STATISTICS with MATLAB. LINEAR REGRESSION MODEL

By L Marvin

Regression models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals This book develops the linear model of regression taking into account the stages of identification, estimation, diagnosis and prediction. The most important content is the following: -Parametric Regression Analysis -Choose a Regression Function -Linear Regression -Prepare Data -Choose a Fitting Method -Choose a Model or Range of Models -Fit Model to Data -Examine Quality and Adjust the Fitted Model -Predict or Simulate Responses to New Data -Share Fitted Models -Linear Regression Workflow -Linear Regression with Interaction Effects -Interpret Linear Regression Results -Cook's Distance -Coefficient Standard Errors and Confidence Intervals -Coefficient Covariance and Standard Errors -Coefficient Confidence Intervals -Coefficient of Determination (R-Squared) -Durbin-Watson Test -F-statistic -Assess Fit of Model Using F-statistic -t-statistic -Assess Significance of Regression Coefficients Using t-statistic -Hat Matrix and Leverage -Residuals -Assess Model Assumptions Using Residuals -Summary of Output and Diagnostic Statistics -Wilkinson Notation -Linear Mixed-Effects Model Examples -Generalized Linear Model Examples -Generalized Linear Mixed-Effects Model Examples -Repeated Measures Model Examples -Stepwise Regression -Stepwise Regression to Select Appropriate Models -Compare large and small stepwise models -Robust Regression - Reduce Outlier Effects -Robust Regression versus Standard Least-Squares Fit -Ridge Regression -Lasso and Elastic Net -Wide Data via Lasso and Parallel Computing -Partial Least Squares -Linear Mixed-Effects Models -Estimating Parameters in Linear Mixed-Effects Models -Fit Mixed-Effects Spline Regression

• Format: Paperback
• Language: English
• ISBN: 1979364095
• ISBN13: 9781979364096
• Release Date: January 1
• Publisher: Createspace Independent Publishing Platform
• Length: 210 Pages
• Weight: 1.05 lbs.
• Dimensions: 0.4" x 8.0" x 10.0"