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Regression Analysis with R

(REG-R.AJ1) / ISBN : 978-1-61691-689-3
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About This Course

Regression Analysis with R course and lab will give you a rundown explaining what regression analysis is, explaining you the process from scratch. Lab simulates real-world, hardware, software, and command-line interface environments and can be mapped to any textbook, course, or training. The course and lab provide you with the knowledge to work with multiple regression in action, logistic regression, data preparation, achieving generalization, online and batch learning; and many more.

Skills You’ll Get

Download Course Outline

1

Preface

  • What this course covers
  • To get the most out of this course
  • Conventions used
2

Getting Started with Regression

  • Going back to the origin of regression
  • Regression in the real world
  • Understanding regression concepts
  • Regression versus correlation
  • Discovering different types of regression
  • The R environment
  • Installing R
  • RStudio
  • R packages for regression
  • Summary
3

Basic Concepts – Simple Linear Regression

  • Association between variables – covariance and correlation
  • Searching linear relationships
  • Least squares regression
  • Creating a linear regression model
  • Modeling a perfect linear association
  • Summary
4

More Than Just One Predictor – MLR

  • Multiple linear regression concepts
  • Building a multiple linear regression model
  • Multiple linear regression with categorical predictor
  • Gradient Descent and linear regression
  • Polynomial regression
  • Summary
5

When the Response Falls into Two Categories – Logistic Regression

  • Understanding logistic regression
  • Generalized Linear Model
  • Multiple logistic regression
  • Multinomial logistic regression
  • Summary
6

Data Preparation Using R Tools

  • Data wrangling
  • Finding outliers in data
  • Scale of features
  • Discretization in R
  • Dimensionality reduction
  • Summary
7

Avoiding Overfitting Problems - Achieving Generalization

  • Understanding overfitting
  • Feature selection
  • Regularization
  • Summary
8

Going Further with Regression Models

  • Robust linear regression
  • Bayesian linear regression
  • Count data model
  • Summary
9

Beyond Linearity – When Curving Is Much Better

  • Nonlinear least squares
  • Multivariate Adaptive Regression Splines
  • Generalized Additive Model
  • Regression trees
  • Support Vector Regression
  • Summary
10

Regression Analysis in Practice

  • Random forest regression with the Boston dataset
  • Classifying breast cancer using logistic regression
  • Regression with neural networks
  • Summary

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