Using a weight of for the most recent observation, for the second most recent observation, and for third most recent observation, compute a three-week weighted moving average for the 3 time series. (Round your answers to two decimal places.) Week 1 2 3 4 5 6 7 8 9 10 11 12 Time Series Value 17 21 19 23 18 16 21 19 23 21 16 23 Weighted Moving Average Forecast Compute the MSE for the weighted moving average in part (a). (Round your answer to two decimal places.) MSE =

Using a weight of for the most recent observation, for the second most recent observation, and for third most recent observation, compute a three-week weighted moving average for the 3 time series. (Round your answers to two decimal places.) Week 1 2 3 4 5 6 7 8 9 10 11 12 Time Series Value 17 21 19 23 18 16 21 19 23 21 16 23 Weighted Moving Average Forecast Compute the MSE for the weighted moving average in part (a). (Round your answer to two decimal places.) MSE =

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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7
Consider the following time series data. Week 1 2 3 4 S 9 Value 18 11 14 10 16 13 (a) Construct a time series plot. What type of pattern exists in the data? ◇ The data appear to follow a cyclical pattern. O The data appear to follow a trend pattern. The data appear to follow a seasonal pattern. ◇ The data appear to follow a horizontal pattern. (b) Develop the three-week moving average for this time series. (Round your answers to two decimal places.) Week Time Series Value Forecast 1 18 2 11 3 14 4 10 5 16 6 13 Compute MSE. (Round your answer to two decimal places.) MSE= What is the forecast for week 7?
Compare the three samples - monodisperse or disperse? Bimodal or multimodal?
Please do not rely too much on chatgpt, because its answer may be wrong. Please consider it carefully and give your own answer. You can borrow ideas from gpt, but please do not believe its answer.Very very grateful!
Question 3, 8.3.46
Consider the following quarterly time series. Quarter 1 2 3 4 Year 1 923 Year 2 1,112 1,056 1,156 992 Year 3 1,078 1,243 1,124 1,124 1,254 1,301 1,198 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year based on the model developed in part b.
Using the time series data in the table, respond to the following items. Period Sales 1 $ 1,001 2 1,129 3 841 4 1,044 5 1,014 6 703 7 682 8 712 9 646 10 686 11 909 12 469 13 566 14 488 15 688 16 675 17 303 18 381 d-1. Compute all possible forecasts using a trend forecasting model using simple linear regression? (Round your answers to 2 decimal places.) Period Sales Predicted Sales Absolute Error 1 1,001 1,026.85 25.85 2 1,129 990.60 138.40 3 841 954.35 113.35 4 1,044 918.10 125.90 5 1,014…
Consider the rate of return of stocks ABC and XYZ. Year 1 2 SAWN T 3 4 5 FABC 24% 10 17 4 1 ABC XYZ a. Calculate the arithmetic average return on these stocks over the sample period. (Round your answers to 2 decimal places.) rxyz 34% 12 18 1 -9 Arithmetic Average ABC XYZ % % b. Which stock has greater dispersion around the mean return?