![]() ![]() By default, the EXACT statement generates 10,000 random tables that have the same row and column sum as the observed table. (Asymptotic) Chi-Square may not be a valid test." This is a classic example that calls for an exact test, which you can compute by using the EXACT statement, as follows:Įxact pchi / MC /* request Monte Carlo estimate */īecause I did not provide a random number seed, the Monte Carlo simulation is seeded by the time of day, which means that you will get a different answer each time you run the program. Consequently, the procedure will also issue a warning: "WARNING: 89% of the cells have expected counts less than 5. However, most of the cells in this table have small counts. ![]() ![]() If you run a chi-square test in PROC FREQ, the value of the chi-square statistic as 14.81 and the p-value of 0.0051. The adjacent 3 x 3 table (the nine number inside the heavy rectangle) appears in Agresti, Wackerly, and Boyett (1979). (Pearson) chi-square test for independence Let's see how an exact test works for a familiar test like the You can also use SAS/IML to simulate many random contingency tables, compute the statistic on each table, and thereby approximate the sampling distribution of the test statistic.Īn example of an exact chi-square test in SAS The EXACT statement in PROC FREQ, which supports the MC option for computing Monte Carlo estimates. This article shows how to generate Monte Carlo estimates for exact tests in SAS. If so, you can use a Monte Carlo approach to randomly generate tables that satisfy the null hypothesis for the test and evaluate the test statistic on those tables. However, even though PROC FREQ uses efficient methods to avoid unnecessary computations, the computational time required by exact tests might be prohibitively expensive for certain tables. For small and mid-sized problems, the procedure runs very quickly. The FREQ procedure in SAS supports computing exact p-values for many statistical tests. ![]()
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