Instruction on JMP IN of Chapter 19

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 1200 1100 1000 900 800 800 900 1000 1100 1200 1300 REVENUE Predicted P<.0001 RSq=0.91 RMSE=44.695 Rsquare 0.906535 RSquare Adj 0.881939 Root Mean Square Error 44.69533 Mean of Response 1085.56 Observations (or Sum Wgts) 25 Model 5 368140.38 73628.1 36.8569 Error 19 37955.78 1997.7 Prob > F C. Total 24 406096.16 <.0001 Intercept -1133.981 320.0193-3.54 0.0022 INCOME 173.20317 28.20399 6.14 <.0001 AGE 23.549963 32.23447 0.73 0.4739 INC sq -3.726129 0.542156-6.87 <.0001

AGE sq -3.868707 1.179054-3.28 0.0039 INC X AGE 1.9672682 0.944082 2.08 0.0509 Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F INCOME 1 1 75338.118 37.7129 <.0001 AGE 1 1 1066.261 0.5338 0.4739 INC sq 1 1 94360.833 47.2354 <.0001 AGE sq 1 1 21507.422 10.7662 0.0039 INC X AGE 1 1 8674.258 4.3422 0.0509 Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept 1085.56 8.939067 121.44 <.0001 INCOME 1558.8285 253.836 6.14 <.0001 AGE 135.41229 185.3482 0.73 0.4739 INC sq -1649.93 240.0666-6.87 <.0001 AGE sq -407.0847 124.066-3.28 0.0039 INC X AGE 254.14154 121.9612 2.08 0.0509 Prediction Profiler 3269 REVENUE 1085.56-1445 15.6 24.2 INCOME 33.6 3.4 8.392 AGE 14.9 243.36 608.425 INC sq 1128.96 11.56 78.2144 AGE sq 222.01 53.04 203.354 311.41 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 24.2. Then click OK. (5). Use the same way to get age. (Right click age and select Formula. Then click AGE and - and input 8.392. Then click OK.) (6). Right click incomesq and select Formula. Then click income and x y. Then click OK.

(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 1200 1100 1000 900 800 800 900 1000 1100 1200 1300 REVENUE Predicted P<.0001 RSq=0.91 RMSE=44.695 RSquare 0.906535 RSquare Adj 0.881939 Root Mean Square Error 44.69533 Mean of Response 1085.56 Observations (or Sum Wgts) 25 Model 5 368140.38 73628.1 36.8569 Error 19 37955.78 1997.7 Prob > F C. Total 24 406096.16 <.0001 Intercept 1472.5221 88.47497 16.64 <.0001 income 9.3678491 2.743887 3.41 0.0029 age 71.157854 22.97214 3.10 0.0059 incomesq -3.726129 0.542156-6.87 <.0001 agesq -3.868707 1.179054-3.28 0.0039 incomexage 1.9672682 0.944082 2.08 0.0509 Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F income 1 1 23284.764 11.6559 0.0029 age 1 1 19167.581 9.5950 0.0059 incomesq 1 1 94360.833 47.2354 <.0001 agesq 1 1 21507.422 10.7662 0.0039 incomexage 1 1 8674.258 4.3422 0.0509

Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept 1085.56 +++++++++++++++++++++++ 8.939067 121.44 <.0001 +++++++++++++ income 84.310642 ++++++ 24.69498 3.41 0.0029 age 409.15766 +++++++++++++++++++++++ 132.0898 3.10 0.0059 +++++++++++++ incomesq -164.6017 -------------- 23.94974-6.87 <.0001 agesq -407.0847 ---------------------------------- 124.066-3.28 0.0039 incomexage 66.194641 ++++ 31.76646 2.08 0.0509 Prediction Profiler 1862 REVENUE 1085.56 173.91-8.6 9.9e-16 income 9.4-4.992 5e-16 age 6.508 0.01 22.7848 incomesq 88.36 11.56 78.2144 agesq 222.01-24.3648 0.2672 42.9312 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 0.655219 RSquare Adj 0.64811 Root Mean Square Error 151.2364 Mean of Response 5411.41 Observations (or Sum Wgts) 100 Model 2 4216263.2 2108132 92.1691 Error 97 2218627.0 22872 Prob > F C. Total 99 6434890.2 <.0001 Intercept 6580.1826 92.95884 70.79 <.0001 Odometer -0.031278 0.002306-13.56 <.0001 Color -21.67052 18.11408-1.20 0.2345 (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 ;

(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 0.69803 RSquare Adj 0.688594 Root Mean Square Error 142.271 Mean of Response 5411.41 Observations (or Sum Wgts) 100 Model 3 4491749.2 1497250 73.9709 Error 96 1943140.9 20241 Prob > F C. Total 99 6434890.2 <.0001 Intercept 6350.3231 92.16653 68.90 <.0001 Odometer -0.02777 0.002369-11.72 <.0001 I1 45.240979 34.08443 1.33 0.1876 I2 147.73801 38.18499 3.87 0.0002 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:

Response Win_Pct Actual by Predicted Plot 0.60 Win_Pct Actual 0.55 0.50 0.45 0.40 0.35.35.40.45.50.55.60 Win_Pct Predicted P=0.3058 RSq=0.99 RMSE=0.0252 RSquare 0.986665 RSquare Adj 0.826649 Root Mean Square Error 0.025243 Mean of Response 0.500071 Observations (or Sum Wgts) 14 Model 12 0.04714773 0.003929 6.1660 Error 1 0.00063720 0.000637 Prob > F C. Total 13 0.04778493 0.3058 Intercept -0.154661 0.904846-0.17 0.8922 Rns_Scrd 0.0005584 0.000585 0.95 0.5148 Team_BA 0.863167 3.10199 0.28 0.8272 Team_Hmr 0.0002489 0.000588 0.42 0.7450 Team_SB 0.0003479 0.000582 0.60 0.6571 Team_Wlk 0.0001627 0.000284 0.57 0.6689 Team_SO 0.0000541 0.000137 0.40 0.7604 Rns_Alw -0.001895 0.002003-0.95 0.5177 Erns_Alw 0.001145 0.001789 0.64 0.6376 Hits_Alw 0.0001834 0.000328 0.56 0.6756 Team_Ers -0.000117 0.000362-0.32 0.8011 Wlk_Alw 0.0002292 0.000214 1.07 0.4778 SO 0.0001692 0.001195 0.14 0.9105 Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F Rns_Scrd 1 1 0.00058047 0.9110 0.5148 Team_BA 1 1 0.00004934 0.0774 0.8272 Team_Hmr 1 1 0.00011424 0.1793 0.7450 Team_SB 1 1 0.00022750 0.3570 0.6571 Team_Wlk 1 1 0.00020894 0.3279 0.6689 Team_SO 1 1 0.00009948 0.1561 0.7604 Rns_Alw 1 1 0.00057007 0.8947 0.5177 Erns_Alw 1 1 0.00026089 0.4094 0.6376 Hits_Alw 1 1 0.00019901 0.3123 0.6756 Team_Ers 1 1 0.00006651 0.1044 0.8011 Wlk_Alw 1 1 0.00073255 1.1496 0.4778 SO 1 1 0.00001277 0.0200 0.9105 Scaled Estimates Continuous factors centered by mean, scaled by range/2 Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t Intercept 0.5000714 +++++++++++++++++++++++ 0.006746 74.12 0.0086

Term Scaled Estimate Plot Estimate Std Error t Ratio Prob> t +++++++++++++ Rns_Scrd 0.0706413 ++++++++++++ 0.074013 0.95 0.5148 Team_BA 0.0142423 ++ 0.051183 0.28 0.8272 Team_Hmr 0.0161808 ++ 0.038215 0.42 0.7450 Team_SB 0.0175675 ++ 0.029401 0.60 0.6571 Team_Wlk 0.0204152 ++++ 0.035652 0.57 0.6689 Team_SO 0.0118522 ++ 0.029997 0.40 0.7604 Rns_Alw -0.181877 ------------------------------------ 0.192287-0.95 0.5177 Erns_Alw 0.0996162 ++++++++++++++++++ 0.155681 0.64 0.6376 Hits_Alw 0.0246623 ++++ 0.04413 0.56 0.6756 Team_Ers -0.012913 -- 0.039971-0.32 0.8011 Wlk_Alw 0.0324346 ++++++ 0.03025 1.07 0.4778 SO 0.0049054 0.034653 0.14 0.9105 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 0.250 Prob to Leave 0.100 Direction: Current Estimates SSE DFE MSE RSquare RSquare Adj Cp AIC 0.0030252 11 0.000275 0.9367 0.9252-3.2524-112.158 Lock Entered Parameter Estimate ndf SS "F Ratio" "Prob>F" Intercept 0.46240196 1 0 0.000 1.0000 Rns_Scrd 0.0007405 1 0.032686 118.852 0.0000 Team_BA. 1 0.000313 1.155 0.3077 Team_Hmr. 1 0.000633 2.644 0.1350 Team_SB. 1 0.00057 2.320 0.1587 Team_Wlk. 1 0.000042 0.140 0.7157 Team_SO. 1 0.000524 2.096 0.1783 Rns_Alw -0.0006887 1 0.022716 82.598 0.0000 Erns_Alw. 1 0.000368 1.387 0.2662 Hits_Alw. 1 0.000539 2.168 0.1717 Team_Ers. 1 0.00046 1.793 0.2101 Wlk_Alw. 1 0.000355 1.331 0.2754 SO. 1 0.000006 0.019 0.8929 Step History Step Parameter Action "Sig Prob" Seq SS RSquare Cp p 1 Rns_Scrd Entered 0.0076 0.022044 0.4613 30.397 2 2 Rns_Alw Entered 0.0000 0.022716 0.9367-3.252 3

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 200000 Salary Actual 150000 100000 50000 0 0 50000 100000 150000 200000 Salary Predicted P<.0001 RSq=0.69 RMSE=16274 RSquare 0.693155 RSquare Adj 0.683567 Root Mean Square Error 16273.96 Mean of Response 89607.7 Observations (or Sum Wgts) 100 Model 3 5.74341e10 1.9145e10 72.2873 Error 96 2.54248e10 264841613 Prob > F C. Total 99 8.28589e10 <.0001 Lack Of Fit Lack Of Fit 80 2.41567e10 301959257 3.8100 Pure Error 16 1268054367 79253398 Prob > F Total Error 96 2.54248e10 0.0021 Max RSq 0.9847 Intercept -5835.104 16082.8-0.36 0.7175 Education 2118.8982 1018.486 2.08 0.0401 Experience 4099.3383 317.1936 12.92 <.0001 Gender 1850.9849 3703.07 0.50 0.6183 Effect Tests Source Nparm DF Sum of Squares F Ratio Prob > F Education 1 1 1146295197 4.3282 0.0401 Experience 1 1 4.42349e10 167.0239 <.0001 Gender 1 1 66171062.5 0.2499 0.6183

Residual by Predicted Plot 50000 40000 Salary Residual 30000 20000 10000 0-10000 -20000-30000 -40000 0 50000 100000 150000 200000 Salary Predicted Education Leverage Plot 200000 Salary Leverage Residuals 150000 100000 50000 0 11 12 13 14 15 16 17 18 19 20 Education Leverage, P=0.0401 Experience Leverage Plot 200000 Salary Leverage Residuals 150000 100000 50000 0 0 5 10 15 20 25 30 35 40 Experience Leverage, P<.0001

Gender Leverage Plot 200000 Salary Leverage Residuals 150000 100000 50000 0-0.5.0.5 1.0 1.5 Gender Leverage, P=0.6183