BUS 310 George Mason University Curvilinear Regression Model Discussion Questions

Description

State the common types of mathematical functions used in predictive analytics and their properties
Explain how to use the Trendline tool in Excel
What does R2 (squared) measure?
What is regression analysis?  What is the difference between simple and multiple regression?
How can you determine whether simple linear regression would be an appropriate technique to use?
Why does simple linear regression provide better predictions than simply using the sample mean of the dependent variable?
Explain the concepts underlying least-squares regression
Explain how to interpret the results from the Excel Regression tool
What are standard residuals, and how can they be used?
Explain the assumptions behind regression analysis
How can the assumptions of regression analysis be checked?
What is a multiple linear regression model?
How do you interpret a partial regression coefficient?
What hypothesis is tested with ANOVA for a multiple linear regression model?
How are ANOVA results in multiple linear regression used to test hypotheses for individual regression coefficients?
How should you properly use p-values from multiple linear regression to build a good model?
Explain how to use the t-statistics to build the best multiple linear regression model
What is multicollinearity, and why is it important to identify in multiple linear regression models?
Explain the concepts of parsimony and overfitting in building regression models
Why is it necessary to code categorical independent variables in regression modeling?
What is an interaction?  How can you test for interactions with regression?
Explain how to build a regression model if a categorical variable has more than two levels
What is a curvilinear regression model?  How are these models often used?
How can you tell if a curvilinear regression model should be used?

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