2 Management Excel Problems

Consider the following data from a small bookstore. Number of sales people working—————–Sales (in $1000) 2————————————————————–10 3————————————————————–11 7————————————————————–13 9————————————————————–14 10————————————————————-18 10————————————————————-20 12————————————————————-20 15————————————————————-22 16————————————————————-22 20————————————————————-26 x = 10.4—————————————————–y = 17.6 SD(x) = 5.64———————————————–SD(y) = 5.34

1. Calculate the mean and standard deviation of # of sales people (x) and sales (y). Find sample size n.

2. Calculate the correlation coefficient with all three methods (covariance method, z-score method, and correlation tool in Excel).

3. Find b0 and b1 for the linear regression model with all three methods (two different formulas and Solver). Interpret the meaning of b1 of this linear regression model.

4. Calculate Se and SST.

5. Calculate R square with both methods (correlation coefficient method and the formula r square = 1 - SSE/SST). Interpret the meaning of R square of this linear regression model.

6. Use Regression tool in Excel to find b0, b1, SSE, SST, r, R square, and Se.

7. Estimate the sales if the number of sales people working is 8, 13, and 19, respectively.

Problem 4-2

(Please answer each question in a different work sheet of the same Excel file.)

The manager of the Petroco Service Station wants to forecast the demand for unleaded gasoline next month so that the proper number of gallons can be ordered from the distributor. The owner has accumulated the following data on demand for unleaded gasoline from sales during the past 12 months:

Month Gasoline Demanded (gal.)

October 800

November 725

December 630

January 500

February 645

March 690

April 730

May 810

June 1,200

July 980

August 1,000

September 850

a. Compute a 2-month moving average demand forecast for the months of December to September. Calculate MAD, MAPD and MSE.

b. Compute the optimal 2-month weighted moving average demand forecast for the months of December to September. Calculate MAD, MAPD and MSE.

c. Assume the demand forecast for October is equal to the actual demand of October, 800. Compute the optimal exponentially smoothed demand forecast for the months of November to September. Calculate MAD, MAPD and MSE.

d. Assume the demand forecast for October is 800 and trend for October is 0. Compute the optimal double exponentially smoothed demand forecast for the months of November to September. Calculate MAD, MAPD and MSE.

e. Compute linear trend line demand forecast for the months of October to September. Calculate MAD, MAPD and MSE.

f. Compare the five forecasting methods by using MAD, MAPD and MSE and indicate which method is the best fit for the data based on MAD, MAPD (or MAPE), and MSE, respectively.