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MATH FOR DATA SCIENCE • CORRELATION & REGRESSION
Correlation & Regression for Data Science
Clear concepts, clean notation, and exam-ready numericals. Start with correlation basics, build regression models, then move to polynomial, multiple regression, and the matrix method.
Correlation and Regression Notes and Numericals
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CORRELATION
Correlation Coefficient and Interpretation
LINEAR
Simple Linear Regression Step by Step
- The regression line \( \hat{y} = a + bx \): meaning of \(a\) and slope \(b\)
- Least squares idea: why we minimize \( \sum (y-\hat{y})^2 \)
- Normal equations: solving \(a\) and \(b\) with clear steps
- Residuals and model checking: what a good residual plot looks like
- Full regression analysis numericals (with \(R\) and \(R^2\))
POLYNOMIAL
Polynomial Regression and Overfitting
MULTIPLE
Multiple Linear Regression for Practical Data
MATRIX
Matrix Method for Regression
PERFORMANCE
Model Checking and Accuracy Measures
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