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

Summary of Fit

Rsquare / 0.906535
RSquare Adj / 0.881939
Root Mean Square Error / 44.69533
Mean of Response / 1085.56
Observations (or Sum Wgts) / 25

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio /
Model / 5 / 368140.38 / 73628.1 / 36.8569
Error / 19 / 37955.78 / 1997.7 / Prob > F
C. Total / 24 / 406096.16 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t| /
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

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”. Then click OK.

(7). Right click “agesq” and select “Formula”. Then click “age” and “x”. 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

Summary of Fit

RSquare / 0.906535
RSquare Adj / 0.881939
Root Mean Square Error / 44.69533
Mean of Response / 1085.56
Observations (or Sum Wgts) / 25

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio /
Model / 5 / 368140.38 / 73628.1 / 36.8569
Error / 19 / 37955.78 / 1997.7 / Prob > F
C. Total / 24 / 406096.16 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t| /
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

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

Summary of Fit

RSquare / 0.655219
RSquare Adj / 0.64811
Root Mean Square Error / 151.2364
Mean of Response / 5411.41
Observations (or Sum Wgts) / 100

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio /
Model / 2 / 4216263.2 / 2108132 / 92.1691
Error / 97 / 2218627.0 / 22872 / Prob > F
C. Total / 99 / 6434890.2 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t| /
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

Summary of Fit

RSquare / 0.69803
RSquare Adj / 0.688594
Root Mean Square Error / 142.271
Mean of Response / 5411.41
Observations (or Sum Wgts) / 100

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio /
Model / 3 / 4491749.2 / 1497250 / 73.9709
Error / 96 / 1943140.9 / 20241 / Prob > F
C. Total / 99 / 6434890.2 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t| /
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

Summary of Fit

RSquare / 0.986665
RSquare Adj / 0.826649
Root Mean Square Error / 0.025243
Mean of Response / 0.500071
Observations (or Sum Wgts) / 14

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio /
Model / 12 / 0.04714773 / 0.003929 / 6.1660
Error / 1 / 0.00063720 / 0.000637 / Prob > F
C. Total / 13 / 0.04778493 / 0.3058

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t| /
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
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