Develop a historical analogy time series and regression


Looking for help with a textbook case study. I tried to create the model in Excel and I just can't figure it out - I really want to know the steps to creating the forecast model so please include formulas and result screen shots.

An important part of planning manufacturing capacity is having a good forecast of sales. Elizabeth Burke is interested in forecasting sales of mowers in each marketing region to assess future changes in market share.

Develop a historical analogy, time series and regression (seasonality and trend) forecasting models for mower unit sales data.

Mower Unit Sales













Month

NA

SA

Europe

Pacific

China

World

Jan-10

6000

200

720

100

0

7020

Feb-10

7950

220

990

120

0

9280

Mar-10

8100

250

1320

110

0

9780

Apr-10

9050

280

1650

120

0

11100

May-10

9900

310

1590

130

0

11930

Jun-10

10200

300

1620

120

0

12240

Jul-10

8730

280

1590

140

0

10740

Aug-10

8140

250

1560

130

0

10080

Sep-10

6480

230

1590

130

0

8430

Oct-10

5990

220

1320

120

0

7650

Nov-10

5320

210

990

130

0

6650

Dec-10

4640

180

660

140

0

5620

Jan-11

5980

210

690

140

0

7020

Feb-11

7620

240

1020

150

0

9030

Mar-11

8370

250

1290

140

0

10050

Apr-11

8830

290

1620

150

0

10890

May-11

9310

330

1650

130

0

11420

Jun-11

10230

310

1590

140

0

12270

Jul-11

8720

290

1560

150

0

10720

Aug-11

7710

270

1530

140

0

9650

Sep-11

6320

250

1590

150

0

8310

Oct-11

5840

250

1260

160

0

7510

Nov-11

4960

240

900

150

0

6250

Dec-11

4350

210

660

150

0

5370

Jan-12

6020

220

570

160

0

6970

Feb-12

7920

250

840

150

0

9160

Mar-12

8430

270

1110

160

0

9970

Apr-12

9040

310

1500

170

0

11020

May-12

9820

360

1440

160

0

11780

Jun-12

10370

330

1410

170

0

12280

Jul-12

9050

310

1440

160

0

10960

Aug-12

7620

300

1410

170

0

9500

Sep-12

6420

280

1350

180

0

8230

Oct-12

5890

270

1080

180

0

7420

Nov-12

5340

260

840

190

0

6630

Dec-12

4430

230

510

180

0

5350

Jan-13

6100

250

480

200

0

7030

Feb-13

8010

270

750

190

0

9220

Mar-13

8430

280

1140

200

0

10050

Apr-13

9110

320

1410

210

0

11050

May-13

9730

380

1340

190

0

11640

Jun-13

10120

360

1360

200

0

12040

Jul-13

9080

320

1410

200

0

11010

Aug-13

7820

310

1490

210

0

9830

Sep-13

6540

300

1310

220

0

8370

Oct-13

6010

290

980

210

0

7490

Nov-13

5270

270

770

220

0

6530

Dec-13

5380

260

430

230

0

6300

Jan-14

6210

270

400

200

0

7080

Feb-14

8030

280

750

190

0

9250

Mar-14

8540

300

970

210

0

10020

Apr-14

9120

340

1310

220

5

10995

May-14

9570

390

1260

200

16

11436

Jun-14

10230

380

1240

210

22

12082

Jul-14

9580

350

1300

230

26

11486

Aug-14

7680

340

1250

220

14

9504

Sep-14

6870

320

1210

220

15

8635

Oct-14

5930

310

970

230

11

7451

Nov-14

5260

300

650

240

3

6453

Dec-14

4830

290

300

230

1

5651

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