What does a Variance Inflation Factor (VIF) value greater than 10 generally suggest?
No multicollinearity
Perfect multicollinearity
Heteroscedasticity
Severe multicollinearity
How do hierarchical linear models help avoid misleading conclusions in nested data analysis?
By ignoring individual-level variations
By assuming all groups have the same effect on the outcome
By accounting for the correlation between observations within groups
By treating all observations as independent
What is the primary role of a link function in a Generalized Linear Model?
It determines the optimal number of predictor variables to include in the model.
It establishes a connection between the linear predictor and the mean of the response variable.
It calculates the residuals between the observed and predicted values.
It transforms the predictor variables to follow a normal distribution.
What happens to the bias and variance of a linear regression model as the regularization parameter (lambda) increases?
Bias increases, Variance decreases
Bias decreases, Variance increases
Bias decreases, Variance decreases
Bias increases, Variance increases
Which of the following is NOT a common link function used in Generalized Linear Models?
Logit
Probit
Polynomial
Complementary log-log
When using Principal Component Analysis (PCA) as a remedy for multicollinearity, what is the primary aim?
To remove all independent variables from the model
To increase the sample size of the dataset
To create new, uncorrelated variables from the original correlated ones
To introduce non-linearity into the model
What is a common consequence of autocorrelation in linear regression?
Biased coefficient estimates
Inflated standard errors of coefficients
Reduced model fit
Which of the following is a potential drawback of using robust regression methods?
They always require data normalization before model fitting
They always result in models with lower predictive accuracy than OLS regression
They can be computationally more expensive than OLS regression
They are not applicable to datasets with categorical variables
What does multicollinearity refer to in the context of multiple linear regression?
Non-linearity in the relationship between predictors and outcome.
The presence of outliers in the data.
A high correlation between the outcome variable and a predictor variable.
A high correlation between two or more predictor variables.
Which of the following is a common method for addressing multicollinearity in multiple linear regression?
Transforming the outcome variable.
Increasing the sample size.
Removing one or more of the correlated predictor variables.
Ignoring the issue, as it has no impact on the model.