17.3.9.1 Test for Homogeneity of Proportions Dialog Box

Contents

Supporting Information

To open the Two-Sample Poisson Rate Test Dialog:

  1. With a worksheet active, select Statistics: Hypothesis Testing: Two-Sample Poisson Rate Test...


See Also:


Input

Data Form
  • Summarized
Perform the test using summary statistics that you enter into the dialog box manually.
  • Raw
Perform the test using a dataset in a column.
Input Data (Summarized)
  • Sample Name
Specify name of the samples. You can specify a column as sample name, or enter space separated strings.
  • Sample Size
The total number of observations. You can specify a column dataset, or enter space separated value. (At least 3 samples.)
  • Number of Events
Specify the value for number of events. You can specify a column dataset, or enter space separated value. (At least 3 samples.)
Input Data (Raw)
  • Sample
Specify columns as the sample dataset. (at least 3 columns.)
  • Event
Specify number of event.

Statistics

Contingency Table This branch determines which statistics to calculate and output in the Contingency Table.
  • Count
The observed frequency for each cell
  • Expected Counts
The observed frequency for each cell under the assumption that the column and row variable are independent
  • Residuals
The difference between the observed count and the expected count.
  • Standardized Residuals
Also called Pearson residual. It standardizes the residuals by dividing by the square root of the expected count.
  • Adjusted Residuals
It is further standardized by taking into account of overall size of the sample. The most useful residual for comparing residual between different cells.
Effect Size
  • Cramer's V
A statistic which adjusts the chi-square by both the sample size and the dimension of table(n*m). It is commonly used for comparing the association between tables which have different dimensions.
Significat Level The significance level of the test.

Pairwise Comparison

Bonferroni The Bonferroni method controls the overall Type I error and is more conservative than Tukey. The method is commonly used for all pairwise comparisons tests.
Dunn-Sidak This is a more powerful method than the Dunnett test method, especially when the number of comparisons is large.
Dunnett Dunnett is a powerful test when comparing each treatment to a control and it is more capable to detect real differences.
Holm-Bonferroni This method is less conservative and more powerful than the Bonferroni method. Hence you have more chances to reject null hypotheses with the Bonferroni-Holm method.
Holm-Sidak The method is more powerful than Holm test. However, it can not be used to compute a set of confidence intervals.
Grouping Letters Table Showed difference of mean value with letters. Same letter means no significant difference, while different letter mean significant difference between two groups.

Power Analysis

Minimal Difference of Interest Specify the minimum difference from the Test Mean you are interested in. Once you enter a value in this box, the Power Analysis graph will show the minimum sample size to detect this difference in the different powers.
Difference(s) for Specified Power Output the differences for the specified power levels. When this option is selected, the specified power levels setting will affect the power level in power curve. If it is selected, it will use the default power levels.
Power Level(s) When check the Difference(s) for Specified Power option, in this edit box enter the power levels separated by space.
Actual Power When Specify the Minimal Difference of Interest, this option is available. Compute the power of the test (the probability of rejecting the null hypothesis when it is false).
Hypothetical Power When Specify the Minimal Difference of Interest, this option is available. Compute the power of the test at various sample sizes. When check this option, it will add corresponding curve to power curve.
Hypothetical Sample Size(s) Only available when Hypothetical Power is checked. Specify hypothetical sample size(s) for power computation.

Plots

Confidence Interval Plot Specify whether to display Confidence Interval plot
Mosaic Plot Specify whether to display Mosaic Plot whose area of each rectangle is proportional to the proportions of the Y variable in each level of the X variable.
Pairwise Comparison Plot Output a Pairwise Comparison plot. This option is checked default

Output

Graphical Summary Report Specify whether to display the graphical summary report
Graphical Power Analysis Report Specify whether to display the graphical Power Analysis report. The power curve shows how the statistical power of the test changes as the difference in proportions varies. Power is the probability of rejecting the null hypothesis when the true difference differs from 0.
Plot Data Specify the worksheet for output the plot datasets.
Result Specify the worksheet for output of test results