#1
What is the primary goal of regression analysis?
To predict the relationship between dependent and independent variables
ExplanationPredicting the relationship between variables.
#2
Which of the following is NOT a type of regression analysis?
Hierarchical Regression
ExplanationHierarchical Regression is not a type.
#3
What is the difference between simple linear regression and multiple linear regression?
Simple linear regression involves one independent variable, while multiple linear regression involves multiple independent variables.
ExplanationNumber of independent variables involved.
#4
What is the role of the intercept term in linear regression?
To account for the variability in the dependent variable that is not explained by the independent variables
ExplanationCapturing unexplained variability in the dependent variable.
#5
What does the coefficient of determination (R-squared) indicate in regression analysis?
The proportion of the variance in the dependent variable that is predictable from the independent variables
ExplanationIndicates the proportion of predictable variance.
#6
What is multicollinearity in regression analysis?
When two or more independent variables are highly correlated
ExplanationHigh correlation between independent variables.
#7
What is the purpose of using regularization techniques in regression models?
To reduce overfitting by penalizing large coefficients
ExplanationPreventing overfitting by penalizing large coefficients.
#8
What is the key assumption of linear regression regarding the residuals?
The residuals should be homoscedastic
ExplanationHomoscedastic residuals are assumed.
#9
What is the purpose of feature scaling in regression analysis?
To standardize the range of independent variables
ExplanationStandardizing independent variables' ranges.
#10
What is the purpose of residual analysis in regression?
To check the assumptions of the regression model
ExplanationValidating assumptions of the model using residuals.
#11
What does the p-value signify in regression analysis?
The significance of the regression coefficients
ExplanationSignificance of regression coefficients.
#12
What is the purpose of polynomial regression?
To model non-linear relationships between variables
ExplanationModeling non-linear relationships.
#13
What is the purpose of residual plots in regression analysis?
To detect patterns or trends in the residuals
ExplanationIdentifying patterns or trends in residuals.
#14
What does the term 'heteroscedasticity' mean in the context of regression analysis?
The residuals have increasing or decreasing variance across different levels of the independent variables
ExplanationResiduals' variance varies across independent variables.
#15
In logistic regression, what is the function used to map the linear combination of features to probabilities?
Sigmoid function
ExplanationMapping linear combination to probabilities using sigmoid.
#16
What is the role of regularization in regression?
To penalize large coefficients and prevent overfitting
ExplanationPreventing overfitting by penalizing large coefficients.
#17
In logistic regression, what type of variable is the dependent variable typically?
Binary
ExplanationDependent variable is typically binary.
#18
What is heteroscedasticity in regression analysis?
When the residuals have a changing variance across different values of the independent variable
ExplanationResiduals' variance changes with independent variable values.
#19
What is the purpose of cross-validation in evaluating regression models?
All of the above
ExplanationEvaluating models' performance with various data splits.
#20
What is the difference between Ridge and Lasso regression?
Ridge regression penalizes the absolute size of coefficients, while Lasso penalizes the square of coefficients.
ExplanationDifferent penalty methods for coefficient sizes.
#21
In stepwise regression, what is the purpose of the 'backward elimination' method?
To remove the least significant variables from the model
ExplanationEliminating least significant variables from the model.
#22
What is the adjusted R-squared value used for in regression analysis?
To compare the performance of different regression models
ExplanationComparing regression models' performance while considering complexity.