Introduction to Statistical Analyses in SAS
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1 Introduction to Statistical Analyses in SAS Programming Workshop Presented by the Applied Statistics Lab Sarah Janse April 5, Introduction Today we will go over some basic statistical analyses in SAS. Let s start by first importing our data with the import procedure and then look at it with the proc contents statement. Figure 1: Importing and looking at the data. 2 Summary Statistics Before we perform a statistical test, it is often a good idea to look at some summary statistics to get an idea of what our data looks like. Let s find group means and standard deviations for the response variable. To do so, we will want to use proc sort proc means And here is the resulting output. 1
2 Figure 2: Calculating group means and standard deviations with the proc means statement. Figure 3: Means and standard deviations of the response variable by groupvar. Figure 4: Means and standard deviations of the response variable by groupvar2. 3 Performing T-tests The TTEST procedure performs tests and computes confidence limits for one sample, paired observations, two independent samples, and the AB/BA crossover 2
3 design. Two-sided, TOST (two one-sided test) equivalence, and upper and lower one-sided hypotheses are supported for means, mean differences, and mean ratios for either normal or lognormal data. Suppose we want to test whether the Control group mean is different from 40. To do so, we will want to perform a one-sample t-test. Figure 5: Testing the null hypothesis of control group mean equal to 40 vs. control group mean not equal to 40 with the proc ttest statement. And here is the resulting output. Figure 6: One-sample t-test output. Now, let s perform two-sample t-tests to test for difference between control group and treatment group and to test for differences between group A and group B. Figure 7: Performing two-sample t-tests with the proc ttest statement. 3
4 Figure 8: Two-sample t-test output for testing for differences between group A and group B. 4 Analysis of Variance (ANOVA) To perform an ANOVA in SAS, you can easily do so with the GLM procedure. Here is the resulting analysis created from the code. If we use the option solution Figure 9: Performing an ANOVA with the proc glm statement. in our model statement, SAS will produce a table of parameter estimates. 4
5 Figure 10: ANOVA table results. Figure 11: Parameter estimates for our model. 5 Linear Regression To do a simple regression in SAS, we can use the REG procedure. Suppose we want to look at the relationship between expvar and respvar. First, let s look at a scatter plot of the data to get an idea of what the data looks like and if a linear regression is appropriate. To make a scatter plot, we will once again use proc sgplot. 5
6 Figure 12: Creating a scatter plot of the data with proc sgplot. This code results in the following scatter plot. Figure 13: Scatter plot of the response variable by the explanatory variable. To perform the simple linear regression, we can use proc reg. Figure 14: Performing linear regression with proc reg. 6
7 This code results in the following output. Figure 15: Linear regression output. Linear Regression with more than one predictor There are often times when you will want to perform a multiple linear regression that involves more than one predictor. To do so in SAS, we will want to use proc glm. Figure 16: Performing multiple linear regression with proc glm. This code results in the following output. 7
8 Figure 17: Output for the multiple linear regression. Note here that the type I SS measures the increment in the sum of squares for the model as each variable is added to the model. The type III SS measures the sum of squares due to adding that variable last in the model. The F Value and Pr > F for the type III sum of squares are equivalent to the results of a t-test for testing that the regression coefficient equals zero. We may also be interested in looking at the interaction between expvar and groupvar. We can easily test this interaction by amending the above code to the following: Figure 18: Performing multiple linear regression with an interaction. 8
9 Figure 19: Output from proc glm. Notice that the parameter estimates for the class variable groupvar are given assuming that the treatment group is the reference parameter. If instead you want the Control group to be the reference parameter, we can change this in the class statement. Figure 20: Changing the reference parameter for groupvar. 9
10 Figure 21: Resulting parameter estimates with Control group as the reference parameter. 6 Need statistical assistance? If you are in need of statistical assistance feel free to contact us! To do so you can, Submit a request: asl@uky.edu 10
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