Instruction on JMP IN of Chapter 19

Transcription

1 Instruction on JMP IN of Chapter 19 Example 19.2 (1). Download the dataset xm19-02.jmp from the website for this course and open it. (2). Go to the Analyze menu and select Fit Model. Click on "REVENUE" and then click on the Y button. Then double click on "INCOME", AGE, INC sq, AGE sq, and INC X AGE variables. Then click Run Model. (3). Sometimes, you need to create one new column that is the square of the other column. You can get the idea of how to do that later in this instruction. Following is the output: Response REVENUE Actual by Predicted Plot 1300 REVENUE Actual REVENUE Predicted P<.0001 RSq=0.91 RMSE= Rsquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 25 Model Error Prob > F C. Total <.0001 Intercept INCOME <.0001 AGE INC sq <.0001

2 AGE sq INC X AGE Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F INCOME <.0001 AGE INC sq <.0001 AGE sq INC X AGE Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept <.0001 INCOME <.0001 AGE INC sq <.0001 AGE sq INC X AGE Prediction Profiler 3269 REVENUE INCOME AGE INC sq AGE sq INC X AGE To run the new model with the transformed data: (1). Right click the mouse and select Add Multiple Columns, fill in 5 in the box after How many columns to add. Click OK. (2).Double left click on Column1 in the data set to change the column name as income. (Fill in income in the box after Column Name.) (3). Use the same method to change column2 to age, column3 to incomesq, column4 to agesq, column5 to incomexage. (4). Right click income and select Formula. Then click INCOME and - and input Then click OK. (5). Use the same way to get age. (Right click age and select Formula. Then click AGE and - and input Then click OK.) (6). Right click incomesq and select Formula. Then click income and x y. Then click OK.

3 (7). Right click agesq and select Formula. Then click age and x y. Then click OK. (8). Right click incomexage and select Formula. Then click income and X and age. Then click OK. (9). Go to the Analyze menu and select Fit Model. Click on "REVENUE" and then click on the Y button. Then double click on "incole", age, incomesq, agesq, and incomexage variables. Then click Run Model. The following is the output: Response REVENUE Actual by Predicted Plot 1300 REVENUE Actual REVENUE Predicted P<.0001 RSq=0.91 RMSE= RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 25 Model Error Prob > F C. Total <.0001 Intercept <.0001 income age incomesq <.0001 agesq incomexage Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F income age incomesq <.0001 agesq incomexage

4 Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept < income age incomesq <.0001 agesq incomexage Prediction Profiler 1862 REVENUE e-16 income e-16 age incomesq agesq incomexage Example 17.1 with color variable (1). Download the dataset xm17-01a.jmp from the website and open it. (2). Click fit model, choose Price as Y while choose Odometer and Color as Construct Model Effects, then click OK, we get the following result: (To make this instruction shorter, I just include part of the output from JMP) Response Price RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 100 Model Error Prob > F C. Total <.0001 Intercept <.0001 Odometer <.0001 Color (1). Create one column, change the name to I1 while creating another column and change the name to I2. (2). Put the cross on the I1 and right click, choose Formula ; (3). Click Conditional -> If, click Comparison -> a==b, in the first square, click Color, in the second square, input 1 ;

5 (4). Choose then clause and input 1, choose else clause, input 0, and then click OK ; (5). Put the cross on the I2 and right click, choose Formula ; (6). Click Conditional -> If, click Comparison -> a==b, in the first square, click Color, in the second square, input 2 ; (7). Choose then clause and input 1, choose else clause and input 0, and then click OK ; (8). Click Fit Model, choose Price as Y while choose Odometer, I1 and I2 as Construct Model Effects, then click OK ; we get the following result: Note: The formula for I1 has the following appearance. Response Price RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 100 Model Error Prob > F C. Total <.0001 Intercept <.0001 Odometer <.0001 I I Example 19.3 (1). Download the dataset xm19-03.jmp from the website for this course and open it. (2). Go to the Analyze menu and select Fit Model. Click on "Win_pct" and then click on the Y button. Then double click on "Rns_scrd", Team_BA,, SO, etc. variables. Then click Run Model. Following is the output:

6 Response Win_Pct Actual by Predicted Plot 0.60 Win_Pct Actual Win_Pct Predicted P= RSq=0.99 RMSE= RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 14 Model Error Prob > F C. Total Intercept Rns_Scrd Team_BA Team_Hmr Team_SB Team_Wlk Team_SO Rns_Alw Erns_Alw Hits_Alw Team_Ers Wlk_Alw SO Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F Rns_Scrd Team_BA Team_Hmr Team_SB Team_Wlk Team_SO Rns_Alw Erns_Alw Hits_Alw Team_Ers Wlk_Alw SO Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept

7 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Rns_Scrd Team_BA Team_Hmr Team_SB Team_Wlk Team_SO Rns_Alw Erns_Alw Hits_Alw Team_Ers Wlk_Alw SO To run Stepwise regression, follow the following steps: (1). Download the dataset xm19-03.jmp from the website for this course and open it. (2). Go to the Analyze menu and select Fit Model. Click on "Win_pct" and then click on the Y button. Then double click on "Rns_scrd", Team_BA,, SO, etc. variables. Then, select Stepwise from the Fitting Personality popup menu in the top-right corner of the dialog, and click Run Model. (3). Keep on clicking Step until no variable will be entered. You will get the following outputs: Stepwise Fit Response: Win_Pct Stepwise Regression Control Prob to Enter Prob to Leave Direction: Current Estimates SSE DFE MSE RSquare RSquare Adj Cp AIC Lock Entered Parameter Estimate ndf SS "F Ratio" "Prob>F" Intercept Rns_Scrd Team_BA Team_Hmr Team_SB Team_Wlk Team_SO Rns_Alw Erns_Alw Hits_Alw Team_Ers Wlk_Alw SO Step History Step Parameter Action "Sig Prob" Seq SS RSquare Cp p 1 Rns_Scrd Entered Rns_Alw Entered

8 Example 19.4 (1). Download the dataset xm19-04.jmp from the website for this course and open it. (2). Go to the Analyze menu and select Fit Model. Click on "Salary" and then click on the Y button. Then double click on "Education", Experience, and Gender variables. Then click Run Model. Following is the output: Response Salary Whole Model Actual by Predicted Plot Salary Actual Salary Predicted P<.0001 RSq=0.69 RMSE=16274 RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 100 Model e e Error e Prob > F C. Total e10 <.0001 Lack Of Fit Lack Of Fit e Pure Error Prob > F Total Error e Max RSq Intercept Education Experience <.0001 Gender Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F Education Experience e <.0001 Gender

9 Residual by Predicted Plot Salary Residual Salary Predicted Education Leverage Plot Salary Leverage Residuals Education Leverage, P= Experience Leverage Plot Salary Leverage Residuals Experience Leverage, P<.0001

10 Gender Leverage Plot Salary Leverage Residuals Gender Leverage, P=0.6183