Math 533 Part C
PROJECT PART C: Regression and Correlation Analysis
Math-533 Applied Managerial Statistics
Prof. Jeffrey Frakes
December 12, 2014
Jared D Stock
1. Generate a scatterplot for income ($1,000) versus credit balance ($), including the graph of the best fit line. Interpret.
This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is a positive relationship between the two variables. Because of the positive relationship between income and credit balance the best fit line or linear regression line fits the data quite well. The speculation can be strongly made that the customer with the largest income will, more than likely, have the largest credit balance.
2. Determine the equation of the best fit line, which describes the relationship between income and credit balance.
Regression Analysis: Income($1000) versus Credit Balance($)
The regression equation is
Income($1000) = - 3.52 + 0.0119 Credit Balance($)
Predictor Coef SE Coef T P
Constant -3.516 5.483 -0.64 0.524
Credit Balance($) 0.011926 0.001289 9.25 0.000
S = 8.40667 R-Sq = 64.1% R-Sq(adj) = 63.3%
Analysis of Variance
Source DF SS MS F P
Regression 1 6052.7 6052.7 85.65 0.000
Residual Error 48 3392.3 70.7
Total 49 9445.0
This MiniTab output shows the equation of the best fit line in the income.
Income = - 3.52 + 0.0119 Credit Balance ($)
The credit balance is represented by the $ and the income is represented by the $1000s.
3. Determine the coefficient of correlation. Interpret.
The coefficient of correlation is 0.801. The positive value of the correlation coefficient shows that there is a strong positive correlation with the two variables. If one of them goes up or down, the other variable will match that
Math-533 Applied Managerial Statistics
Prof. Jeffrey Frakes
December 12, 2014
Jared D Stock
1. Generate a scatterplot for income ($1,000) versus credit balance ($), including the graph of the best fit line. Interpret.
This scatter plot graph is a representation of combining income and credit balance. It shows the income increasing as the credit balance increases. As a result of this data it can be inferred that there is a positive relationship between the two variables. Because of the positive relationship between income and credit balance the best fit line or linear regression line fits the data quite well. The speculation can be strongly made that the customer with the largest income will, more than likely, have the largest credit balance.
2. Determine the equation of the best fit line, which describes the relationship between income and credit balance.
Regression Analysis: Income($1000) versus Credit Balance($)
The regression equation is
Income($1000) = - 3.52 + 0.0119 Credit Balance($)
Predictor Coef SE Coef T P
Constant -3.516 5.483 -0.64 0.524
Credit Balance($) 0.011926 0.001289 9.25 0.000
S = 8.40667 R-Sq = 64.1% R-Sq(adj) = 63.3%
Analysis of Variance
Source DF SS MS F P
Regression 1 6052.7 6052.7 85.65 0.000
Residual Error 48 3392.3 70.7
Total 49 9445.0
This MiniTab output shows the equation of the best fit line in the income.
Income = - 3.52 + 0.0119 Credit Balance ($)
The credit balance is represented by the $ and the income is represented by the $1000s.
3. Determine the coefficient of correlation. Interpret.
The coefficient of correlation is 0.801. The positive value of the correlation coefficient shows that there is a strong positive correlation with the two variables. If one of them goes up or down, the other variable will match that