Week 7: Simple Linear Regression

These flashcards cover key vocabulary terms and definitions related to simple linear regression from the COMP1006 Engineering Programming lecture.

Regression

A statistical method for estimating the relationships among variables.

Dependent variable

Also known as the response variable, it is the outcome that is being predicted or explained.

Independent variable

Also known as a predictor variable, it is the variable that is manipulated or categorized to observe its effect on the dependent variable.

Predictor

A variable used to predict the outcome of another variable.

Model

A mathematical representation of the relationship between variables in regression analysis.

Residuals

The difference between the observed values and the predicted values in regression analysis.

Ordinary least squares (OLS)

A method used in regression analysis to estimate the parameters of the model by minimizing the sum of squared residuals.

Coefficient

A numerical value that represents the relationship between an independent variable and the dependent variable in a regression model.

Residual sum of squares (RSS)

A measure of the discrepancy between the data and the estimation model, calculated as the sum of the squares of residuals.

Affine model

A model that represents a linear relationship between two variables, usually expressed in the form y = β0 + β1x.

R-squared value (R²)

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

Prediction error

The difference between the observed value and the predicted value.

Classification

The process of predicting the category or class of an object based on its features.

Scatter plot

A graphical depiction of the relationship between two quantitative variables.

Normalised RSS

The ratio of the residual sum of squares to the total sum of squares, bounded between 0 and 1.