Linear Regression and Transformations

Front

What is Linear Regression?

Back

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

Front

What is the equation of a line in slope-intercept form?

Back

The equation of a line in slope-intercept form is given by: y = mx + b, where m is the slope and b is the y-intercept.

Front

What does the slope in a linear regression equation represent?

Back

The slope represents the change in the dependent variable for a one-unit change in the independent variable.

Front

What is the purpose of the line of best fit?

Back

The line of best fit minimizes the distance between the observed data points and the line itself, providing the best approximation of the relationship between variables.

Front

What is a power transformation?

Back

A power transformation is a technique used to stabilize variance and make the data more closely conform to a normal distribution by applying a power function to the data.

Front

What is a quadratic transformation?

Back

A quadratic transformation involves squaring the values of the variable to model non-linear relationships.

Front

What does it mean if there is no association between two variables?

Back

No association means that changes in one variable do not predict changes in another variable; there is no discernible relationship.

Front

How do you interpret the y-intercept in a linear regression model?

Back

The y-intercept is the expected value of the dependent variable when all independent variables are equal to zero.

Front

What is the significance of R-squared in linear regression?

Back

R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model.

Front

What is the difference between positive linear and negative linear association?

Back

Positive linear association means that as one variable increases, the other variable also increases. Negative linear association means that as one variable increases, the other variable decreases.