To make PROC FREQ run the binomial test on the "Yes" values of 1, use the LEVEL= option to look not at the first value of COLIC but the second as shown in the next example. If the COLIC variable were set up to be 0=No and 1=Yes, a common format for categorical data, the default settings would result in a binomial test on the "No" values because 0 comes before 1 in sorting order. The LEVEL= option in the TABLES statement will modify the group level on which the test of proportions is performed. The binomial test is applied to COLIC values of 1 because it is the first sorted value for that variable. For example, the above statements run a binomial test on COLIC, which takes one of two numeric values – a 1 (Yes) or a 2 (No). PROC FREQ will run a binomial test assuming that the probability of interest is the first level of the variable (in sorting order) in the TABLES statement. Notice that the Z statistic = 5.04 although our Z statistic was = 5.24. One Sample Test of Proportions Using SAS: proc freq The 95% confidence interval is 0.12 to 0.25. Thus we reject the null hypothesis and conclude that the prevalence of colic among children living near lead smelters is different from 0.07. We calculated a z-statistic of 5.24 which is greater than the critical value, 1.96 associated with a significance level α = 0.05. In our sample, the proportion of colic among children living near lead smelters was 0.19. H 1: The proportion of colic among children living near lead smelters is not 0.07 (p≠ 0.07).H 0: The proportion of colic among children living near lead smelters is 0.07 (p= 0.07).Using the information from the pbkid data set we can test if the prevalence of colic among children who live near lead smelter differs from that in the general public, which is around 7%. We can first estimate the proportion of colicky infants as: In the pbkid data set there were 124 children and 23 of them had colic. The upper and lower limits of the confidence interval are given by: Reject if Z > Z α/2, where Z α/2 is the 1-α/2 percentile of the standard normal distributionĪdditionally we can calculate confidence intervals for the sample proportion, again relying on the rule of thumb as stated above. Calculate the following test statistic, which under the null hypothesis, follows approximately (dependent on the rule of thumb stated above) a Standard Normal Distribution:.Estimate the population proportion by the sample proportion.A rule of thumb used to perform this test is that both np 0 and n(1-p 0) are greater than five. This value might be of historical interest or a result obtained in another study that we are trying to corroborate with our study data. This hypothesis considers whether the population proportion is equivalent to some pre-specified value, p 0. Where x is the number in the sample who have the trait or outcome of interest, and n is the size of the sample. We can use these data to make inferences on the general population of children living near a lead smelter. The control group (n=78) consisted of children whose blood lead levels were less than 40 ug/100mL in both 19, whereas the exposed group of children (n=46) had blood lead levels of at least 40 ug/100mL in either 1982 or 1983. These readings were used to quantify lead exposure. Each child had his or her blood lead level measured twice, once in 1982 and again in 1983. Rosner (Rosner, Fundamentals of Biostatistics, 1995) presents the data from an observational study which evaluated the effects of lead exposure on neurological and psychological function in children who lived near a lead smelter. The data set " pbkiddat" contains information on a sample of children living near a lead smelter. The prevalence of colic in the general public is estimated to be as low as 7%. For instance, we might be interested in studying the proportion of children living near a lead smelter who have colic. Suppose we are interested in estimating the proportion of individuals in a population who have a certain trait. Perform and interpret a chi square test, including with proc freq.Compute and interpret risk ratios and odds ratios.Perform cross-tabulation and generate 2x2 tables.Define "odds" and distinguish between proportions and odds.Perform a one sample test of proportions, including with proc freq.Learning ObjectivesĪfter successfully completing this module, students will be able to:
We will first describe one sample tests for a single proportion and then consider tests for association in cross tabulations. In this module we will address categorical data or count data.
How to interpret ordered difference report on sas jmp how to#
Up to this point we have discussed how to analyze continuous data.
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