# Explain the effect of multicollinearity in multiple regression, and how multicollinearity is detected?

Question:

Please answer each of the following questions in detail and provide examples for better clarity wherever applicable. Provide in-text citations. In answering the following questions please include choice of significance level and the effect of p-values

1. Explain the linear multiple regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives

2. Given the data, what approach is taken to construct the model?

3. Explain the effect of multicollinearity in multiple regression, and how multicollinearity is detected?

4. Show that the estimates of the coefficients are unbiased estimates of actual values.

5. Explain the hypotheses on coefficients of the regression and how the results of testing these hypotheses are interpreted about significance of these coefficients? Include both unidirectional and bidirectional situations

6. How do you interpret the effect of significant coefficients?

7. How are the distribution of the observed residuals of the constructed model tested for normality?

8. What is the coefficient of determination, and what is its significance?

9. Why is the adjusted coefficient of determination used as an alternative assessment

10. How can the regression model be used for prediction?

Note:

1. Need to have at least 1 peer-reviewed article as the reference and textbook as the reference

2. Need in-text citation

3. Please find the attachments as the power points of the course for reference.

4. Textbook Information:

Bowerman, B., Drougas, A. M., Duckworth, A. G., Hummel, R. M. Moniger, K. B., & Schur, P. J. (2019). Business statistics and analytics in practice (9th ed.). McGraw-Hill

ISBN 9781260187496

5. Please find the Course Learning Outcome list of this course in the attachment

6. Need to explain in detail and provide examples

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bowerman_9e_chap_14.pptx
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bowerman_9e_chap_15.pptx