PSY 293 Exam 2

Linear Regression

A statistical method used to model the relationship between a dependent variable and one or more independent variables, assuming a linear relationship.

Regression line

The line that best fits both variables in a linear regression analysis; known as the line of best fit.

Formula for Linear Regression

The equation used to represent the regression model: y = bx + a.

Intercept (a)

The value of the dependent variable when the independent variable is zero.

Prediction error (residual)

The difference between a data point and the corresponding predicted value from the regression line.

Hypothesis Testing in Regression

Testing to determine if the regression is significant, if x is a good predictor of y, and if the slope (b) is significantly different from 0.

Degrees of Freedom

The number of independent values or quantities which can be assigned to a statistical distribution; in regression, calculated as N-2.

Standard Error estimate

A measure of the accuracy of predictions made with a regression model; derived from the formula S = S(1 - r²)(N - 1)/(N - 2)/2.

Correlation

degree to which two variables are related

Positive Correlation

A relationship where an increase in one variable results in an increase in the other variable.

Negative Correlation

A relationship where an increase in one variable results in a decrease in the other variable.

Covariance

A measure of the degree to which two variables change together.

Correlation Coefficient (r)

A statistic that measures the strength and direction of a relationship between two variables, ranging from -1 to 1.

Pearson's product moment correlation

A measure of the linear correlation between two variables; represented by r.