~NEED HELP WITH QUESTIONS 4 TO 20~JMP PRO 15 PERFERRED BUT EXCEL ACCEPTED 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. EARNINGSAverage Earnings per Event SCOREAverage Score DRIVE_DAverage Drive Distance DRIVE_AAverage Drive Accuracy PUTTSAverage 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. CorrelationCoefficientInterpret 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 EARNINGSSCOREDRIVE DDRIVE_APUTTS1$239,493.6870.37288.460.231.8242$177,249.1869.43286.967.931.3023$218,619.1870.232767131.8064$186,380.0870.46308.556.431.806$209,511.7569.78282.968.531.428$181,987.2970.34299.152.731.7167$162,536.1369.92287.865.231.688.$174,534.9570.2527762.431.5189$135,353.7070.64291.867.932.34610$212,540.8269.93294.261.331.55411$297,079.5070.26298.761.332.3112$168,904.4569.96291.464.831.78813$135,791.5870.21309.855.731.73414$133,695.5270.53289.164.831.8615$112,192.0470.59279.771.231.30216$215,121.6770.22292.460.132.29217$183,922.9370.86287.25231.98618$150,251.7670.9430062.632.3119$183,356.6971.13291.767.132.05820$130,274.3571.53286.862.732.47221$286,285.4069.73308.470.632.09422$72,708.0570.79292.156.731.523$99,597.3171.07295.857.231.51824$85,557.5671.1290.469.331.95 [9] Is there a variable that should be removed from the multiple regression model? Ifso, 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)VariableDownload the file Golfejop Use JMP to develop a multiple linear regression modelto predict the Earnings/Event using the data found in Golfimp, Consider the fourindependent variables listed in the table below. Find the best model and checkassumptions.Use JMP the fit the full model (all of the independent variables). Include theconfidence interval and VIF for each of the regression parameters. Paste JMP outputp- valueSignificant 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 EstimatesEARNINGS Average Earnings per Event[15] Do the values of Variable Inflation Factors indicate any potential problems forthis multiple regression? Why or why not?SCOREDRIVE DDRIVE AAverage ScoreAverage Drive DistanceAverage Drive AccuracyAverage Putts per RoundX2X3X PUTTS[16] State and interpret the Confidence Interval for the estimate of the regressioncoefficient 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 multipleregression model. Interpret this value in terms of the problem.p- valueSignificant at the 0.05 level?[5] State the null and alternative hypothesis statements for testing a multipleregression 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? Ifso, 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 thelinear 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 EstimatesCorrelation17] State the null and alternative hypothesis statements for testing the regressioncoefficient of an independent variable. (t-test)Interpret the linear relationshipCoefficient[8] What are your conclusions for each of the independent variables in the model?(Is the variable significant or not)VariableSCOREDRIVE DDRIVE APUTTS[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 plotsupport the assumption of constant variance? (Is there an extreme change in thevariance?)p- valueSignificant at the 0.05 level?Name[20] Use the multiple regression model to estimate the earnings per event for a golferwith an average score of 70 and an average of 32 putts per round. Compute a pointestimate and construct a 95% Confidence Interval for the Mean. Interpret theconfidence interval in terms of the problem.Point estimateConfidence Interval for the MeanInterpretation of theConfidence Interval for the Mean

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~NEED HELP WITH QUESTIONS 4 TO 20~ JMP PRO 15 PERFERRED BUT EXCEL ACCEPTED 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