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(b) Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. If required, round your answers to two decimal places. y-intercept, b0 = 19.99 Slope, bi = 1.77 MSE = 3.89

(b) Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. If required, round your answers to two decimal places. y-intercept, b0 = 19.99 Slope, bi = 1.77 MSE = 3.89

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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I need help with finding the MSE.

**Title: Understanding Linear Regression for Time Series Analysis** **Objective:** Learn how to perform simple linear regression analysis to determine the parameters that minimize the Mean Squared Error (MSE) for a given time series. **Instructions:** 1. **Calculate the Parameters:** - **y-intercept (\(b_0\)):** 19.99 - **Slope (\(b_1\)):** 1.77 Both values are verified as correct. 2. **Determine the MSE:** - The calculated MSE value is 3.89, which appears to be incorrect. **Summary:** This exercise involves finding the optimal parameters (y-intercept and slope) for a regression line that best fits a time series data set by minimizing the MSE. Use the provided correct values for the y-intercept and slope, and reevaluate the MSE calculation for accuracy.
Transcribed Image Text:**Title: Understanding Linear Regression for Time Series Analysis** **Objective:** Learn how to perform simple linear regression analysis to determine the parameters that minimize the Mean Squared Error (MSE) for a given time series. **Instructions:** 1. **Calculate the Parameters:** - **y-intercept (\(b_0\)):** 19.99 - **Slope (\(b_1\)):** 1.77 Both values are verified as correct. 2. **Determine the MSE:** - The calculated MSE value is 3.89, which appears to be incorrect. **Summary:** This exercise involves finding the optimal parameters (y-intercept and slope) for a regression line that best fits a time series data set by minimizing the MSE. Use the provided correct values for the y-intercept and slope, and reevaluate the MSE calculation for accuracy.
The table illustrates the cost per unit over a period of eight years. Below is the detailed breakdown: | Year | Cost/Unit ($) | |------|---------------| | 1 | 20.00 | | 2 | 24.50 | | 3 | 28.20 | | 4 | 27.50 | | 5 | 26.60 | | 6 | 30.00 | | 7 | 31.00 | | 8 | 36.00 | This data indicates a general trend of increasing cost per unit over the eight-year period, with a slight fluctuation between years 3 and 5. The highest cost per unit is recorded in year 8 at $36.00, while the lowest is in year 1 at $20.00.
Transcribed Image Text:The table illustrates the cost per unit over a period of eight years. Below is the detailed breakdown: | Year | Cost/Unit ($) | |------|---------------| | 1 | 20.00 | | 2 | 24.50 | | 3 | 28.20 | | 4 | 27.50 | | 5 | 26.60 | | 6 | 30.00 | | 7 | 31.00 | | 8 | 36.00 | This data indicates a general trend of increasing cost per unit over the eight-year period, with a slight fluctuation between years 3 and 5. The highest cost per unit is recorded in year 8 at $36.00, while the lowest is in year 1 at $20.00.
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