This example uses the Office Visits.jmp sample data table, which records late and ontime 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 ontime, the patient must be taken to an exam room within five minutes of their scheduled appointment time. Examine the proportion of patients that arrived ontime to their appointment.
1.

Open the Office Visits.jmp sample data table.

2.

Select Analyze > Fit Y by X.

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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 shows the proportion of patients who were ontime from each clinic. From Example of Analysis of Means for Proportions, notice the following:
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.

The Response values range from one to nine, where one is the least liked, and nine is the best liked.
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Click OK.

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 shows the correspondence analysis graphically, with the plot axes labeled c1 and c2. Notice the following:
8.

From the red triangle menu next to Correspondence Analysis, select 3D Correspondence Analysis.

From Example of a 3D Scatterplot, notice the following:
This example uses the Mail Messages.jmp sample data table, which contains data about email messages that were sent and received. The data includes the time, sender, and receiver. Examine the pattern of email senders and receivers.
1.

Open the Mail Messages.jmp sample data table.

2.

Select Analyze > Fit Y by X.

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Click OK.

Further visualize the results of the contingency table with a correspondence analysis. From the red triangle menu next to Contingency Analysis, select Correspondence Analysis.
From the Details report in Correspondence Analysis for Email Data, you notice the following:
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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.

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The Correspondence Analysis plot of c1 and c2 shows the pattern of mail distribution among the mail group, as follows:

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.

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Click OK.

6.

From the red triangle menu next to Contingency Analysis, select Cochran Mantel Haenszel.

7.

From Example of a CochranMantelHaenszel Test, you notice the following:
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.

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Click OK.

6.

From the red triangle menu next to Contingency Analysis, select Agreement Statistic.

From Example of the Agreement Statistic Report, you notice that the agreement statistic of 0.86 is high (close to 1) and the pvalue 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.

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Click OK.

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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:

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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.

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.

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Select Analyze > Fit Y by X.

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Click OK.

6.

From the red triangle menu next to Contingency Analysis, select Two Sample Test for Proportions.

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.

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Click OK.

6.

From the red triangle menu next to Contingency Analysis, select Measures of Association.

1.

Open the Car Poll.jmp sample data table.

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Select Analyze > Fit Y by X.

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Click OK.

7.

From the red triangle menu next to Contingency Analysis, select Cochran Armitage Trend Test.
