PSCH 443 Intro to Regression Bivariate Regrassion

Regression

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

Bivariate Regression

A type of regression analysis that involves two variables: one dependent variable and one independent variable.

Dependent Variable (Y)

The outcome variable that researchers are interested in explaining or predicting.

Predictor Variable (X)

An independent variable used to explain variations in the dependent variable.

Multiple Regression

A regression analysis involving multiple predictor variables to understand their effect on a dependent variable.

Variance

A statistical measure of how much values differ from the average value; crucial in understanding variability in data.

Y-intercept (A)

The value of the dependent variable when the independent variable is zero, representing the starting point of the regression line.

Slope (B)

A measure of the steepness of the regression line, indicating the change in the dependent variable for every one-unit change in the independent variable.

Simple Linear Model

The simplest form of regression model defined by the equation Y = A + BX, where A is the y-intercept and B is the slope.

Predicted Values (Ŷ)

Values estimated by the regression model, based on the model parameters and input values of the independent variable.

Causal Modeling

An approach in statistical analysis focused on understanding the causes and relationships between variables.

Model Evaluation

The process of assessing how well a regression model fits the data and how accurately it predicts outcomes.

Observed Values of Y

The actual measurements of the dependent variable that are collected during the study.