PN2002 Methodology Workshop 3 - Regression

Flashcards covering key vocabulary and concepts related to regression learned in PN2002 Methodology Workshop 3.

Regression

A family of inferential statistics used to make predictions about data.

Simple Linear Regression

Predicting one outcome variable from one predictor variable, expressed as y = a + bx.

Multiple Regression

Predicting one outcome variable from more than one predictor variable, expressed as y = a + b1x1 + b2x2 + …

Assumptions of Regression

Key conditions that must be met for regression analysis, including linearity, normal distribution, and independence of predictors.

Homoscedasticity

The condition where residuals have the same variance across all predictor variable scores.

Heteroscedasticity

A systematic difference of residuals across predictors indicating varying dispersion.

Pearson’s Correlation

A test of the relationship between two continuous variables, representing the strength and direction of their linear relationship.

Spearman’s Correlation

A non-parametric measure of rank correlation that assesses how well the relationship between two variables can be described using a monotonic function.

Variance Inflation Factor (VIF)

A measure used to detect multicollinearity in regression analysis, indicating how much the variance of a predicted coefficient is inflated due to multicollinearity.

Intercept (a)

The expected outcome value when the predictor variable is zero in a regression model.

Slope (b)

The amount by which the outcome variable is expected to change for a one-unit increase in the predictor variable.

R-squared (R²)

The proportion of variance in the outcome variable that can be explained by the model.