Data involving two variables analyzed simultaneously to explore their relationship.

The proportion of observations that fall into a specific cell in a two-way table.

The proportion of observations in each category of a single variable.

The proportion of observations in a specific category of one variable, given a specific category of the other variable.

A measure of the strength and direction of a linear relationship between two quantitative variables, ranging from -1 to 1.

The proportion of variation in the dependent variable that is predictable from the independent variable.

The difference between the actual and predicted y-values in a regression analysis.

Residual = Actual y - Predicted y

<math-inline>\hat{y} = a + bx, where $\hat{y}$ is the predicted value, a is the y-intercept, b is the slope, and x is the independent variable.

<math-inline>\hat{y} = a + bx

A table that organizes categorical data to show the relationships between two categorical variables. It displays frequencies for each combination of categories.

A graph that displays the relationship between two quantitative variables. Each point on the scatterplot represents a pair of values for the two variables.

A statistical method used to model the relationship between two variables by fitting a linear equation to the observed data. It aims to find the line of best fit that minimizes the sum of squared residuals.

A strong correlation (close to -1 or 1) indicates that the points on a scatterplot cluster closely around a line. It suggests a strong linear relationship between the variables.

R² represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It indicates the goodness of fit of the regression model.

Just because two variables are related doesn't mean one causes the other. There could be lurking variables influencing both, leading to a spurious correlation.