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The file P12_12.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the company's suspicion. An exponential ✔✔✔ fit looks reasonable. The graph of sales ✔ b. Fit the appropriate regression model to the data. Report the resulting equation. Let X represent the time index. Round your answer to three decimal places, if necessary. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) S 8.6619 Log() State explicitly what it says about the percentage growth per month. Round your answer to one decimal place, if necessary The equation implies an approximate 2.6 % increase per month. c. What are the RMSE and MAPE for the forecast model in part b? Round your answers to two decimal places, if necessary. 0.108099 RMSE MAPE 0.8738 In words, what do they measure? 0.0260 96 shows some sign of increasing at an increasing rate, but the graph of Log(sales) ✓ ✔ is nearly linear. forecast error. RMSE measures the square root of the average square MAPE measures the average absolute percentage v forecast error. d. Given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months? Round your answer to four decimal places, if necessary. Starting with the last forecast value for month 60, multiply each forecasted value by to obtain the next one. 2.4

The file P12_12.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the company's suspicion. An exponential ✔✔✔ fit looks reasonable. The graph of sales ✔ b. Fit the appropriate regression model to the data. Report the resulting equation. Let X represent the time index. Round your answer to three decimal places, if necessary. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) S 8.6619 Log() State explicitly what it says about the percentage growth per month. Round your answer to one decimal place, if necessary The equation implies an approximate 2.6 % increase per month. c. What are the RMSE and MAPE for the forecast model in part b? Round your answers to two decimal places, if necessary. 0.108099 RMSE MAPE 0.8738 In words, what do they measure? 0.0260 96 shows some sign of increasing at an increasing rate, but the graph of Log(sales) ✓ ✔ is nearly linear. forecast error. RMSE measures the square root of the average square MAPE measures the average absolute percentage v forecast error. d. Given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months? Round your answer to four decimal places, if necessary. Starting with the last forecast value for month 60, multiply each forecasted value by to obtain the next one. 2.4

MATLAB: An Introduction with Applications
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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Month Sales
1 5600
2 5740
3 6230
4 6210
5 7090
6 7130
7 6690
8 6080
9 8040
10 8030
11 7720
12 8200
13 7980
14 6930
15 8310
16 6870
17 7330
18 7670
19 7490
20 10230
21 10230
22 11010
23 11920
24 11530
25 13070
26 12700
27 12000
28 12700
29 11970
30 15690
31 17020
32 16980
33 15330
34 14890
35 15130
36 14630
37 15990
38 15910
39 16510
40 17060
41 18080
42 18220
43 16940
44 16600
45 17650
46 18070
47 17930
48 17150
49 19100
50 22090
51 20540
52 22250
53 23170
54 23610
55 26370
56 26320
57 24710
58 24150
59 24390
60 24360
The file P12_12.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the company's suspicion. An exponential V fit looks reasonable. The graph of sales ✔ b. Fit the appropriate regression model to the data. Report the resulting equation. Let X represent the time index. Round your answer to three decimal places, if necessary. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) ✔ Log() ✓ State explicitly what it says about the percentage growth per month. Round your answer to one decimal place, if necessary 8.6619 RMSE 0.108099 The equation implies an approximate 2.6 % increase per month. c. What are the RMSE and MAPE for the forecast model in part b? Round your answers to two decimal places, if necessary. 0.8738 MAPE In words, what do they measure? %6 0.0260 X ✓ shows some sign of increasing at an increasing rate, but the graph of Log(sales) ✓ is nearly linear. RMSE measures the square root of the average square MAPE measures the average absolute percentage ✓ forecast error. d. Given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months? Round your answer to four decimal places, if necessary. Starting with the last forecast value for month 60, multiply ✓ each forecasted value by to obtain the next one. forecast error. 2.4
Transcribed Image Text:The file P12_12.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the company's suspicion. An exponential V fit looks reasonable. The graph of sales ✔ b. Fit the appropriate regression model to the data. Report the resulting equation. Let X represent the time index. Round your answer to three decimal places, if necessary. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) ✔ Log() ✓ State explicitly what it says about the percentage growth per month. Round your answer to one decimal place, if necessary 8.6619 RMSE 0.108099 The equation implies an approximate 2.6 % increase per month. c. What are the RMSE and MAPE for the forecast model in part b? Round your answers to two decimal places, if necessary. 0.8738 MAPE In words, what do they measure? %6 0.0260 X ✓ shows some sign of increasing at an increasing rate, but the graph of Log(sales) ✓ is nearly linear. RMSE measures the square root of the average square MAPE measures the average absolute percentage ✓ forecast error. d. Given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months? Round your answer to four decimal places, if necessary. Starting with the last forecast value for month 60, multiply ✓ each forecasted value by to obtain the next one. forecast error. 2.4
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