Question-2:The same company decided to improve the forecasting technique. Usually they use exponentialsmoothing using smoothing constant 0.3. However, they would like to try alternate options: 0.4, 0.6, 0.7.Based on the following data for actual demand in the year of 2022, which smoothing constant they shouldchose among 0.3, 0.4,0.5 and 0.6. You can use excel/other software to calculate and draw graph. Showyour final result with a graph representing the actual demand and forecast for each smoothing constant.Also, calculate the MAPE (mean absolute percentage error) to justify your answer.Period123456789101112Actual demand (in thousands)101214111318201713161219

If At denotes the actual demand for time period t and Ft denotes the forecasted demand for time…

Answered: The same company decided to improve the…

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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Suppose you ran a quadratic trend forecasting model on some time series data for 18 months. The Excel Data Analysis regression output provided the following: Intercept: 45.140 Slope 1: -1.940 Slope 2: 0.195 What is the forecast value for time period 21? 4.4 90.395 43.395 8.495
While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.385*(age) + 5.218. If a customer is 44.315 years old and they make an average of 11.239 claims per year, the residual is -11.04. Interpret this residual in terms of the problem. Question 10 options: 1) The number of claims per year is 11.04 claims less than what we would expect. 2) The age is 11.04 years larger than what we would expect. 3) The age is 11.04 years less than what we would expect. 4) The number of claims per year is 11.239 claims less than what we would expect. 5) The number of claims per year is 11.04 claims…
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What needs to change in an overall problem if one wants to use linear regression?
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Statistics Canada produces consumer price indexes for several different categories. Shown here are the percentage changes in consumer price indexes over a period of 22 years for durable goods, semidurable goods, and nondurable goods. Also displayed are the percentage changes in total goods price index. Use these data and multiple regression to develop a model that attempts to predict the total goods index by the other three variables. Comment on the result of this analysis. Year TotalGoods DurableGoods SemidurableGoods NondurableGoods 1 85.7 89.4 91.9 83.2 2 86.4 90.4 92.5 83.8 3 87.8 92.5 93.4 85.2 4 86.8 96.0 94.2 81.6 5 88.4 99.0 94.9 82.9 6 89.9 100.8 95.4 84.4 7 91.2 101.6 97.0 86.0 8 91.4 101.5 97.7 86.1 9 93.1 101.5 99.2 88.4 10 96.0 100.8 99.6 93.3 11 98.4 100.1 100.3 97.4 12 100 100 100 100 13 101.9 99.2…
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Price/Book Value Ratio Return on Equity 13.032 1.405 8.305 2.113 6.654 1.239 3.262 2.449 5.291 2.398 7.719 0.353 2.569 7.593 5.104 2.012 4.797 2.182 4.129 1.918 1.549 1.951 5.046 2.417 2.159 3.011 1.725 5.582 4.698 Growth% 6.385 11.846 135.669 12.459 0.073 25.092 14.188 8.804 22.766 38.082 18.985 25.696 24.519 19.666 11.624 22.849 49.965 69.649 36.696 3.819 41.139 9.218 29.108 17.772 25.114 29.295 23.764 31.405 9.497 14.759 18.541 12.026 39.016 14.228 39.439 14.097 27.022 14.841 13.237 20.669 17.311 14.887 15.849 5.601 16.775 11.172 8.401 16.161 18.404 23.973 16.673 14.725 46.605 28.839 52.021

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