FAST Regression Analysis 1

Question 1

What is the purpose of linear regression analysis?

Accepted Answer

To model the relationship between a dependent variable and one or more independent variables

Answer

To prove causation between variables

Answer

To classify data into groups

Answer

To calculate the mode of a dataset

Question 2

What does R-squared (R²) measure?

Accepted Answer

The proportion of variance in the dependent variable explained by the independent variables

Answer

The correlation between the independent variables

Answer

The number of outliers in the data

Answer

The sample size needed for significance

Question 3

What is multicollinearity?

Accepted Answer

High correlation among independent variables in a regression model

Answer

A non-linear relationship between variables

Answer

Missing data in the dataset

Answer

Unequal variance across groups

Question 4

What is the purpose of residual analysis in regression?

Accepted Answer

To check whether the assumptions of the regression model are met

Answer

To calculate the regression coefficients

Answer

To determine the sample size

Answer

To test for correlation only

Question 5

What assumption must be met for ordinary least squares regression?

Accepted Answer

Residuals should be normally distributed with constant variance

Answer

All variables must be categorical

Answer

The sample size must exceed 1000

Answer

Independent variables must be perfectly correlated

Question 6

What is the difference between simple and multiple regression?

Accepted Answer

Simple regression has one predictor; multiple regression has two or more predictors

Answer

Simple regression is less accurate

Answer

Multiple regression can only be used with large datasets

Answer

There is no practical difference