Module 3 Flashcards
In evaluating a multiple linear model,
O The F-test is used to evaluate the overall regression.
O The coefficient of determination is interpreted as the percentage of variability in the response variable explained by the model.
O Residual analysis is used for goodness of fit assessment
O All of the above
All of the above
The percentage of variability in the response variable explained by the model
Coefficient of Determination
True or False
Residual analysis is used for goodness of fit assessment.
True.
In the presence of near multicollinearity,
O The coefficient of determination decreases
O The regression coefficients will tend to be identified as statistically significant even if they are not
O The prediction will not be impacted
O None of the above.
None of the above.
Multicollinearity does not affect R^2 since adding redundant variables c
When do we use transformations?
O If the linearity assumption with respect to one or more predictors does not hold, then we use transformations of the corresponding predictors to improve on this assumption
O If the normality assumption does not hold, we transform the response variable, commonly using the Box-Cox transformation
O If the constant variance assumption does not hold, we transform the response variable
O All of the above
All of the above.
Which one is correct?
O The residuals have constant variance for the multiple linear regression model.
O The residuals vs fitted can be used to assess the assumption of independence.
O The residuals have a t-distribution if the error term is assumed to have a normal distribution
O None of the above
None of the above.
In a multiple linear regression model, the true errors have constant var
T/F Module 3 Topic 3.1 Lesson 4
In the context of the relationship between a response variable and a series of predicting variables, the estimated regression coefficients for conditional and marginal relationships can differ only in magnitude but the sign or direction of the relationship remains the same.
False
Presence of different predicting variables influence the overall relatio
T/F Module 3 Topic 3.2 Lesson 7
The partial F-test when adding one additional variable to the multiple linear regression model is equivalent to testing for statistical significance using the t-test on that same variable.
True
The partial F-test essentially tests if an additional variable Z has an
Module 3 Topic 3.1 Lesson 2
In case of multiple linear regression model with 7 quantitative predicting variables, and one qualitative variable (containing 3 levels D1, D2, D3) and an intercept. Excluding variance estimate term, ____ number of parameters need to be estimated in the regression model.
O 10
O 11
O 12
O 13
10
We remove one level to avoid linear dependencies between qualitative var
Module 3 Topic 3.2 Lesson 7
You performed a simple linear regression analysis, but after considering the link between factors, you chose to switch the predicting and response variables. You expect the following after refitting the regression model to the new data:
O The value of the correlation coefficient will change
O The value of the coefficient of determination will change
O The sign of the slope will change
O The value of SSE will change
The value of SSE will change
Least square regression line remains same even if we swap X and Y, hence
T/F Module 3.1 Lesson 4
You are interested in understanding the relationship between education level and IQ, with IQ as the response variable. In your model, you also include age. Age would be considered a controlling variable while education level would be an explanatory variable.
True
Controlling variables can be used to control for bias selection in a sam
T/F Topic 3.1 Lesson 2
If a predicting variable is categorical with 5 categories in a linear regression model without intercept, we will include 5 dummy variables in the model.
True
When we have qualitative variables with k levels, we only include k-1 du
The ____ model or simple linear regression captures the association of one predicting variable to the response variable marginally, that means without consideration of other factors.
Marginal Model
The ____ or multiple linear regression model captures the association of a predictor variable to the response variable, conditional of other predicting variables in the model.
conditional
T/F 3.4 Model Interpretation
The estimated regression coefficients for the conditional and marginal relationships can be different, not only in magnitude but also in sign or direction of the relationship.
True
The two models used to capture the relationship between a predicting var
