Problem: Do you see any evidence of autocorrelation in this data?

Month Sales Month Sales

1 (Jan.) $23,500 25 (Jan.) 31,000

2 21,700 26 30,400

3 18,750 27 29,800

4 22,000 28 32,500

5 23,000 29 34,500

6 26,200 30 33,800

7 27,300 31 34,200

8 29,300 32 36,700

9 31,200 33 39,700

10 34,200 34 42,400

11 39,500 35 43,600

12 43,400 36 47,400

13 (Jan.) 23,500 37 (Jan.) 32,400

14 23,400 38 35,600

15 21,400 39 31,200

16 24,200 40 34,600

17 26,900 41 36,800

18 29,700 42 35,700

19 31,100 43 37,500

20 32,400 44 40,000

21 34,500 45 43,200

22 35,700 46 46,700

23 42,000 47 50,100

24 42,600 48 52,100

Problem: Consider the following set of sales data given in millions of dollars.

1997 1999

1st quarter 152 1st quarter 217

2nd quarter 162 2nd quarter 209

3rd quarter 157 3rd quarter 202

4th quarter 167 4th quarter 221

1998 2000

1st quarter 182 1st quarter 236

2nd quarter 192 2nd quarter 242

3rd quarter 191 3rd quarter 231

4th quarter 197 4th quarter 224

1. Plot these data. Based on your visual observations, what time-series components are present in the data?
2. Determine the seasonal index for each quarter.
3. Fit a linear trend model to the deseasonalized data for 1997 through 2000 and determine the MAD and
   MSE values. Comment on the adequacy of the linear trend model based on these measures of forecast
   error.
4. Provide a seasonally unadjusted forecast using the linear trend model for each quarter of 2001.
5. Use the seasonal index values computed in part b to provide seasonal adjusted forecasts for each quarter

of 2001.

Problem: A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. The following data reflect the number of new customers added during each quarter between 1998 and 2001.

1998 1999

1st quarter 218 1st quarter 250

2nd quarter 190 2nd quarter 220

3rd quarter 236 3rd quarter 265

4th quarter 218 4th quarter 241

2000 2001

1st quarter 244 1st quarter 229

2nd quarter 228 2nd quarter 221

3rd quarter 263 3rd quarter 248

4th quarter 240 4th quarter 231

1. Plot the time series and discuss components that are present in the data.
2. Referring to part a, fit the linear trend model to the data for 1998 through 2000. Then use the resulting
   model to forecast the number of new brokerage customers for each quarter in 2001. Compute the MAD
   and MSE for these forecasts and discuss the results.
3. Using the data for 1998 through 2000, determine the seasonal indexes for each quarter.
4. Develop a seasonally unadjusted forecast for the four quarters of 2001.
5. Using the seasonal indexes computed in part d, determine the seasonally adjusted forecast for each
   quarter of 2001. Compute the MAD and MSE for these forecasts.
6. Examine the values for the MAD and MSE in parts b and e. Which of the two forecasting techniques

would you recommend the manager use to forecast the number of new clients generated each quarter?

Support your choice by giving your rationale.

15.63 Refer to Exercise 15.62, in which Anna Chen was interested in forecasting the number of sick days taken by employees at Datatron each month.

1. Develop a single exponential smoothing model using alpha = 0.20. Use as a starting value the average of the
   first six months’ data. Determine the forecasted value for month 37.
2. Compute the MAD for this model.
3. Plot the forecast values against the actual data.
4. Use the same starting value, but try different smoothing constants (say, 0.10, 0.20, 0.40, and 0.50) in an effort to reduce the MAD value. Prepare a short report that summarizes your efforts.

Price: $25.02

Solution: The downloadable solution consists of 15 pages, 1002 words and 17 charts.
Deliverable: Word Document