In the above study, we are interested in determining the relationship between the home ownership rates and per capita incomes for all the 50 U.S states and districts of Columbia. The data were corrected in the district city of Columbia. A relationship between the dependent and independent variable can be determined by taking the correlation analysis. This can be achieved by either determining the Pearson’s correlation coefficient for parametric analysis or the Spearman rank correlation coefficient for the non-parametric test. In this study, we have decided to run the two correlation analysis; the Pearson’s correlation coefficient for the parameters and the Spearman’s rank correlation coefficient. In the study, the values of the correlation coefficient for both Pearson’s correlation coefficient and Spearman’s rank correlation, the t-test statistics for Pearson correlation coefficient and the Spearman rank correlation coefficient.
The null hypothesis: there is no significant relationship between per capita incomes and home ownership rates
The alternative hypothesis: there is a significant relationship between per capita incomes and home ownership rates
Several of the researches have shown that per capita income were correlated with wealth generated (e.g., Loovis et al., 2003; Dintiman & Ward, 2003). However, the per capita income usually affects the home ownership directly and the economy of the country determines the income per capita, Kinnunen et al. (2001) indicated that the correlations between income per capital measurements and home ownership were low.
Results and anaysis
Variable N Mean SE Mean StDev Q1 Median Q3
Homeownership Rate 1997 51 67.149 0.917 6.549 64.100 68.100 71.300
Income Per Capita 51 21856 500 3573 19030 21350 23707
The test of normality of the data should be tested before the correlation analysis on the data.