Which of the following is a synonym for Hierarchical Linear Models?
Simple Linear Models
Nonlinear Regression Models
Time Series Models
Multilevel Models
What advantage does Polynomial Regression offer over simple Linear Regression when dealing with non-linear relationships between variables?
It reduces the need for feature scaling.
It simplifies the model, making it easier to interpret.
It always results in a better fit regardless of the data distribution.
It introduces polynomial terms, enabling the model to fit curved relationships in the data.
In multiple linear regression, what does a coefficient of 0 for a predictor variable indicate?
The variable is perfectly correlated with another predictor.
The variable is not statistically significant.
The variable has no impact on the predicted value.
The variable has a non-linear relationship with the outcome.
In which scenario might you prefer Huber regression over RANSAC for robust regression?
When the proportion of outliers is relatively small
When the outliers are expected to be clustered together
When dealing with high-dimensional data with a large number of features
When it's important to completely discard the outliers from the analysis
What does a high Cook's distance value indicate?
The observation has high leverage but low influence
The observation has both high leverage and high influence
The observation is not an outlier
The observation has low leverage but high influence
How do hierarchical linear models help avoid misleading conclusions in nested data analysis?
By accounting for the correlation between observations within groups
By treating all observations as independent
By ignoring individual-level variations
By assuming all groups have the same effect on the outcome
What does heteroscedasticity refer to in the context of multiple linear regression?
Multicollinearity among the predictor variables.
Non-constant variance of errors across different levels of the predictor variables.
The presence of outliers in the data.
Non-linearity in the relationship between predictors and outcome.
In which scenario would you prioritize using MAE over RMSE as your primary evaluation metric?
When you need a metric that is easy to compute.
When the dataset contains a large number of outliers.
When you want to give more weight to larger errors.
When you want a metric that is robust to outliers.
What is the primary reason multicollinearity poses a problem in linear regression?
It violates the assumption of linearity between the dependent and independent variables.
It makes the model too complex.
It inflates the variance of the regression coefficients, making them unreliable.
It reduces the model's predictive accuracy on new data.
Poisson regression, another type of GLM, is particularly well-suited for analyzing which kind of data?
Continuous measurements
Ordinal data with a specific order
Proportions or percentages
Count data of rare events