Lecture_20Video_20W11D2_20-_20Interactions

Interaction (in regression)

A scenario where the effect of one predictor variable on the response variable depends on the level of another predictor variable.

Additive Model

A model where the effects of predictors are assumed to add together without interaction.

Effect Modification

Another term for interaction, particularly in medical statistics.

Beta Coefficient (β)

Represents the average change in the response variable for a one-unit increase in the predictor variable, holding other predictors constant.

Main Effect

The effect of a single predictor variable on the response variable, not considering interactions.

Cross Product Term

A term used in a regression model to represent the interaction between two predictor variables (e.g., X1 * X2).

Difference in Differences

A method to estimate the causal effect by comparing changes over time between a treatment group and a control group.

Dummy Variable

A binary variable used in regression models to represent categorical data.

Slope of a Predictor

The rate of change in the response variable per unit change in the predictor variable.

Collinearity

The correlation between predictor variables, which can lead to multicollinearity issues in regression models.

Statistical Significance

A statistical determination of whether observed effects in data are likely to be genuine rather than due to chance.

Response Variable

The main variable that you are trying to predict or explain in a regression model.

Categorical Predictor

A predictor variable with a limited number of distinct categories or groups.

Continuous Predictor

A predictor variable that can take any numerical value within a given range.

Model Fitting

The process of estimating the parameters of a regression model based on observed data.

Hypothesis Testing

A statistical method used to determine whether there is enough evidence to reject a null hypothesis.

Interpreting Beta Coefficients

Understanding the meaning of each beta coefficient in the context of the predictors and response variable.