Series
Uncover insights with statistics! From basics to advanced concepts like hypothesis testing and data modeling, explore methods to understand data distributions and make data-driven decisions.
When tasked with fitting a line to data, least squares regression is often the first technique analysts reach for. Its simplicity, ease of implementation, and widespread acceptance make it a cornerstone of statistical modeling. But is it always the b...
Table of Content 7 Moving Beyond Linearity 7.1 Polynomial Regression 7.2 Step Functions 7.3 Basis Functions 7.4 Regression Splines 7.4.1 Piecewise Polynomials 7.4.2 Choosing the Number and Locations of the Knots 7.5 Smoothing Splines ...
Table of Content 6. Linear Model Selection and Regularization 6.1 Subset Selection 6.1.1 Best Subset Selection 6.1.2 Stepwise Selection 6.1.3 Choosing the Optimal Model 6.2 Shrinkage Methods 6.2.1 Ridge Regression 6.2.2 The Lasso 6.3 Dimensi...
Table of Content 5. Resampling Methods 5.1 Cross-Validation 5.1.1 The Validation Set Approach 5.1.2 Leave-One-Out Cross-Validation 5.1.3 k-Fold Cross-Validation 5.1.4 Bias-Variance Trade-Off for k-Fold 5.1.5 Cross-Validation on Classifi...
Table of Content 4. Classification 4.1 An Overview of Classification 4.2 Why Not Linear Regression? 4.3 Logistic Regression 4.4 Generative models for classification 4.5 Comparison of Classification Methods 4.6 Generalized Linear Models ...
Table of Content 3. Linear Regression 3.1 Simple Linear Regression 3.1.1 Estimating the Coefficients 3.1.2 Assessing the Accuracy of the Coefficient Estimates 3.1.3 Assessing the Accuracy of the Model 3.2 Multiple Linear Regression 3.2.1 Estim...
