Model 1: Poverty Rates
Data Overview
We have provided with a data set which contains information regarding poverty rates in 58 counties in California. This dataset explains the relationship and significance of Poverty Rates based on the percentage of urban population, family size, unemployment rate, high school education, college education and family income. The summary of Poverty Rates implies that the percentage of families with income below poverty line which ranges from 3% to 21%, with a mean of 9.90% and standard deviation of 4.00%. The percentage of urban population ranges from 2.7% to 94.3%, with a mean of 34% and standard deviation of 19.5%. The variable Family size is expressed in terms of persons per household which ranges from 2 to 3 persons, with a mean of 3 and standard deviation of 0.24. The unemployment rate ranges from 4% to 21.3%, with a mean of 10% and standard deviation of 4%.   The variable highschl implies that the percentage population (25+) with only high school education ranges from 43% to 68.5%, with a mean of 57.6% and standard deviation of 6.21%. The variable college implies that the percentage population (25+) with 4+ college education ranges from 9% to 44%, with a mean of 19% and standard deviation of 8%.   The median family income ranges from \$24,364 to \$59,147, with a mean of \$35,338 and standard deviation of \$8,264.

Before running a model, we should know whether the correlations are significant among the variables. According to the nature of the variables in our dataset, we found that all the variables are continuous. Therefore, we use pair wise correlation to interpret the relationship between Poverty Rates and other variables. The relationship between poverty rates and percentage of urban population is not significant (r= -0.0277; p≥ 0.05). There is a significant positive relationship between poverty rates and family size (r = 0.3662; p ≤ 0.05). There is a highly significant positive correlation between poverty rates and... [continues]

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