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…

Taken from minitab:
Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI 1 6623.7 346.5 (5927.0, 7320.4) (5195.3, 8052.0)XX

XX denotes a point that is an extreme outlier in the predictors.

Values of Predictors for New Observations

New Obs Size 1 10.0

10. After reviewing the data household size 10 does not fall in the range for household size for the credit customers. We can conclude that the estimate for a household size of 10 is not reliable and should not be used.
11. Credit Balance($) = 1276.0 + 32.272 Income ($1000) + 346.85 Size + 7.88 Years

Where credit balance is the dependent variable and income, size and years are independent variables.
Credit balance = β0+β1(Income)+β2(Size)+β3(Years)
Taken from minitab:
Regression Analysis: Credit Balance($ versus Income ($1000), Size, Years

The regression equation is
Credit Balance($) = 1276 + 32.3 Income ($1000) + 347 Size + 7.9 Years

Predictor Coef SE Coef T P
Constant 1276.0 273.6 4.66 0.000
Income ($1000) 32.272 4.348 7.42 0.000
Size 346.85 36.03 9.63 0.000
Years 7.88 12.34 0.64 0.526

S = 424.715 R-Sq = 80.5% R-Sq(adj) = 79.2%

Analysis of Variance

Source DF SS MS F P
Regression 3 34255444 11418481 63.30 0.000
Residual