Linear Regression Model Overview

These flashcards cover key concepts and terminology related to linear regression models as discussed in the lecture.

Dependent Variable

The outcome or target variable that is being predicted in a regression model, often represented as Y.

Independent Variable

Variables that are used to predict the dependent variable, also referred to as features in machine learning.

Feature Engineering

The process of selecting, modifying, or creating variables (features) to improve model performance.

Simple Linear Regression

A regression model that uses one independent variable to predict the dependent variable.

Multiple Linear Regression

A regression model that uses multiple independent variables to predict the dependent variable.

R-squared (R²)

A statistical measure that represents the proportion of variance for the dependent variable explained by the independent variables in a regression model.

P-value

A measure used to determine the statistical significance of results in regression analysis, usually compared against a significance level.

Coefficient

Values that represent the relationship between independent variables and the dependent variable in regression analysis, indicating the expected change in Y with a one-unit change in the predictor.

ANOVA Table

A table used in regression analysis to assess the overall significance of the model and its predictors.

Predicted Sales

The estimated value of sales based on the regression model and input values of independent variables.