The exercises from Chapter 6 are: 1. Using the data set IRIS in the SASHELP library, generate summary statistics for variables PetalLength and PetalWidth. Include the mean, standard deviation, median, the number of nonmissing observations, and the number of missing observations. Using the tab ADDITIONAL ROLES, request the 95% confidence limits. Finally, request a histogram for the variable PetalLength. 2. Using the same data set in exercise 1, check for normality for the variable PetalLength. Add a Normal Quantile-Quantile (Q-Q) plot, and compute skewness and kurtosis for this variable. 3. Using the same data set in exercise 1, compute one-way frequencies for the variable Species. 4. Using the same data set in exercise 1, generate a horizontal box plot for the variable PetalLength using Species as the category variable. Use a filter to remove the species "Virginica." 5. Using the data set Heart in the SASHELP library, investigate the distribution of the variable Weight, broken down by the variable Sex. Using the Anderson-Darling test for normality, would you reject the null hypothesis (at the .05 level) that Weight is normally distributed? 6. Using the data set Heart in the SASHELP library, produce a histogram and box plot for Weight and Height separately for males and females (variable Sex with values of F and M). 7. Using the data set Cars in the SASHELP library, create a horizontal box plot for Invoice (invoice price) for each value of the variable Cylinders. Do this again with a filter to restrict the rows in the table to 4 or 6 cylinders. Hint: Your filter expression should be: Cylinders = 4 or Cylinders=6

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