Applying Linear Regression

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 does the correlation coefficient (r) indicate?

Back

The correlation coefficient (r) measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.

Front

What is the formula for the equation of the Line of Best Fit?

Back

The equation of the Line of Best Fit is typically expressed as y = mx + b, where m is the slope and b is the y-intercept.

Front

How do you interpret a positive correlation coefficient?

Back

A positive correlation coefficient indicates that as one variable increases, the other variable also tends to increase.

Front

How do you interpret a negative correlation coefficient?

Back

A negative correlation coefficient indicates that as one variable increases, the other variable tends to decrease.

Front

What is the significance of the slope in a linear regression equation?

Back

The slope (m) in a linear regression equation represents the change in the dependent variable (y) for a one-unit change in the independent variable (x).

Front

What does a correlation coefficient of -0.9 indicate?

Back

A correlation coefficient of -0.9 indicates a strong negative linear relationship between the two variables.

Front

What is the purpose of calculating the Line of Best Fit?

Back

The Line of Best Fit is used to predict the value of the dependent variable based on the value of the independent variable.

Front

What does a correlation coefficient of -0.34 indicate?

Back

A correlation coefficient of -0.34 indicates a weak negative linear relationship between the two variables.

Front

How can you calculate the linear regression equation from a data set?

Back

To calculate the linear regression equation, you can use statistical software or methods such as the least squares method to find the best-fitting line.