2020 Words9 Pages

August 26, 2012
MATH 533
Course Project Part C
Professor Khago
Introduction:
The following report displays regression and correlation analysis for AJ Davis Department Stores data on credit balance and size. We will use the data collected from 50 credit customers to complete the following analysis; * Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret. * Determine the equation of the "best fit" line, which describes the relationship between CREDIT BALANCE and SIZE. * Determine the coefficient of correlation. Interpret. * Determine the coefficient of determination. Interpret. * Test the utility of this regression model (use a two tail test with α =.05).*…show more content…*

The p-value is less than .05 so we would reject the null hypothesis. 6. Based on findings from questions 1-5 we can conclude that the relationship between credit balance and size is linear with positive correlation. That is model is useful for predicting the credit balance using the variable size as a independent variable. 7. 95% confidence interval. (300.788, 505.655). This means that we can say with 95% confidence that the true value of the slope will be between (300.788, 505.655) 8. The 95% confidence interval for the mean credit balance for a customer that has a household size of 5 is (4368.2, 4846.9). The predicted value of the credit balance of a customer that has a household size of 5 is $4607.5. This confidence interval means that with 95% confidence, we can say that the true value of the mean credit balance for a customer that has a household size of 5 will be within the interval ($4368.2, $4846.9) Taken from minitab: Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 4607.5 119.0 (4368.2, 4846.9) (3337.9, 5877.2) Values of Predictors for New Observations New Obs Size 1 5.00 9. The 95% confidence interval for the mean credit balance for a customer that has a household size of 10 is (5927.0, 7320.4). The predicted value of the credit balance for a customer that has a household size of 10 is $6623.7. This confidence interval means

The p-value is less than .05 so we would reject the null hypothesis. 6. Based on findings from questions 1-5 we can conclude that the relationship between credit balance and size is linear with positive correlation. That is model is useful for predicting the credit balance using the variable size as a independent variable. 7. 95% confidence interval. (300.788, 505.655). This means that we can say with 95% confidence that the true value of the slope will be between (300.788, 505.655) 8. The 95% confidence interval for the mean credit balance for a customer that has a household size of 5 is (4368.2, 4846.9). The predicted value of the credit balance of a customer that has a household size of 5 is $4607.5. This confidence interval means that with 95% confidence, we can say that the true value of the mean credit balance for a customer that has a household size of 5 will be within the interval ($4368.2, $4846.9) Taken from minitab: Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 4607.5 119.0 (4368.2, 4846.9) (3337.9, 5877.2) Values of Predictors for New Observations New Obs Size 1 5.00 9. The 95% confidence interval for the mean credit balance for a customer that has a household size of 10 is (5927.0, 7320.4). The predicted value of the credit balance for a customer that has a household size of 10 is $6623.7. This confidence interval means

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