Download the file Golf.jmp. Use JMP to develop a multiple linear regression model to predict the Earnings/Event using the data found in Golf.jmp. Consider the four independent variables listed in the table below. Find the best model and check assumptions.

	EARNINGS	Average Earnings per Event
	SCORE	Average Score
	DRIVE_D	Average Drive Distance
	DRIVE_A	Average Drive Accuracy
	PUTTS	Average Putts per Round

[1] Make a correlation matrix for the variables EARNINGS, SCORE, DRIVE_D, DRIVE_A, and PUTTS using JMP.

[2] What is the correlation coefficient for EARNINGS and SCORE? Interpret the linear relationship between the two variables.

Correlation Coefficient	Interpret the linear relationship
-0.5471	There is a negative correlation between the two variables. The strength of the correlation is moderate.

[3] Does the correlation matrix indicate a potential multicollinearity problem? If so, which pair(s) of independent variables are a concern? If your answer is no, state why?

Use JMP the fit the full model (all of the independent variables). Include the confidence interval and VIF for each of the regression parameters. Paste JMP output below. Include Summary of Fit, Analysis of Variance, and Parameter Estimates

[4] Summary of Fit, Analysis of Variance, and Parameter Estimates

[5] State the null and alternative hypothesis statements for testing a multiple regression model. (F-test)

[6] What is your conclusion about the overall model?

[7] State the null and alternative hypothesis statements for testing the regression coefficient of an independent variable. (t-test)

[8] What are your conclusions for each of the independent variables in the model?

(Is the variable significant or not)

Variable		Significant at the 0.05 level?
SCORE
DRIVE_D
DRIVE_A
PUTTS

[9] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed?

[10] Fit a new multiple regression model with the remaining independent variables.

Summary of Fit, Analysis of Variance, and Parameter Estimates

[11] What are your conclusions for each of the independent variables in the model?

(Is the variable significant or not)

Variable	p-value	Significant at the 0.05 level?

[12] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed?

[13] Fit a new multiple regression model with the remaining independent variables.

Summary of Fit, Analysis of Variance, and Parameter Estimates

[14] What are your conclusions for each of the independent variables in the model?

(Is the variable significant or not)

Variable	p-value	Significant at the 0.05 level?

[15] Do the values of Variable Inflation Factors indicate any potential problems for this multiple regression? Why or why not?

[16] State and interpret the Confidence Interval for the estimate of the regression coefficient of the independent variable SCORE.

[17] What proportion of the variability of EARNINGS is explained by the multiple regression model. Interpret this value in terms of the problem.

[18] Copy and paste the Residual by Predicted Plot

[19] Describe the shape of the distribution. Does the residual by predicted plot support the assumption of constant variance? (Is there an extreme change in the variance?)

[20] Use the multiple regression model to estimate the earnings per event for a golfer with an average score of 70 and an average of 32 putts per round. Compute a point estimate and construct a 95% Confidence Interval for the Mean. Interpret the confidence interval in terms of the problem.

Point estimate
Confidence Interval for the Mean
Interpretation of the Confidence Interval for the Mean

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