This case study included information on a sample of fifty credit card accounts. This information, table one, included household size, annual income, and the amount charged to the account. Scatter plots of the data were produced. Figure one shows household size vs. amount charged. This graph shows that the positive linear relationship of the data is somewhat strong. The r squared is 0.56, analyzing the graph there is a correlation of household size to amount charged, but there is a range per household size.
Figure two shows annual income vs. amount charged. The linear relationship of the data is weak, with an r squared of 0.398. Though a positive linear relationship is present.
The last scatter plot, Figure 3, shows household size vs. annual income. This graph shows that there is no correlation at all between these two factors. Making the factors independent of each other and viable for use in multiple regression.
Frequency tables and plots of annual incomes and household size from the sample were also constructed. Figure four plots the frequency of household size. From this plot we can see that a household size of 2 is most common, with 30 percent of the entire sample. Table 2 shows a breakdown of the frequencies.
Figure five is a plot of the frequency of household incomes separated by $5000 steps. We can see that the incomes of the sample are close to evenly distributed with peaks at 30-34 and 50-54. Table three shows the frequencies and percents of household incomes from the sample.
Regressions of the data were also performed. First a regression with the annual income as the independent variable and the amount charged dependent . With this regression an estimated regression equation is formed, Y=2203.999 + 40.479(income). This equation shows that there is a positive relationship between the amount charged and annual income. The p value is very low, 0.0000009012, well under the significance alpha of 0.05, and F. This means...
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