A set of experimental runs were made to determine a way of predicting a parameter Z in terms of A and B. The results are represented in TABLE 1. 1) Using simple linear regression models, find the missing values in TABLE 1. 2) Using TWO (2) different methods, determine which independent variable is the most linearly related to the dependent variable? Show your calculations and discuss your results. 3) Using a multiple linear regression model, find the missing values in TABLE 1. Compare your results with those obtained in question 1) and discuss your findings.
A set of experimental runs were made to determine a way of predicting a parameter Z in terms of A and B. The results are represented in TABLE 1. 1) Using simple linear regression models, find the missing values in TABLE 1. 2) Using TWO (2) different methods, determine which independent variable is the most linearly related to the dependent variable? Show your calculations and discuss your results. 3) Using a multiple linear regression model, find the missing values in TABLE 1. Compare your results with those obtained in question 1) and discuss your findings.
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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Can you please solve this problem without using Excel? Please clearly show how to get all the values step by step, including all the formulas. Thank You!
Transcribed Image Text:A set of experimental runs were made to determine a way of predicting a parameter Z in terms of
A and B. The results are represented in TABLE 1.
1) Using simple linear regression models, find the missing values in TABLE 1.
2) Using TWO (2) different methods, determine which independent variable is the most
linearly related to the dependent variable? Show your calculations and discuss your
results.
3) Using a multiple linear regression model, find the missing values in TABLE 1. Compare
your results with those obtained in question 1) and discuss your findings.
4) Analyze and discuss the fitness of the multiple linear regression model for the data.
TABLE 1.
Z
22.1
12
16.5
17.9
7.2
11.8
9.2
19
22.4
12.5
24.4
11.3
14.6
18
20.9
18.9
A
37.8
45.9
a₁
10.8
48.9
32.8
a2
32.9
47.7
36.6
39.6
20.5
23.9
27.7
16.7
27.1
B
69.2
69.3
58.5
58.4
75
23.5
65.9
46
52.9
b₁
55.8
18.3
19.1
53.4
22.9
22.9
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Step 1: Create the data table
VIEWStep 2: Derive relatioship between Z and A
VIEWStep 3: Obtain missing values using the relationship
VIEWStep 4: Obtain relationship between Z and B
VIEWStep 5: Obtain the missing value using the regresson equation
VIEWStep 6: Obtain multiple linear regression equation
VIEWStep 7: Computation of regression estimates
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