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
- Explain the linear multiple regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives
- Given the data, what approach is taken to construct the model?
- Explain the effect of multicollinearity in multiple regression, and how multicollinearity is detected?
- Show that the estimates of the coefficients are unbiased estimates of actual values.
- 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
- How do you interpret the effect of significant coefficients?
- How are the distribution of the observed residuals of the constructed model tested for normality?
- What is the coefficient of determination, and what is its significance?
- Why is the adjusted coefficient of determination used as an alternative assessment
- How can the regression model be used for prediction?
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
5. Please find the Course Learning Outcome list of this course in the attachment
6. Need to explain in detail and provide examples