Lower Counties

(noun)

Another term for Delaware Colony in the North American Middle Colonies from 1682 until 1776.

Related Terms

Examples of Lower Counties in the following topics:

  • Explanatory and response variables

    • Consider the following question from page 7 for the county data set:
    • Is federal spending, on average, higher or lower in counties with high rates of poverty?
    • If we suspect poverty might affect spending in a county, then poverty is the explanatory variable and federal spending is the response variable in the relationship.
    • If home-ownership is lower than the national average in one county, will the percent of multi-unit structures in that county likely be above or below the national average?

  • Relationships between variables

    • Is federal spending, on average, higher or lower in counties with high rates of poverty?
    • If homeownership is lower than the national average in one county, will the percent of multi-unit structures in that county likely be above or below the national average?
    • For instance, the highlighted dot corresponds to County 1088 in the county data set: Owsley County, Kentucky, which had a poverty rate of 41.5% and federal spending of $21.50 per capita.
    • It appears that the larger the fraction of units in multi-unit structures, the lower the home-ownership rate.
    • Because there is a downward trend in Figure 1.9 - counties with more units in multi-unit structures are associated with lower home-ownership - these variables are said to be negatively associated.

  • The Powers of Local Government

    • Some states have their counties further divided into townships.
    • Counties form the first-tier administrative division of the states.
    • All the states are divided into counties for administrative purposes.
    • In areas lacking a county government, services are provided either by lower level townships or municipalities or the state.
    • In some states, a city can become independent of any separately functioning county government and function both as a county and as a city.

  • Mapping data (special topic)

    • The county data set offers many numerical variables that we could plot using dot plots, scatterplots, or box plots, but these miss the true nature of the data.
    • Rather, when we encounter geographic data, we should map it using an intensity map, where colors are used to show higher and lower values of a variable.
    • There are also seemingly random counties with very high federal spending relative to their neighbors.
    • If we did not cap the federal spending range at $18 per capita, we would actually find that some counties have extremely high federal spending while there is almost no federal spending in the neighboring counties.
    • These high-spending counties might contain military bases, companies with large government contracts, or other government facilities with many employees.

  • Suburbanization

    • For many of the families that fled the city in favor of the suburbs, the catalyst was the perception of racially diverse urban areas as lower-class and crime-ridden.
    • Louis is a city surrounded by suburbs that are clumped together as the county of St.
    • Louis County developed as whites fled the city for the suburbs.
    • Louis County still reflect the racial component of the county's origins.
    • Louis County is 70.3 percent Caucasian, 23.3 percent African American, 3.5 percent Asian, 2.5 percent Hispanic, and 0.03 percent Pacific Islander.

  • Practice 1: Goodness-of-Fit Test

    • The cumulative number of AIDS cases reported for Santa Clara County is broken down by ethnicity as follows: (Source: HIV/AIDS Epidemiology Santa Clara County, Santa Clara County Public Health Department, May 2011)
    • The percentage of each ethnic group in Santa Clara County is as follows:
    • If the ethnicity of AIDS victims followed the ethnicity of the total county population, fill in the expected number of cases per ethnic group.
    • Perform a goodness-of-fit test to determine whether the make-up of AIDS cases follows the ethnicity of the general population of Santa Clara County.
    • Does it appear that the pattern of AIDS cases in Santa Clara County corresponds to the distribution of ethnic groups in this county?

  • Range Wars

    • Famous range wars included the Lincoln County War, the Pleasant Valley War, the Mason County War, and the Johnson County Range War.
    • The Lincoln County Range War arose between two factions over the control of dry goods trade in the county.
    • Meanwhile, young newcomers to the county, John Tunstall and Alexander McSween, opened a competing store in 1876.
    • The Mason County War (1875–1876) was a cattle rustling dispute between German-American settlers and the non-German ranchers in Mason County, Texas.
    • The Johnson County War, also known as the War on Powder River and the Wyoming Range War, was a range war that took place in Johnson, Natrona and Converse County, Wyoming in April 1892.

  • Socioeconomic and Racial Demographics

    • Cultural factors contribute to the lower levels of Asian American and Pacific Islander voting; for example, some are recent immigrants who still maintain strong ties to their ethnic culture.
    • County-by-county and district-by-district maps reveal that the "true" nature of geographical division, ideologically, is between urban areas/inner suburbs and suburbs/rural areas.
    • For example, in the 2008 elections, even in "solidly blue" states, the majority of voters in most rural counties voted for Republican John McCain, with some exceptions.
    • In "solidly red" states, a majority of voters in most urban counties voted for Democrat Barack Obama.

  • Introduction to reuse, repair, remanufacturing and recycling

    • Since the bottles are reused 100 times before being replaced, the school's waste has been reduced by 700,000 milk cartons per year, which dramatically lowered the school's disposal and purchasing costs.
    • Further west, in Minnesota, the Itasca County Road and Bridge Department replaced the disposable air filters in its garages with reusable filters.

  • Comparing numerical data across groups

    • We will take a look again at the county data set and compare the median household income for counties that gained population from 2000 to 2010 versus counties that had no gain.
    • There were 2,041 counties where the population increased from 2000 to 2010, and there were 1,099 counties with no gain (all but one were a loss).
    • Use the plots in Figure 1.43 to compare the incomes for counties across the two groups.
    • The counties with population gains tend to have higher income (median of about $45,000) versus counties without a gain (median of about $40,000).
    • Median incomes from a random sample of 50 counties that had no population gain are shown on the right.