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
Chapter4: Linear Functions
Section: Chapter Questions
Problem 8PT: Does Table 1 represent a linear function? If so, finda linear equation that models the data.
Related questions
Question
Please help with 4.2 and 4.3 only.
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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