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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] Create 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   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   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

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] Create 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   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   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

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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] Create 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

 

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

 

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

 

 

[9] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed? [14] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable Download the file Golfejop Use JMP to develop a multiple linear regression model to predict the Earnings/Event using the data found in Golfimp, Consider the four independent variables listed in the table below. Find the best model and check assumptions. 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 p- value Significant at the 0.05 level? below. Include Summary of Fit, Analysis of Variance, and Parameter Estimates [10] Fit a new multiple regression model with the remaining independent variables. Summary of Fit, Analysis of Variance, and Parameter Estimates [4] Summary of Fit, Analysis of Variance, and Parameter Estimates EARNINGS Average Earnings per Event [15] Do the values of Variable Inflation Factors indicate any potential problems for this multiple regression? Why or why not? SCORE DRIVE D DRIVE A Average Score Average Drive Distance Average Drive Accuracy Average Putts per Round X2 X3 X PUTTS [16] State and interpret the Confidence Interval for the estimate of the regression coefficient of the independent variable SCORE. [1] Create a correlation matrix for the variables EARNINGS, SCORE, DRIVE_D, DRIVE_A, and PUTTS using JMP. [11] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable [17] What proportion of the variability of EARNINGS is explained by the multiple regression model. Interpret this value in terms of the problem. p- value Significant at the 0.05 level? [5] State the null and alternative hypothesis statements for testing a multiple regression model. (F-test) [18] Copy and paste the Residual by Predicted Plot [12] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed? [6] What is your conclusion about the overall model? [2] What is the correlation coefficient for EARNINGS and SCORE? Interpret the linear relationship between the two variables. [13] Fit a new multiple regression model with the remaining independent variables. Summary of Fit, Analysis of Variance, and Parameter Estimates Correlation 17] State the null and alternative hypothesis statements for testing the regression coefficient of an independent variable. (t-test) Interpret the linear relationship Coefficient [8] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable SCORE DRIVE D DRIVE A PUTTS [3] Does the correlation matrix indicate a potential multicollinearity problem? If so, which pair(s) of independent variables are a concem? If your answer is no, state why. [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?) p- value Significant at the 0.05 level? Name [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
Transcribed Image Text:[9] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed? [14] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable Download the file Golfejop Use JMP to develop a multiple linear regression model to predict the Earnings/Event using the data found in Golfimp, Consider the four independent variables listed in the table below. Find the best model and check assumptions. 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 p- value Significant at the 0.05 level? below. Include Summary of Fit, Analysis of Variance, and Parameter Estimates [10] Fit a new multiple regression model with the remaining independent variables. Summary of Fit, Analysis of Variance, and Parameter Estimates [4] Summary of Fit, Analysis of Variance, and Parameter Estimates EARNINGS Average Earnings per Event [15] Do the values of Variable Inflation Factors indicate any potential problems for this multiple regression? Why or why not? SCORE DRIVE D DRIVE A Average Score Average Drive Distance Average Drive Accuracy Average Putts per Round X2 X3 X PUTTS [16] State and interpret the Confidence Interval for the estimate of the regression coefficient of the independent variable SCORE. [1] Create a correlation matrix for the variables EARNINGS, SCORE, DRIVE_D, DRIVE_A, and PUTTS using JMP. [11] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable [17] What proportion of the variability of EARNINGS is explained by the multiple regression model. Interpret this value in terms of the problem. p- value Significant at the 0.05 level? [5] State the null and alternative hypothesis statements for testing a multiple regression model. (F-test) [18] Copy and paste the Residual by Predicted Plot [12] Is there a variable that should be removed from the multiple regression model? If so, which variable and why should it be removed? [6] What is your conclusion about the overall model? [2] What is the correlation coefficient for EARNINGS and SCORE? Interpret the linear relationship between the two variables. [13] Fit a new multiple regression model with the remaining independent variables. Summary of Fit, Analysis of Variance, and Parameter Estimates Correlation 17] State the null and alternative hypothesis statements for testing the regression coefficient of an independent variable. (t-test) Interpret the linear relationship Coefficient [8] What are your conclusions for each of the independent variables in the model? (Is the variable significant or not) Variable SCORE DRIVE D DRIVE A PUTTS [3] Does the correlation matrix indicate a potential multicollinearity problem? If so, which pair(s) of independent variables are a concem? If your answer is no, state why. [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?) p- value Significant at the 0.05 level? Name [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
EARNINGS SCORE DRIVE D DRIVE_A PUTTS 1 $239,493.68 70.37 288.4 60.2 31.824 2 $177,249.18 69.43 286.9 67.9 31.302 3 $218,619.18 70.23 276 71 31.806 4 $186,380.08 70.46 308.5 56.4 31.806 $209,511.75 69.78 282.9 68.5 31.428 $181,987.29 70.34 299.1 52.7 31.716 7 $162,536.13 69.92 287.8 65.2 31.68 8. $174,534.95 70.25 277 62.4 31.518 9 $135,353.70 70.64 291.8 67.9 32.346 10 $212,540.82 69.93 294.2 61.3 31.554 11 $297,079.50 70.26 298.7 61.3 32.31 12 $168,904.45 69.96 291.4 64.8 31.788 13 $135,791.58 70.21 309.8 55.7 31.734 14 $133,695.52 70.53 289.1 64.8 31.86 15 $112,192.04 70.59 279.7 71.2 31.302 16 $215,121.67 70.22 292.4 60.1 32.292 17 $183,922.93 70.86 287.2 52 31.986 18 $150,251.76 70.94 300 62.6 32.31 19 $183,356.69 71.13 291.7 67.1 32.058 20 $130,274.35 71.53 286.8 62.7 32.472 21 $286,285.40 69.73 308.4 70.6 32.094 22 $72,708.05 70.79 292.1 56.7 31.5 23 $99,597.31 71.07 295.8 57.2 31.518 24 $85,557.56 71.1 290.4 69.3 31.95
Transcribed Image Text:EARNINGS SCORE DRIVE D DRIVE_A PUTTS 1 $239,493.68 70.37 288.4 60.2 31.824 2 $177,249.18 69.43 286.9 67.9 31.302 3 $218,619.18 70.23 276 71 31.806 4 $186,380.08 70.46 308.5 56.4 31.806 $209,511.75 69.78 282.9 68.5 31.428 $181,987.29 70.34 299.1 52.7 31.716 7 $162,536.13 69.92 287.8 65.2 31.68 8. $174,534.95 70.25 277 62.4 31.518 9 $135,353.70 70.64 291.8 67.9 32.346 10 $212,540.82 69.93 294.2 61.3 31.554 11 $297,079.50 70.26 298.7 61.3 32.31 12 $168,904.45 69.96 291.4 64.8 31.788 13 $135,791.58 70.21 309.8 55.7 31.734 14 $133,695.52 70.53 289.1 64.8 31.86 15 $112,192.04 70.59 279.7 71.2 31.302 16 $215,121.67 70.22 292.4 60.1 32.292 17 $183,922.93 70.86 287.2 52 31.986 18 $150,251.76 70.94 300 62.6 32.31 19 $183,356.69 71.13 291.7 67.1 32.058 20 $130,274.35 71.53 286.8 62.7 32.472 21 $286,285.40 69.73 308.4 70.6 32.094 22 $72,708.05 70.79 292.1 56.7 31.5 23 $99,597.31 71.07 295.8 57.2 31.518 24 $85,557.56 71.1 290.4 69.3 31.95
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