11/24/2023 0 Comments Proc freq sas![]() ![]() The following sections give the formulas that PROC FREQ uses to compute the chi-square tests and statistics. The other chi-square tests and statistics in this section are appropriate for either nominal or ordinal variables. Note that the Mantel-Haenszel chi-square statistic is appropriate only when both variables lie on an ordinal scale. See the section Exact Statistics for more information. You can request these exact tests by specifying the corresponding options in the EXACT statement. PROC FREQ also provides an exact chi-square goodness-of-fit test for one-way tables. PROC FREQ provides exact tests for the Pearson chi-square, the likelihood-ratio chi-square, and the Mantel-Haenszel chi-square (in addition to Fisher’s exact test). When the sample size is not large, exact tests might be useful. When the sample size is large, these test statistics have an asymptotic chi-square distribution when the null hypothesis is true. The other chi-square tests and statistics described in this section are computed only for two-way tables.Īll of the two-way test statistics described in this section test the null hypothesis of no association between the row variable and the column variable. You can request Fisher’s exact test for general tables by specifying the FISHER option in the TABLES or EXACT statement.įor one-way frequency tables, the CHISQ option provides a chi-square goodness-of-fit test. ![]() ![]() For tables, the CHISQ option also provides Fisher’s exact test and the continuity-adjusted chi-square. PROC FREQ provides the following measures of association based on the Pearson chi-square statistic: the phi coefficient, contingency coefficient, and Cramer’s. When you specify the CHISQ option in the TABLES statement, PROC FREQ computes the following chi-square tests for each two-way table: the Pearson chi-square, likelihood-ratio chi-square, and Mantel-Haenszel chi-square. The CHISQ option provides chi-square tests of homogeneity or independence and measures of association based on the chi-square statistic. ![]()
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