Understanding Regression Techniques

What is the primary purpose of linear regression?

• To calculate the mean of a dataset.

• To predict the value of a dependent variable based on the values of independent variables.

• To visualize data trends in a graph.

• To classify data into distinct categories.

How does logistic regression differ from linear regression?

• Logistic regression is used for time series forecasting; linear regression is not.

• Logistic regression requires normally distributed data; linear regression does not.

• Logistic regression predicts probabilities for categorical outcomes; linear regression predicts continuous values.

• Logistic regression can only handle binary outcomes; linear regression can handle multiple categories.

What are the key assumptions of linear regression?

• High multicollinearity

• Dependence of errors

• Non-linearity

• Linearity, independence, homoscedasticity, normality, no multicollinearity

Which technique is used to evaluate the goodness of fit in regression models?

• R-squared

• Standard deviation

• Mean absolute error

• P-value

What does the term 'multicollinearity' refer to in regression analysis?

• Multicollinearity is the presence of high correlation among independent variables in regression analysis.

• Multicollinearity is the effect of outliers on regression results.

• Multicollinearity indicates the presence of a single independent variable in the model.

• Multicollinearity refers to the relationship between dependent and independent variables.

What is the role of the intercept in a linear regression model?

• The intercept represents the slope of the regression line.

• The intercept indicates the value of the dependent variable when all predictors are zero.

• The intercept is the sum of all predictor variables.

• The intercept indicates the maximum value of the dependent variable.

How can you assess the significance of predictors in a regression model?

• Use p-values, t-tests, F-tests, and R-squared values.

• Ignore multicollinearity effects.

• Rely solely on visual inspection of data.

• Use correlation coefficients only.

What is the difference between R-squared and adjusted R-squared?

• Adjusted R-squared accounts for the number of predictors, while R-squared does not.

• R-squared is always higher than adjusted R-squared.

• R-squared measures the goodness of fit while adjusted R-squared measures the model complexity.

• Adjusted R-squared is used only for linear regression models.

What is the purpose of residual analysis in regression?

• To calculate the mean of the residuals.

• To determine the correlation between independent variables.

• To predict future values based on past data.

• The purpose of residual analysis in regression is to evaluate the goodness of fit of the model and identify any potential issues.

What are some common techniques for handling outliers in regression analysis?

• Applying standard deviation to all data points

• Common techniques for handling outliers in regression analysis include removing outliers, transforming variables, using robust regression methods, capping or winsorizing, and imputation.

• Ignoring the outliers completely

• Using linear regression exclusively