Download Biostatistics Homework: Testing for Normality using SAS Proc Univariate and Analyst and more Lab Reports Biostatistics in PDF only on Docsity! BIO 510/710 Biostatistics Frederick Testing for Normality: SAS Proc Univariate and Analyst Objective: To complete homework problems 6.1 (parts a and b) and 6.5 (Kolmogorov- Smirnov: an alternative to the D’Agostino - Pearson test). Introduction: Because common parametric statistical analyses require data be distributed normally, it is common to test data for their adherence to the normal distribution before the parametric tests and their associated probabilities are relied upon. In Chapter 6 of the Zar text, we find we can calculate the g1 statistic for symmetry (estimates the “gamma one” parameter), where a value near 0 indicates symmetry, a negative value indicates data skewed to the left (mean less than median), and a positive value indicates data skewed to the right (see figure 3.2). The g2 statistic indicates kurtosis (0 = mesokurtic, <0 = platykurtic, >0 = leptokurtic). The hand calculation for g1 is somewhat tedious, and g2 is even worse. Luckily, SAS will calculate these values easily under Proc Univariate, which can also be done via the Analyst utility. Significant asymmetry or significant deviations from mesokurtosis result in data unsuitable for parametric analysis. A single test statistic for how well data fit the normal distribution is a more popular means than the individual tests for symmetry or kurtosis. Zar recommends the D’Agostino - Pearson test, but SAS uses other tests, like the Kolmogorov - Smirnov test instead. Data are considered no different than normal unless we have significant reason to reject that null hypothesis (e.g., P<0.05). Homework Exercise 6.1 a and b, and Exercise 6.5: Calculate the g1 and g2 statistics and the SAS tests for normality by using Proc Univariate. You may use the frequencies of observations in Proc Univariate by using the Freq command. Enter the program below into the editor window and submit it. The Normal and Plot options will produce the tests for normality and will provide some standard plots of your data. Find the g1 and g2 statistics and circle and label them as your answers to 6.1 a and b. Compare the results from the SAS tests for normality with the answer to exercise 6.5 given by Zar (note the two do NOT use the same test). data weight; title Exercise 6.1 and 6.5 in Zar - <your name here>; input mass numobs; cards; 4.0 2 4.3 3 4.5 5 4.6 8 4.7 6 4.8 5 4.9 4 5.0 3 5.1 1 ; proc univariate normal plot; freq numobs; run; Enter the data in the Analyst table, then use Statistics, Descriptive, and Distributions to do the same test.
