# Progression Analysis Aj Davis Department Stores

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AJ Davis Department Stores | Regression and Correlation Analysis | | Ryan McKinney 8/25/2013 | 1 - Scatterplot of Income (\$1000) vs. Credit Balance (\$) 2 - Line of Best Fit Income = -3.516 + 0.01193x 3 - Coefficient of correlation = .8005 This is a positive correlation because its value is close to 1. It is a good fit for the independent variable in question. 4 - Coefficient of determination √.8005 = .6408 Here the independent variable explains 64.08% of the independent variable. This figure provides for a decent regression model of the independent variable in question. 5 – Utility Test α = .05 Analysis of Variance Source DF SS MS F P Regression 1 27269711 27269711 85.65 0.000 Error 48 15283352 318403 Total 49 42553062 Ho: the regression model is not significant vs. Ha: the regression model is significant. The test statistic is F=85.65 and the p-value is close to 0. Since the p-value is less than 0.025, we reject Ho. We can conclude that the regression model is significant. 6 – Opinion It is my opinion that using the customer’s credit balance is a good way to predict their annual income. Based off the data from the model we can see that as the credit balance increases so does the income level of the customer. The higher the income level the more the customer is willing to buy items on credit because they have the financial means to pay the minimum monthly premiums. 7 - 95% confidence interval for beta-1 0.011926-2.010(0.001289) = 0.00934 0.011926+2.010(0.001289) = 0.01452 95% CI = (0.00934, 0.01452) 8/9 – CI and PI for \$4,000 credit balance Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 44.19 1.21 (41.77, 46.61) (27.11, 61.27) Values of Predictors for New Observations