Pricilla Phranklin opened her clothing store, Pricilla Phashions, in Downtown Dover a little over three years ago. Pants, skirts, blouses, and shirts have fairly consistent sales, while things like shorts, sweater and outerwear are highly seasonal. On the other hand, dresses are a little harder to predict. Pricilla is hiring you to arrive at the best forecasting method for dresses. The following table contains three years of dress sales data Year 3 Year 1 Year 2 Mont Dema Dema Dema h nd nd nd Jan 186 217 212 Feb 222 220 245 Mar 220 239 252 Apr 356 362 376 May 720 727 757 011 Jun 367 376 404 Jul 340 341 354 Aug 293 290 324 Sep 305 296 338 Oct 600 600 618 325 318 356 679 708 4613 4694 انال Nov Dec Total 713 4. 5. 4949 Using the dress data above, calculate the following 1. Forecast year 3 using a simple 3 period moving average 2. Forecast year 3 Calculate a forecast using the exponential smoothing method. Assume a = 0.20 3. (a) Using the information you have for years 1 & 2, calculate a monthly index. (b) Using year 1 and 2 data determine the slope and intercept of line equation using linear regression. Finally, calculate the new annual demand and forecast the monthly demand for year 3 Using the actual Year-3 numbers check your forecast accuracy, by calculating CFE, MSE, MAD, MAPE, and the tracking signal Finally, you are to indicate which forecasting technique is best and do an analysis of the shortcoming of each forecasting method, as indicated by the measure of forecast accuracy.
Pricilla Phranklin opened her clothing store, Pricilla Phashions, in Downtown Dover a little over three years ago. Pants, skirts, blouses, and shirts have fairly consistent sales, while things like shorts, sweater and outerwear are highly seasonal. On the other hand, dresses are a little harder to predict. Pricilla is hiring you to arrive at the best forecasting method for dresses. The following table contains three years of dress sales data Year 3 Year 1 Year 2 Mont Dema Dema Dema h nd nd nd Jan 186 217 212 Feb 222 220 245 Mar 220 239 252 Apr 356 362 376 May 720 727 757 011 Jun 367 376 404 Jul 340 341 354 Aug 293 290 324 Sep 305 296 338 Oct 600 600 618 325 318 356 679 708 4613 4694 انال Nov Dec Total 713 4. 5. 4949 Using the dress data above, calculate the following 1. Forecast year 3 using a simple 3 period moving average 2. Forecast year 3 Calculate a forecast using the exponential smoothing method. Assume a = 0.20 3. (a) Using the information you have for years 1 & 2, calculate a monthly index. (b) Using year 1 and 2 data determine the slope and intercept of line equation using linear regression. Finally, calculate the new annual demand and forecast the monthly demand for year 3 Using the actual Year-3 numbers check your forecast accuracy, by calculating CFE, MSE, MAD, MAPE, and the tracking signal Finally, you are to indicate which forecasting technique is best and do an analysis of the shortcoming of each forecasting method, as indicated by the measure of forecast accuracy.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
![Pricilla Phranklin opened her clothing store, Pricilla Phashions, in Downtown Dover a little over three years ago. Pants, skirts, blouses, and shirts have fairly consistent sales, while things like shorts, sweater and outerwear are highly seasonal. On
the other hand, dresses are a little harder to predict. Pricilla is hiring you to arrive at the best forecasting method for dresses.
The following table contains three years of dress sales data
Year
3
Year 1
Year 2
Mont Dema
Dema
h
nd
nd
nd
Jan
186
217
212
Feb
222
220
245
Mar
220
239
252
Apr
356
362
376
720
May
727
757
[1]
Jun
367
376
404
Jul
340
341
354
Aug
293
290
324
Sep
305
296
338
Oct
600
600
618
325
318
679
708
4613
4694
Nov
Dec
Total
1.
2.
3.
Dema
4.
5.
356
713
Using the dress data above, calculate the following
4949
Forecast year 3 using a simple 3 period moving average
Forecast year 3 Calculate a forecast using the exponential smoothing method. Assume a = 0.20
(a) Using the information you have for years 1 & 2, calculate a monthly index. (b) Using year 1 and 2 data determine the slope and intercept of line equation using linear regression. Finally, calculate the new annual demand and forecast the
monthly demand for year 3
Using the actual Year-3 numbers check your forecast accuracy, by calculating CFE, MSE, MAD, MAPE, and the tracking signal
Finally, you are to indicate which forecasting technique is best and do an analysis of the shortcoming of each forecasting method, as indicated by the measure of forecast accuracy.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F054d0b4a-edca-48ba-9ebe-36c23e945d13%2Fed6a2722-161e-4c14-8452-d48dace3d87d%2F8095qn_processed.png&w=3840&q=75)
Transcribed Image Text:Pricilla Phranklin opened her clothing store, Pricilla Phashions, in Downtown Dover a little over three years ago. Pants, skirts, blouses, and shirts have fairly consistent sales, while things like shorts, sweater and outerwear are highly seasonal. On
the other hand, dresses are a little harder to predict. Pricilla is hiring you to arrive at the best forecasting method for dresses.
The following table contains three years of dress sales data
Year
3
Year 1
Year 2
Mont Dema
Dema
h
nd
nd
nd
Jan
186
217
212
Feb
222
220
245
Mar
220
239
252
Apr
356
362
376
720
May
727
757
[1]
Jun
367
376
404
Jul
340
341
354
Aug
293
290
324
Sep
305
296
338
Oct
600
600
618
325
318
679
708
4613
4694
Nov
Dec
Total
1.
2.
3.
Dema
4.
5.
356
713
Using the dress data above, calculate the following
4949
Forecast year 3 using a simple 3 period moving average
Forecast year 3 Calculate a forecast using the exponential smoothing method. Assume a = 0.20
(a) Using the information you have for years 1 & 2, calculate a monthly index. (b) Using year 1 and 2 data determine the slope and intercept of line equation using linear regression. Finally, calculate the new annual demand and forecast the
monthly demand for year 3
Using the actual Year-3 numbers check your forecast accuracy, by calculating CFE, MSE, MAD, MAPE, and the tracking signal
Finally, you are to indicate which forecasting technique is best and do an analysis of the shortcoming of each forecasting method, as indicated by the measure of forecast accuracy.
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