqmi. There were 103 housing units at an average density of 547.9 /sqmi. The racial makeup of the town was 98.32% White, 1.26% Native American, and 0.42% from two or more races. Hispanic or Latino of any race were 2.10% of the population.

There were 93 households, out of which 34.4% had children under the age of 18 living with them, 62.4% were married couples living together, 14.0% had a female householder with no husband present, and 19.4% were non-families. 17.2% of all households were made up of individuals, and 8.6% had someone living alone who was 65 years of age or older. The average household size was 2.56 and the average family size was 2.88.

In the town, the population was spread out, with 27.3% under the age of 18, 6.7% from 18 to 24, 31.1% from 25 to 44, 19.3% from 45 to 64, and 15.5% who were 65 years of age or older. The median age was 38 years. For every 100 females, there were 110.6 males. For every 100 females age 18 and over, there were 98.9 males.

The median income for a household in the town was $39,844, and the median income for a family was $43,295. Males had a median income of $30,625 versus $28,750 for females. The per capita income for the town was $16,187. About 5.5% of families and 11.3% of the population were below the poverty line, including 21.3% of those under the age of eighteen and 4.3% of those 65 or over.

Education
Napoleon residents may obtain a free library card from the Osgood Public Library Central Library in Osgood, or its branch in Milan.

title: Napoleon, Indiana


text:
Osgood, Indiana

Osgood is a town in Center Township, Ripley County, in the U.S. state of Indiana. The population was 1,624 at the 2010 census.

History
Osgood was platted in 1854 when the railroad was extended to that point. The town was named for A. L. Osgood, a railroad official. A post office called Osgood has been in operation since 1855. In the 1890s, Osgood was a sundown town, where African Americans were not allowed to reside. By then, Osgood was still much more closely connected to Cincinnati than Indianapolis in terms of trade.

In 1999, the community received a $23 million bequest from the Gilmore and Golda Reynolds Foundation, which was established by two lifelong Osgood residents to assist the town government as well as local non-profit organizations.

Geography
According to the 2010 census, Osgood has a total area of 1.48 sqmi, of which 1.45 sqmi (or 97.97%) is land and 0.03 sqmi (or 2.03%) is water.

2010 census
As of the census of 2010, there were 1,624 people, 638 households, and 417 families living in the town. The population density was 1120.0 PD/sqmi. There were 728 housing units at an average density of 502.1 /sqmi. The racial makeup of the town was 98.2% White, 0.1% African American, 0.2% Native American, 0.1% from other races, and 1.5% from two or more races. Hispanic or Latino of any race were 1.3% of the population.

There were 638 households, of which 36.2% had children under the age of 18 living with them, 44.7% were married couples living together, 15.2% had a female householder with no husband present, 5.5% had a male householder with no wife present, and 34.6% were non-families. 31.2% of all households were made up of individuals, and 14% had someone living alone who was 65 years of age or older. The average household size was 2.45 and the average family size was 3.06.

The median age in the town was 36 years. 26.9% of residents were under the age of 18; 8.5% were between the ages of 18 and 24; 26% were from 25 to 44; 22.3% were from 45 to 64; and 16.4% were 65 years of age or older. The gender makeup of the town was 47.5% male and 52.5% female.

2000 census
As of the census of 2000, there were 1,669 people, 668 households, and 424 families living in the town. The population density was 1,285.0 PD/sqmi. There were 731 housing units at an average density of 562.8 /sqmi. The racial makeup of the town was 98.98% White, 0.12% African American, 0.54% Native American, 0.12% Asian, 0.06% from other races, and 0.18% from two or more races. Hispanic or Latino of any race were 0.96% of the population.

There were 668 households, out of which 34.0% had children under the age of 18 living with them, 47.2% were married couples living together, 12.3% had a female householder with no husband present, and 36.4% were non-families. 31.3% of all households were made up of individuals, and 16.5% had someone living alone who was 65 years of age or older. The average household size was 2.41 and the average family size was 3.03.

In the town, the population was spread out, with 27.4% under the age of 18, 8.0% from 18 to 24, 26.4% from 25 to 44, 19.7% from 45 to 64, and 18.6% who were 65 years of age or older. The median age was 36 years. For every 100 females, there were 89.7 males. For every 100 females age 18 and over, there were 83.4 males.

The median income for a household in the town was $29,659, and the median income for a family was $35,750. Males had a median income of $29,375 versus $20,938 for females. The per capita income for the town was $13,842. About 9.2% of families and 11.0% of the population were below the poverty line, including 10.4% of those under age 18 and 17.2% of those age 65 or over.

Education
The town has a lending library, the Osgood Public Library.

Notable people

 * Jasper S. Bilby, surveyor
 * Grover Hartley, baseball player and umpire

title: Osgood, Indiana


text:
Sunman, Indiana

Sunman is a town in Adams Township, Ripley County, in the U.S. state of Indiana. The population was 1,049 at the 2010 census.

History
A post office has been in operation at Sunman since 1833. John Sunman, an early postmaster, gave the place his name. Sunman began as a town in 1856 when the railroad was extended to that point.

Geography
According to the 2010 census, Sunman has a total area of 1.17 sqmi, all land.

Climate
The climate in this area is characterized by hot, humid summers and generally mild to cool winters. According to the Köppen Climate Classification system, Sunman has a humid subtropical climate, abbreviated "Cfa" on climate maps.

2010 census
At the 2010 census, there were 1,049 people, 371 households and 272 families living in the town. The population density was 896.6 /sqmi. There were 413 housing units at an average density of 353.0 /sqmi. The racial make-up of the town was 95.1% White, 0.6% African American, 0.2% Asian, 2.7% from other races and 1.4% from two or more races. Hispanic or Latino of any race were 7.9% of the population.

There were 371 households, of which 42.6% had children under the age of 18 living with them, 53.6% were married couples living together, 12.7% had a female householder with no husband present, 7.0% had a male householder with no wife present and 26.7% were non-families. 22.6% of all households were made up of individuals and 11.3% had someone living alone who was 65 years of age or older. The average household size was 2.83 and the average family size was 3.27.

The median age was 32 years. 30% of residents were under the age of 18, 10.8% were between the ages of 18 and 24, 26.7% were from 25 to 44, 20.5% were from 45 to 64 and 11.9% were 65 years of age or older. The sex make-up of the town was 51.2% male and 48.8% female.

2000 census
At the 2000 census, there were 805 people, 319 households and 210 families living in the town. The population density was 793.8 /sqmi. There were 345 housing units at an average density of 340.2 /sqmi. The racial make-up of the town was 93.79% White, 0.37% Native American, 5.59% from other races and 0.25% from two or more races. Hispanic or Latino of any race were 5.96% of the population.

There were 319 households, of which 35.4% had children under the age of 18 living with them, 47.3% were married couples living together, 11.9% had a female householder with no husband present and 33.9% were non-families. 28.5% of all households were made up of individuals and 15.0% had someone living alone who was 65 years of age or older. The average household size was 2.52 and the average family size was 3.05.

29.1% of the population were under the age of 18, 10.6% from 18 to 24, 30.8% from 25 to 44, 15.5% from 45 to 64 and 14.0% were 65 years of age or older. The median age was 31 years. For every 100 females, there were 93.5 males. For every 100 females age 18 and over, there were 94.2 males.

The median household income was $33,819 and the median family income was $44,167. Males had a median income of $30,179 and females $24,107. The per capita income was $14,828. About 6.6% of families and 9.6% of the population were below the poverty line, including 9.3% of those under age 18 and 16.7% of those age 65 or over.

title: Sunman, Indiana


text:
Versailles, Indiana

Versailles is a town in Johnson Township, Ripley County, in the U.S. state of Indiana. The population was 2,113 at the 2010 census. The town is the county seat of Ripley County.

History
It was decided in 1818 a county seat should be located at Versailles. The town of Versailles was laid out in 1819. It was named after the Palace of Versailles.

A post office has been in operation at Versailles since 1823.

The Ripley County Courthouse, Fernando G. Taylor House, Tyson United Methodist Church, and Versailles School and Tyson Auditorium are listed on the National Register of Historic Places.

Geography
According to the 2010 census, Versailles has a total area of 1.51 sqmi, all land. Laughery Creek is the major stream in the area, and was dammed in 1954 to form 230-acre Versailles Lake, within the Versailles State Park.

2010 census
As of the census of 2010, there were 2,113 people, 874 households, and 538 families living in the town. The population density was 1399.3 PD/sqmi. There were 999 housing units at an average density of 661.6 /sqmi. The racial makeup of the town was 98.2% White, 0.2% African American, 0.3% Asian, 0.1% from other races, and 1.2% from two or more races. Hispanic or Latino of any race were 0.4% of the population.

There were 874 households, of which 31.0% had children under the age of 18 living with them, 42.6% were married couples living together, 14.4% had a female householder with no husband present, 4.6% had a male householder with no wife present, and 38.4% were non-families. 33.3% of all households were made up of individuals, and 17.3% had someone living alone who was 65 years of age or older. The average household size was 2.32 and the average family size was 2.95.

The median age in the town was 37.9 years. 23.6% of residents were under the age of 18; 10.6% were between the ages of 18 and 24; 24.5% were from 25 to 44; 25.4% were from 45 to 64; and 15.9% were 65 years of age or older. The gender makeup of the town was 47.5% male and 52.5% female.

2000 census
As of the census of 2000, there were 1,784 people, 746 households, and 492 families living in the town. The population density was 1,168.9 PD/sqmi. There were 803 housing units at an average density of 526.1 /sqmi. The racial makeup of the town was 98.82% White, 0.06% African American, 0.45% Native American, 0.17% Asian, 0.11% from other races, and 0.39% from two or more races. Hispanic or Latino of any race were 0.50% of the population.

There were 746 households, out of which 31.6% had children under the age of 18 living with them, 50.4% were married couples living together, 12.5% had a female householder with no husband present, and 34.0% were non-families. 31.5% of all households were made up of individuals, and 16.1% had someone living alone who was 65 years of age or older. The average household size was 2.36 and the average family size was 2.93.

In the town, the population was spread out, with 25.7% under the age of 18, 10.1% from 18 to 24, 26.2% from 25 to 44, 22.2% from 45 to 64, and 15.8% who were 65 years of age or older. The median age was 36 years. For every 100 females, there were 83.9 males. For every 100 females age 18 and over, there were 84.0 males.

The median income for a household in the town was $35,144, and the median income for a family was $41,442. Males had a median income of $31,607 versus $22,237 for females. The per capita income for the town was $17,352. About 8.3% of families and 8.7% of the population were below the poverty line, including 9.3% of those under age 18 and 6.8% of those age 65 or over.

Education
Most students in Versailles attend South Ripley Elementary School for grades K through 6 and South Ripley Junior/Senior High School for grades 7 through 12.

The town has a lending library, the Tyson Public Library.

Notable people

 * Amos W. Jackson (1904-1972), judge and resident of Versailles
 * Rowland H. Jackson (1872-1957), politician and resident of Versailles
 * Harry Wheeler, 19th Century baseball player

title: Versailles, Indiana