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2. One of the ways to address the problem of multicollinearity is to do ridge regression. Give a short but detailed explanation of the motivation and application of using ridge regression. 3. Suggest a reasonable value for the biasing constant c based on the VIF values and R² using the following table. Motivate your suggestion. Also give the fitted model. с b b b 0.01 0.51 0.32 0.16 0.02 0.50 0.33 0.16 0.04 0.52 0.33 0.15 0.05 0.57 0.34 0.15 0.06 0.55 0.352 0.145 0.07 0.545 0.352 0.145

2. One of the ways to address the problem of multicollinearity is to do ridge regression. Give a short but detailed explanation of the motivation and application of using ridge regression. 3. Suggest a reasonable value for the biasing constant c based on the VIF values and R² using the following table. Motivate your suggestion. Also give the fitted model. с b b b 0.01 0.51 0.32 0.16 0.02 0.50 0.33 0.16 0.04 0.52 0.33 0.15 0.05 0.57 0.34 0.15 0.06 0.55 0.352 0.145 0.07 0.545 0.352 0.145

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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Question 4 A company that focuses on research and development (R&D) is interested in the relationship between profit and some variables that can explain profit variations. They want to predict the profits of some new and exciting projects because some are risky. The predictor variables considered are: RISK which is a company derived metric, R&D which is the research and development costs, REGION (WC, EC and KZN) and average per capita income (INCOME in R1000.00). The analyst decides to estimate the regression function with the following response function. Ý (profit) = bo + b₁XRisk + b₂XR&D +b3Xwc + b₁XEC + b5XINC Answer the following questions. 4.1. The analyst considered the fact that the effects of multicollinearity may influence some analytical and visual conclusions. Use the following results to show the process of establishing by using extra sums of squares whether there is that multicollinearity present or not. Explain and interpret in detail. SSR(X) = 2573.72, SSR(X₁) = 2600.7, SSR(X₂)X₁) = 1573.72, SSR(X₁|X5) = 400.2. 4.2. One of the ways to address the problem of multicollinearity is to do ridge regression. Give a short but detailed explanation of the motivation and application of using ridge regression. 4.3. Suggest a reasonable value for the biasing constant c based on the VIF values and R² using the following table. Motivate your suggestion. Also give the fitted model. с bR b b (VIF₂) (VIF₂) 5 (VIF3) R² 0.01 0.51 0.32 0.16 10 10.5 2 0.81 0.02 0.50 0.33 0.16 9 8 1.9 0.805 0.04 0.52 0.33 0.15 8 7 1.7 0.8 0.05 0.57 0.34 0.15 5 4 1.3 0.80 0.06 0.55 0.352 0.145 2 2.5 1.01 0.795 0.07 0.545 0.352 0.145 1.1 1.05 0.98 0.8
Transcribed Image Text:Question 4 A company that focuses on research and development (R&D) is interested in the relationship between profit and some variables that can explain profit variations. They want to predict the profits of some new and exciting projects because some are risky. The predictor variables considered are: RISK which is a company derived metric, R&D which is the research and development costs, REGION (WC, EC and KZN) and average per capita income (INCOME in R1000.00). The analyst decides to estimate the regression function with the following response function. Ý (profit) = bo + b₁XRisk + b₂XR&D +b3Xwc + b₁XEC + b5XINC Answer the following questions. 4.1. The analyst considered the fact that the effects of multicollinearity may influence some analytical and visual conclusions. Use the following results to show the process of establishing by using extra sums of squares whether there is that multicollinearity present or not. Explain and interpret in detail. SSR(X) = 2573.72, SSR(X₁) = 2600.7, SSR(X₂)X₁) = 1573.72, SSR(X₁|X5) = 400.2. 4.2. One of the ways to address the problem of multicollinearity is to do ridge regression. Give a short but detailed explanation of the motivation and application of using ridge regression. 4.3. Suggest a reasonable value for the biasing constant c based on the VIF values and R² using the following table. Motivate your suggestion. Also give the fitted model. с bR b b (VIF₂) (VIF₂) 5 (VIF3) R² 0.01 0.51 0.32 0.16 10 10.5 2 0.81 0.02 0.50 0.33 0.16 9 8 1.9 0.805 0.04 0.52 0.33 0.15 8 7 1.7 0.8 0.05 0.57 0.34 0.15 5 4 1.3 0.80 0.06 0.55 0.352 0.145 2 2.5 1.01 0.795 0.07 0.545 0.352 0.145 1.1 1.05 0.98 0.8
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