Workshop: Correlation and Regression

Front

What is the linear regression equation?

Back

A linear regression equation is a mathematical representation of the relationship between two variables, typically in the form y = mx + b, where m is the slope and b is the y-intercept.

Front

How do you predict values using a linear regression equation?

Back

To predict values, substitute the given value of x into the linear regression equation and solve for y.

Front

What does a correlation coefficient indicate?

Back

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

Front

What does a correlation coefficient of -0.19 indicate?

Back

A correlation coefficient of -0.19 indicates a weak negative linear relationship between the two variables.

Front

What does a correlation coefficient of 0.89 indicate?

Back

A correlation coefficient of 0.89 indicates a strong positive linear relationship between the two variables.

Front

What is the least-squares regression line?

Back

The least-squares regression line is the line that minimizes the sum of the squares of the vertical distances of the points from the line.

Front

What is the formula for calculating the slope (m) in linear regression?

Back

The slope (m) is calculated using the formula m = (NΣxy - ΣxΣy) / (NΣx² - (Σx)²), where N is the number of data points.

Front

What is the significance of the y-intercept (b) in a regression equation?

Back

The y-intercept (b) represents the value of y when x is zero, indicating where the line crosses the y-axis.

Front

How can you determine if a correlation is strong, moderate, or weak?

Back

A correlation is considered strong if it is close to 1 or -1, moderate if it is around 0.5 or -0.5, and weak if it is close to 0.

Front

What is the range of possible values for a correlation coefficient?

Back

The range of possible values for a correlation coefficient is from -1 to 1.