The data in the file named Fast100 was collected by D. L. Green & Associates, a regional investment management company that specializes in working with clients who wish to invest in smaller companies with high growth potential. To aid the investment firm in locating appropriate investments for its clients, Sandra Williams, an assistant client manager, put together a database on 100 fast-growing companies. The database consists of data on eight variables for each of the 100 companies. Note that in some cases data are not available. A code of 99 has been used to signify missing data. These data will have to be omitted from any calculations.

a.  Select the variable Sales. Develop a frequency distribution and histogram for Sales.

b. Compute the mean, median, and standard deviation for the Sales variable.

c.  Determine the interquartile range for the Sales variable.

d. Construct a box and whisker plot for the Sales variable. Identify any outliers. Discard the outliers and recalculate the measures in part b.

a.  Calculate the sample mean miles per gallon (mpg) for both city and highway driving for the 30 cars. Also calculate the sample standard deviation for the two mileage variables. Do the data tend to support the premise that cars get better mileage on the highway than around town? Discuss.

b. Referring to part a, what can the EPA conclude about the relative variability between car models for highway versus city driving? (Hint: Compute the appropriate measure to compare relative variability.)

c.  Assume that mileage ratings are approximately bell- shaped. Approximately what proportion of cars gets at least as good mileage in city driving conditions as the mean mileage for highway driving for all cars?