Overview

Regression is the process of fitting models to data. The regression process depends on the model. If a model is parametric, regression estimates the parameters from the data. If a model is linear in the parameters, estimation is based on methods from linear algebra that minimize the norm of a residual vector. If a model is nonlinear in the parameters, estimation is based on search methods from optimization that minimize the norm of a residual vector.This book develops parametric regression techniques included in machine learning techniques of supervised analysis with continuous dependent variable and continuous independent variables. Specifically, he develops the linear and nonlinear regression model including the phases of identification, estimation, diagnosis and prediction.The more important topics are de next: - Parametric Regression Analysis - Linear Regression - Linear Regression Workflow - Regression Using Dataset Arrays - Regression Using Tables - Linear Regression with Interaction Effects - Interpret Linear Regression Results - Cook's Distance - Coefficient Standard Errors and Confidence Intervals - Coefficient of Determination (R-Squared) - Delete-1 Statistics - Durbin-Watson Test - F-statistic and t-statistic - Hat Matrix and Leverage - Residuals - Summary of Output and Diagnostic Statistics - Wilkinson Notation - Stepwise Regression - Robust Regression - Reduce Outlier Effects - Ridge Regression - Lasso and Elastic Net - Wide Data via Lasso and Parallel Computing - Lasso Regularization - Lasso and Elastic Net with Cross Validation - Partial Least Squares PLS - Linear Mixed-Effects Models - Prepare Data for Linear Mixed-Effects Models - Relationship Between Formula and Design Matrices - Estimating Parameters in Linear Mixed-Effects Models - Linear Mixed-Effects Model Workflow - Fit Mixed-Effects Spline Regression - Linear Mixed-Effects Model Workflow - Nonlinear Regression - Nonlinear Regression Workflow - Mixed effects in Nonlinear Regression Models

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9781797023670

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