Regression Basics

What is the main goal of linear regression?

• To model the relationship between variables using a linear equation.

• To calculate the mean of the variables

• To analyze time series data

• To predict categorical outcomes

Explain the difference between simple linear regression and multiple linear regression.

• The difference lies in the type of dependent variable used

• The main difference is the number of predictor variables used in the regression model.

• Simple linear regression uses more predictor variables than multiple linear regression

• The main difference is the type of data used in the regression models

What is the formula for a straight line in linear regression?

• y = mx / b

• y = mx - b

• y = mx * b

• y = mx + b

How is the best-fit line determined in linear regression?

• By minimizing the sum of the squared differences between observed values and predicted values using the method of least squares.

• By asking a magic eight ball for guidance

• By flipping a coin and choosing the line based on heads or tails

• By selecting the line that looks the most visually appealing

What is the coefficient of determination (R-squared) in linear regression?

• R-squared is a value between 0 and 1.

• R-squared is a value between 0 and 10.

• R-squared is a value between 0 and 2.

• R-squared is a value between -1 and 1.

What is the difference between correlation and regression?

• Correlation and regression are interchangeable terms used to describe the same statistical concept.

• Correlation measures the strength and direction of a relationship, while regression predicts one variable based on another.

What are the assumptions of linear regression?

• Linearity, Independence, Homoscedasticity, Normality of residuals

• Linearity, Dependence, Heteroscedasticity, Normality of residuals

• Non-linearity, Independence, Homoscedasticity, Normality of residuals

• Linearity, Independence, Heteroscedasticity, Non-normality of residuals

How is the error calculated in linear regression?

• Root Mean Squared Error (RMSE)

• Sum of Squared Errors (SSE)

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

What is the role of the cost function in linear regression?

• The cost function helps in optimizing the model by minimizing the difference between predicted and actual values.

• The cost function is used to calculate the accuracy of the model predictions

• The cost function determines the number of features in the model

• The cost function helps in selecting the best algorithm for linear regression

Explain the concept of residuals in linear regression.

• Residuals are only calculated for categorical variables in linear regression.

• Residuals are the same as predicted values in linear regression.

• Residuals in linear regression are the differences between the observed values and the values predicted by the regression model.

• Residuals are the errors made by the independent variables in linear regression.