Instruction on JMP IN of Chapter 17

Example 17.1

(1). Download the dataset xm17-01.JMP from the website for this course and open it.

(2). Go to the Analyze menu and select Fit Y by X. Click on "Odometer" and then click the “X, Factor” button. Then click "Price" and then click the “Y, Response” button. Click OK.

(3). Click the red triangle next to the heading “Bivariate Fit of… ”, then select “Fit Line”.

Following is the output:

Bivariate Fit of Price By Odometer

Linear Fit

Price = 6533.383 - 0.0311577 Odometer

Summary of Fit

RSquare / 0.650132
RSquare Adj / 0.646562
Root Mean Square Error / 151.5688
Mean of Response / 5411.41
Observations (or Sum Wgts) / 100

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio
Model / 1 / 4183527.7 / 4183528 / 182.1056
Error / 98 / 2251362.5 / 22973 / Prob > F
C. Total / 99 / 6434890.2 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t|
Intercept / 6533.383 / 84.51232 / 77.31 / <.0001
Odometer / -0.031158 / 0.002309 / -13.49 / <.0001

Example 17.2

In the output of Example 17.1, "Root Mean Square Error" is just the standard error.

Example 17.3

See the last part of the output ("Parameter Estimates") of Example 17.1 for the test.

Example 17.4

"RSquare" in the output of Example 17.1 is the coefficient of determination.

Example 17.5

(1). Download the dataset xm17-05.JMP from the website for this course and open it.

(2). Go to the Analyze menu and select “Fit Y by X”. Click "TSE" and then click the “X, Factor” button. Then click "Nortel" and then click the “Y, Response” button. Click OK.

(3). Click the red triangle next to the heading “Bivariate Fit of… ”, then select “Fit Line”.

Following is the output:

Bivariate Fit of Nortel By TSE

Linear Fit

Nortel = 0.0128181 + 0.8876912 TSE

Summary of Fit

RSquare / 0.313688
RSquare Adj / 0.301855
Root Mean Square Error / 0.063123
Mean of Response / 0.018459
Observations (or Sum Wgts) / 60

Analysis of Variance

Source / DF / Sum of Squares / Mean Square / F Ratio
Model / 1 / 0.10562961 / 0.105630 / 26.5097
Error / 58 / 0.23110484 / 0.003985 / Prob > F
C. Total / 59 / 0.33673445 / <.0001

Parameter Estimates

Term / Estimate / Std Error / t Ratio / Prob>|t|
Intercept / 0.0128181 / 0.008223 / 1.56 / 0.1245
TSE / 0.8876912 / 0.172409 / 5.15 / <.0001

Example 17.6

At this step, you are recommended to use calculator to compute the confidence interval and predictive interval. We will show you how to use JMP IN to find these intervals in the instruction of Chapter 18.

Example 17.7

(1). Download the dataset xm17-01.JMP from the website for this course and open it.

(2).Go to the Analyze menu and select Multivariate. Double click "Odometer" and "Price". Then click OK.

(3). Click on the red triangle next to the heading “Multivariate” and select “Pairwise Correlations”.

Following is the output: (Note: The t-statistic is not given in the output.)

Multivariate

Correlations

Odometer / Price
Odometer / 1.0000 / -0.8063
Price / -0.8063 / 1.0000

Scatterplot Matrix

Pairwise Correlations

Variable / by Variable / Correlation / Count / Signif Prob
Price / Odometer / -0.8063 / 100 / 0.0000

Example 17.8

(1). Download the dataset xm17-08.JMP from the website for this course and open it.

(2).Go to the Analyze menu and select Multivariate. Double click "Aptitude" and "Performance". Then click OK.

(3). Click on the red triangle next to the heading “Multivariate” and select “Nonparametric Correlations”. Then choose "Spearman's Rho".

Following is the output:

Multivariate

Correlations

Aptitude / Performance
Aptitude / 1.0000 / 0.4541
Performance / 0.4541 / 1.0000

Scatterplot Matrix

Nonparametric: Spearman's Rho

Variable / by Variable / Spearman Rho / Prob>|Rho|
Performance / Aptitude / 0.3792 / 0.0991 / ++++++++++++++++