Index

A

• accuracy / Customer satisfaction analysis with the multiple logistic regression
• action of regressing / Going back to the origin of regression
• Akaike Information Criterion (AIC) / Simple logistic regression
• apodixis / Going back to the origin of regression
• area under curve (AUC) / Model fitting
• artificial neural networks (ANN) / Regression with neural networks

B

• BAS package
  • used, for creating Bayesian linear regression / Bayesian model using BAS package
• batch GD / Stochastic Gradient Descent
• Bayes' theorem / Bayes' theorem
• Bayesian Information Criterion (BIC) / Bayesian model using BAS package
• Bayesian linear regression
  • about / Bayesian linear regression
  • probability, basic concepts / Basic concepts of probability
  • Bayes' theorem / Bayes' theorem
  • creating, with BAS package / Bayesian model using BAS package
• binning
  • using, for discretization / Data discretization by binning
• BLR package
  • about / The BLR package
  • BLR / The BLR package
  • sets / The BLR package
• Boston dataset
  • random forest regression / Random forest regression with the Boston dataset
• Box-Cox power transformation / The MASS package
• breast cancer
  • classifying, with logistic regression / Classifying breast cancer using logistic regression

C

• caret package / The caret package
• caret package, functions
  • train / The caret package
  • trainControl / The caret package
  • varImp / The caret package
  • defaultSummary / The caret package
  • knnreg / The caret package
  • plotObsVsPred / The caret package
  • predict.knnreg / The caret package
• car package
  • ANOVA / The car package
  • linear.hypothesis / The car package
  • cookd / The car package
  • outlier.test / The car package
  • durbin.watson / The car package
  • levene.test / The car package
  • ncv.test / The car package
• categorical data
  • multiple logistic regression / Multiple logistic regression with categorical data
• categorical variables
  • about / Categorical variables
  • nominal variables / Categorical variables
  • dichotomous variables / Categorical variables
  • ordinal variables / Categorical variables
• certain event / Basic concepts of probability
• Classification and Regression Tree (CART) / Regression trees
• classification tree / Regression trees
• Comprehensive R Archive Network (CRAN)
  • URL / Installing R
• conditional probability / Basic concepts of probability
• Cook's distance / Diagnostic plots
• correlation
  • versus regression / Regression versus correlation
  • about / Association between variables – covariance and correlation
• count data model
  • about / Count data model
  • Poisson distributions / Poisson distributions
  • Poisson regression model / Poisson regression model
  • warp breaks per loom, modeling / Modeling the number of warp breaks per loom
• covariance / Association between variables – covariance and correlation
• cross-validation
  • used, for overfitting detection / Overfitting detection – cross-validation
  • k-fold / Overfitting detection – cross-validation
  • Leave-one-out cross-validation (LOOCV) / Overfitting detection – cross-validation
• Cumulative Distribution / Multivariate Adaptive Regression Splines
• customer satisfaction analysis
  • with multiple logistic regression / Customer satisfaction analysis with the multiple logistic regression

D

• data scaling / Scale of features
• data wrangling
  • about / Data wrangling
  • data, viewing / A first look at data
  • datatype, modifying / Change datatype
  • empty cells, removing / Removing empty cells
  • incorrect value, replacing / Replace incorrect value
  • missing values / Missing values           
  • NaN values / Treatment of NaN values
• decision trees / Regression trees
• deduction / Understanding regression concepts
• Department of Transportation (DOT) / Creating a linear regression model
• dependent / Regression versus correlation
• diagnostic plots / Diagnostic plots
• dimensionality reduction
  • about / Dimensionality reduction
  • principal component analysis / Principal Component Analysis
• discretization
  • about / Discretization in R
  • by binning / Data discretization by binning
  • by histogram analysis / Data discretization by histogram analysis
• dummy coding / Discovering different types of regression

E

• Earth / Multivariate Adaptive Regression Splines
• ElasticNet regression / ElasticNet regression
• exponential family / Generalized Linear Model

F

• feature scaling
  • about / Scale of features, Exploratory analysis
  • min-max normalization / Min–max normalization
  • z score standardization / z score standardization
• feature selection
  • about / Feature selection
  • stepwise regression / Stepwise regression
  • regression subset selection / Regression subset selection
• Federal Highway Administration (FHWA) / Creating a linear regression model
• File Transfer Protocol (FTP) / Installing R
• Fitted values / Diagnostic plots
• Free Software Foundation's (FSF) / The R environment
• Froude number / Stepwise regression

G

• Galton universal regression law / Going back to the origin of regression
• Generalized Additive Model (GAM) / Generalized Additive Model
• Generalized Cross Validation (GCV) / Multivariate Adaptive Regression Splines
• generalized least squares (GLS) / The MASS package, Robust linear regression
• Generalized Linear Model (GLM)
  • about / The R stats package, Generalized Linear Model, Ridge regression, Modeling the number of warp breaks per loom
  • simple logistic regression / Simple logistic regression
• Generalized Ridge Regression (GRR) / Generalized Additive Model
• General Public License (GPL) / The R environment
• glmnet package, function
  • glmnet / The glmnet package
  • glmnet.control / The glmnet package
  • predict.glmnet / The glmnet package
  • print.glmnet / The glmnet package
  • plot.glmnet / The glmnet package
  • deviance.glmnet / The glmnet package
• globally convergent version (GRPROP) / Neural network model
• Gradient Descent (GD) / Gradient Descent and linear regression, Gradient Descent

H

• histogram analysis
  • using, for discretization / Data discretization by histogram analysis

I

• impossible event / Basic concepts of probability
• independent / Regression versus correlation
• indicator variables / Discovering different types of regression
• induction / Understanding regression concepts
• inference / Understanding regression concepts
• Integrated Development Environment (IDE) / RStudio
• interquartile range (IQR) / Finding outliers in data
• iteratively reweighted least squares (IRLS) / Robust linear regression

K

• K-Nearest Neighbor (KNN) regression / The caret package

L

• Lars package
  • about / The Lars package
  • lars / The Lars package
  • summary.lars / The Lars package
  • plot.lars / The Lars package
  • predict.lars / The Lars package
• lasso regression / Lasso regression
• least absolute deviations / Lasso regression
• least absolute errors / Lasso regression
• least squares / Lasso regression
• least squares regression / Least squares regression
• Leverage / Diagnostic plots
• linear regression
  • with SGD / Linear regression with SGD
• linear regression model
  • creating / Creating a linear regression model
  • statistical significance test / Statistical significance test
  • model results, exploring / Exploring model results
  • diagnostic plots / Diagnostic plots
  • about / Gradient Descent and linear regression, Robust linear regression
  • with SGD / Linear regression with SGD
• linear relationships
  • searching / Searching linear relationships
• lobules / Classifying breast cancer using logistic regression
• log-Iteration / Linear regression with SGD
• log-linear model / Count data model
• log-odds / The logit model
• logistic regression
  • about / Understanding logistic regression
  • logit model / The logit model
  • used, for classifying breast cancer / Classifying breast cancer using logistic regression
  • exploratory analysis / Exploratory analysis
  • model, fitting / Model fitting
• logit / The logit model
• Log Posterior Odds / Bayesian model using BAS package
• Lowess curve / Multivariate Adaptive Regression Splines

M

• marginal likelihood / Bayes' theorem
• MARS model equation / Multivariate Adaptive Regression Splines
• Mean Square Error (MSE) / Linear regression with SGD, Overfitting detection – cross-validation, Multivariate Adaptive Regression Splines, Multiple linear model fitting
• milk ducts / Classifying breast cancer using logistic regression
• min-max normalization / Min–max normalization
• model
  • building / Building a model
• model results
  • exploring / Exploring model results
• Model Selection / Multivariate Adaptive Regression Splines
• multicollinearity / Ridge regression
• multinomial logistic regression / Multinomial logistic regression
• multiple linear model
  • fitting / Multiple linear model fitting
• Multiple Linear Regression (MLR) model
  • concepts / Multiple linear regression concepts
  • building / Building a multiple linear regression model
  • with categorical predictor / Multiple linear regression with categorical predictor
  • categorical variables / Categorical variables
  • model, building / Building a model
  • about / Bayesian linear regression
• multiple logistic regression
  • about / Multiple logistic regression
  • customer satisfaction analysis / Customer satisfaction analysis with the multiple logistic regression
  • with customer satisfaction analysis / Customer satisfaction analysis with the multiple logistic regression
  • with categorical data / Multiple logistic regression with categorical data
• Multivariate Adaptive Regression Splines (MARS) / Multivariate Adaptive Regression Splines
• multivariate models / Discovering different types of regression
• multivariate multiple regression / Discovering different types of regression

N

• National Weather Service (NWS) / Min–max normalization
• neural networks
  • using, for regression / Regression with neural networks
  • exploratory analysis / Exploratory analysis
  • about / Neural network model
• nonlinear least squares / Nonlinear least squares
• nonlinear least squares, arguments
  • about / Nonlinear least squares
  • formula / Nonlinear least squares
  • data / Nonlinear least squares
  • start / Nonlinear least squares
  • control / Nonlinear least squares
  • algorithm / Nonlinear least squares
  • trace / Nonlinear least squares
  • subset / Nonlinear least squares
  • weights / Nonlinear least squares
  • na.action / Nonlinear least squares
  • model / Nonlinear least squares
  • upper bounds / Nonlinear least squares
  • lower bounds / Nonlinear least squares
• Normal Q-Q plot / Building a multiple linear regression model
• Not a Number (NaN) / A first look at data
• null hypothesis / Statistical significance test

O

• Ordinary Least Squares (OLS) / Ridge regression, Robust linear regression, Nonlinear least squares
• outliers
  • searching, in data / Finding outliers in data
  • about / Robust linear regression
• overfitting
  • about / Understanding overfitting
  • detection, with cross-validation / Overfitting detection – cross-validation

P

• partial / Multiple logistic regression
• penalized quasi-likelihood (PQL) / The MASS package
• perfect linear association
  • modeling / Modeling a perfect linear association
• Poisson distributions / Poisson distributions
• Poisson regression model / Poisson regression model
• polynomial regression / Polynomial regression
• Posterior Inclusion Probabilities (pip) / Bayesian model using BAS package
• posterior probability / Bayes' theorem
• precision / Customer satisfaction analysis with the multiple logistic regression
• Principal Component Analysis (PCA) / Principal Component Analysis
• principal components / Principal Component Analysis
• prior probability / Bayes' theorem
• probability
  • basic concepts / Basic concepts of probability

R

• R
  • installing / Installing R
  • precompiled binary distribution, using / Using precompiled binary distribution
  • installing, on Windows / Installing on Windows
  • installing, on macOS / Installing on macOS
  • installing, on Linux / Installing on Linux
  • source code, installation / Installation from source code
• random forest regression
  • with Boston dataset / Random forest regression with the Boston dataset
  • exploratory analysis / Exploratory analysis
  • multiple linear model, fitting / Multiple linear model fitting
  • about / Random forest regression model
• Receiver Operator Characteristic (ROC) / Model fitting
• regression
  • origin / Going back to the origin of regression
  • applications / Regression in the real world
  • about / Understanding regression concepts
  • versus correlation / Regression versus correlation
  • types / Discovering different types of regression
  • R packages, using / R packages for regression
  • with neural networks / Regression with neural networks
• regression subset selection / Regression subset selection
• regression towards mediocrity / Going back to the origin of regression
• regression tree
  • about / Regression trees
  • splitting / Regression trees
  • pruning / Regression trees
  • tree selection / Regression trees
• regularization
  • about / Regularization
  • ridge regression / Ridge regression
  • lasso regression / Lasso regression
  • ElasticNet regression / ElasticNet regression
• R environment / The R environment
• resampling
  • bootstrap resampling / Overfitting detection – cross-validation
• Residual QQ / Multivariate Adaptive Regression Splines
• Residuals / Diagnostic plots
• Residual Sum of Squares (RSS) / Ridge regression, Multivariate Adaptive Regression Splines
• Residuals vs Fitted values / Multivariate Adaptive Regression Splines
• resilient backpropagation (RPROP) / Neural network model
• ridge regression / Ridge regression
• Road Casualties Great Britain (RCGB) / Ridge regression
• Road Traffic Accidents (RTA) / Ridge regression
• robust linear regression / Robust linear regression
• Robust Regression Model / Robust linear regression
• R packages
  • using, for regression / R packages for regression
  • R stats package / The R stats package
  • car package / The car package
  • caret package / The caret package
  • glmnet package / The glmnet package
  • sgd package / The sgd package
  • BLR package / The BLR package
  • Lars package / The Lars package
• RStudio
  • about / RStudio
  • URL / RStudio

S

• sensitivity / Model fitting
• sgd package / The sgd package
• sgd package, functions
  • sgd / The sgd package
  • print.sgd / The sgd package
  • predict.sgd / The sgd package
  • plot.sgd / The sgd package
• simple logistic regression / Simple logistic regression
• smallest absolute gradient (sag) / Neural network model
• smallest learning rate (slr) / Neural network model
• specificity / Customer satisfaction analysis with the multiple logistic regression, Model fitting
• Standardized residuals / Diagnostic plots
• standard score / z score standardization, Neural network model
• statistical significance test / Statistical significance test
• stepwise regression
  • about / Stepwise regression
  • forward method / Stepwise regression
  • backward method / Stepwise regression
  • stepwise method / Stepwise regression
• stochastic / Stochastic Gradient Descent
• Stochastic Gradient Descent (SGD) / The sgd package, Stochastic Gradient Descent
• Support Vector Machine (SVM) / Support Vector Regression

T

• Theoretical Quantiles / Diagnostic plots
• TIOBE
  • URL / The R environment
• True Negative Rate (TNR) / Customer satisfaction analysis with the multiple logistic regression, Model fitting
• true positive rate (TPR) / Model fitting

U

• UCI Machine Learning Repository
  • URL / Regression with neural networks

V

• variables
  • relationships / Association between variables – covariance and correlation

Z

• z score standardization / z score standardization, Neural network model