This example uses the Office Visits.jmp sample data table, which records late and on-time appointments for six clinics in a geographic region. 60 random appointments were selected from 1 week of records for each of the six clinics. To be considered on-time, the patient must be taken to an exam room within five minutes of their scheduled appointment time. Examine the proportion of patients that arrived on-time to their appointment.
 1 Open the Office Visits.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select On Time and click Y, Response.
 4 Select Clinic and click X, Factor.
 5 Select Frequency and click Freq.
 6 Click OK.
 7 From the red triangle menu next to Contingency Analysis, select Analysis of Means for Proportions.
 8 From the red triangle menu next to Analysis of Means for Proportions, select Show Summary Report.
Example of Analysis of Means for Proportions
Example of Analysis of Means for Proportions shows the proportion of patients who were on-time from each clinic. From Example of Analysis of Means for Proportions, notice the following:
 • The proportion of on-time arrivals is the highest for clinic F, followed by clinic B.
 • Clinic D has the lowest proportion of on-time arrivals, followed by clinic A.
 • Clinic E and clinic C are close to the average, and do not exceed the decision limits.
This example uses the Cheese.jmp sample data table, which is taken from the Newell cheese tasting experiment, reported in McCullagh and Nelder (1989). The experiment records counts more than nine different response levels across four different cheese additives.
 1 Open the Cheese.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select Response and click Y, Response.
The Response values range from one to nine, where one is the least liked, and nine is the best liked.
 4 Select Cheese and click X, Factor.
 5 Select Count and click Freq.
 6 Click OK.
Mosaic Plot for the Cheese Data
From the mosaic plot in Mosaic Plot for the Cheese Data, you notice that the distributions do not appear alike. However, it is challenging to make sense of the mosaic plot across nine levels. A correspondence analysis can help define relationships in this type of situation.
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Example of a Correspondence Analysis Plot
Example of a Correspondence Analysis Plot shows the correspondence analysis graphically, with the plot axes labeled c1 and c2. Notice the following:
 • c1 seems to correspond to a general satisfaction level. The cheeses on the c1 axis go from least liked at the top to most liked at the bottom.
 • c2 seems to capture some quality that makes B and D different from A and C.
 • Cheese D is the most liked cheese, with responses of 8 and 9.
 • Cheese B is the least liked cheese, with responses of 1,2, and 3.
 • Cheeses C and A are in the middle, with responses of 4,5,6, and 7.
 8 From the red triangle menu next to Correspondence Analysis, select 3D Correspondence Analysis.
Example of a 3-D Scatterplot
From Example of a 3-D Scatterplot, notice the following:
 • Looking at the c1 axis, responses 1 through 5 appear to the right of 0 (positive). Responses 6 through 9 appear to the left of 0 (negative).
 • Looking at the c2 axis, A and C appear to the right of 0 (positive). B and D appear to the left of 0 (negative).
 • You can have two conclusions: c1 corresponds to the general satisfaction (from least to most liked). c2 corresponds to a quality that makes B and D different from A and C.
This example uses the Mail Messages.jmp sample data table, which contains data about e-mail messages that were sent and received. The data includes the time, sender, and receiver. Examine the pattern of e-mail senders and receivers.
 1 Open the Mail Messages.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select To and click Y, Response.
 4 Select From and click X, Factor.
 5 Click OK.
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Contingency Analysis for E-mail Data
 • Jeff sends messages to everyone but receives messages only from Michael.
 • Michael and John send many more messages than the others.
 • Michael sends messages to everyone.
 • John sends messages to everyone except Jeff.
 • Katherine and Ann send messages only to Michael and John.
Correspondence Analysis for E-mail Data
 • The Portion column shows that the bulk of the variation (56% + 42%) of the mail sending pattern is summarized by c1 and c2, for the To and From groups.
 • The Correspondence Analysis plot of c1 and c2 shows the pattern of mail distribution among the mail group, as follows:
 ‒ Katherine and Ann have similar sending and receiving patterns; they both send e-mails to Michael and John and receive e-mails from Michael, John, and Jeff.
 ‒ John’s patterns differ from the others. He sends e-mail to Ann, Katherine, and Michael, and receives e-mail from everyone.
This example uses the Hot Dogs.jmp sample data table. Examine the relationship between hot dog type and taste.
 1 Open the Hot Dogs.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select Type and click Y, Response.
 4 Select Taste and click X, Factor.
 5 Click OK.
 6 From the red triangle menu next to Contingency Analysis, select Cochran Mantel Haenszel.
 7 Select Protein/Fat as the grouping variable and click OK.
Example of a Cochran-Mantel-Haenszel Test
 • The Tests report shows a marginally significant Chi-square probability of about 0.0799, indicating some significance in the relationship between hot dog taste and type.
This example uses the Attribute Gauge.jmp sample data table. The data gives results from three people (raters) rating fifty parts three times each. Examine the relationship between raters A and B.
 1 Open the Attribute Gauge.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select A and click Y, Response.
 4 Select B and click X, Factor.
 5 Click OK.
 6 From the red triangle menu next to Contingency Analysis, select Agreement Statistic.
Example of the Agreement Statistic Report
From Example of the Agreement Statistic Report, you notice that the agreement statistic of 0.86 is high (close to 1) and the p-value of <.0001 is small. This reinforces the high agreement seen by looking at the diagonal of the contingency table. Agreement between the raters occurs when both raters give a rating of 0 or both give a rating of 1.
This example uses the Car Poll.jmp sample data table. Examine the relative probabilities of being married and single for the participants in the poll.
 1 Open the Car Poll.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select marital status and click Y, Response.
 4 Select sex and click X, Factor.
 5 Click OK.
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The Choose Relative Risk Categories Window
 • If you are interested in only a single response and factor combination, you can select that here. For example, if you clicked OK in the window in The Choose Relative Risk Categories Window, the calculation would be as follows:
 • If you would like to calculate the risk ratios for all (=4) combinations of response and factor levels, select the Calculate All Combinations check box. See Example of the Risk Ratio Report.
 7 Ask for all combinations by selecting the Calculate All Combinations check box. Leave all other default selections as is.
Example of the Risk Ratio Report
 1 Examine the first entry in the Relative Risk report, which is P(Married|Female)/P(Married|Male).
 2 You can find these probabilities in the Contingency Table. Since the probabilities are computed based on two levels of sex, which differs across the rows of the table, use the Row% to read the probabilities, as follows:
This example uses the Car Poll.jmp sample data table. Examine the probability of being married for males and females.
 1 Open the Car Poll.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select marital status and click Y, Response.
 4 Select sex and click X, Factor.
 5 Click OK.
 6 From the red triangle menu next to Contingency Analysis, select Two Sample Test for Proportions.
Example of the Two Sample Test for Proportions Report
This example uses the Car Poll.jmp sample data table. Examine the probability of being married for males and females.
 1 Open the Car Poll.jmp sample data table.
 2 Select Analyze > Fit Y by X.
 3 Select marital status and click Y, Response.
 4 Select sex and click X, Factor.
 5 Click OK.
 6 From the red triangle menu next to Contingency Analysis, select Measures of Association.
Example of the Measures of Association Report
 1 Open the Car Poll.jmp sample data table.
 2
 3 Select Analyze > Fit Y by X.
 4 Select sex and click Y, Response.
 5 Select size and click X, Factor.
 6 Click OK.
 7 From the red triangle menu next to Contingency Analysis, select Cochran Armitage Trend Test.
Example of the Cochran Armitage Trend Test Report