Simple Linear Regression in Python - Study Notes

These flashcards cover the key concepts, definitions, and terminology related to Simple Linear Regression as outlined in the study notes.

What is Regression?

A method that predicts a numeric value based on input variables.

Independent variable (X)

The input variable, such as Hours Studied.

Dependent variable (y)

The output variable, such as Exam Score.

Residual

The error calculated as the difference between actual and predicted values, expressed as y - predicted value.

Importing Libraries

The process of including necessary libraries for data manipulation and analysis in Python, such as numpy, pandas, matplotlib, and sklearn.

Dataset

A collection of data structured in a DataFrame format for analysis, in this case representing hours studied and exam scores.

Model Formula

The equation used in linear regression: predicted value = b0 + b1 * X.

Coefficients

The parameters of the model where b0 represents the intercept and b1 represents the slope.

R-squared

A statistical measure that indicates how well the model explains the variation in the dependent variable.

Correlation

A statistical measure (r) that indicates the strength and direction of the linear relationship between two variables.

Visualization

The graphical representation of data, often using plots, to better understand relationships in data.

Residuals

The differences between actual and predicted values in regression analysis.

Interpolation vs Extrapolation

Interpolation involves predictions within the data range, while extrapolation involves predictions outside the data range, which are less reliable.