LITMIR.BIZ

       The first section of the output contains come useful and some not-so-useful values. For example, you see the number of nonmissing values that were used to compute the statistics (N), mean, and standard deviation.

      Also in this section, you see skewness and kurtosis, measures that show deviations from normality. A skewness value of 0 indicates a symmetric distribution about the mean; positive skewness values indicate a right-skewed distribution, and negative values indicate a left-skewed distribution. Left and right refer to the direction in which the elongated tail points. The value -.145 in this listing is very close to 0 and shows that there are no pronounced tails in the distribution of SBP. Kurtosis values indicate whether the distribution is more peaked than or flatter than a normal distribution. The value that SAS computes for kurtosis is scaled so that you get the value 0 for a normal distribution (also known as relative kurtosis). Positive values for kurtosis indicate both that the distribution is too peaked (leptokurtic) and that the tails are too heavy. Negative values for kurtosis indicate that the distribution is too flat (platykurtic) and that the tails are too light. The kurtosis value for SBP (-.535) indicates that the distribution of SBP is reasonably consistent with a normal distribution.

      The coefficient of variation (often abbreviated CV) expresses the standard deviation as a percent of the mean. This output shows that the standard deviation is about 8.38% of the mean. Finally, the value at the bottom right of this section is the standard error of the mean (1.46), which gives you an estimate of how accurately this sample has estimated the population mean.

      The remaining values in the section are less useful. This author believes that they were originally included so that you could use them in hand calculations of other statistics that were not computed by SAS. The sum of weights is useful only if you use a WEIGHT statement with PROC UNIVARIATE; with a WEIGHT statement you select a variable that weights the SBP values. In this example, because you did not specify any weights, the sum of weights is equal to the number of observations (all the weights are equal to 1). The uncorrected SS is the sum of squares of all the data values. To compute the corrected SS, you subtract the mean from each value before you square them, and then add them up. This value is the same as the numerator of the sample variance.

       The values listed in this section are somewhat redundant. They are grouped here for convenience as measures of location (mean, median, and mode) and measures of variability (standard deviation, variance, range, and interquartile range).

       This section displays a number of statistical tests that determine whether various measures of central location are significantly different from a theoretical value (mu). The default value for mu is mu=0. You can change the default value to another value by using the procedure option MU=n, where n is the nonzero value of your choice.

      The tests listed in this section are a one-sample Student’s t-test, a sign test, and a signed-rank test (also known as the Wilcoxon signed-rank test). These statistics are discussed in Chapter 5 (one-sample t-test) and Chapter 12 (the sign and Wilcoxon tests).