What Is Analytics? The Emergence and Application of Analytics Similarities with and Dissimilarities from Classical Statistical Analysis Theory versus Computational Power Fact versus Knowledge: Report versus Prediction Actionable Insight Suggested Further Reading Introducing R--An Analytics Software Basic System of R Reading, Writing, and Extracting Data in R Statistics in R Graphics in R Further Notes about R Suggested Further Reading Reporting Data What Is Data? Types of Data Data Collection and Presentation Reporting Current Status Measures of Association for Categorical Variables Suggested Further Reading Statistical Graphics and Visual Analytics Univariate and Bivariate Visualization Multivariate Visualization Mapping Techniques Scopes and Challenges of Visualization Suggested Further Reading Probability Basic Set Theory The Classical Definition of Probability Counting Rules Axiomatic Definition of Probability Conditional Probability and Independence The Bayes Theorem Comprehensive Example Appendix Suggested Further Reading Random Variables and Probability Distributions Discrete and Continuous Random Variables Some Special Discrete Distributions Distribution Functions Bivariate and Multivariate Distributions Expectation Appendix Suggested Further Reading Continuous Random Variables The PDF and the CDF Special Continuous Distributions Expectation The Normal Distribution Continuous Bivariate Distributions Independence The Bivariate Normal Distribution Sampling Distributions The Central Limit Theorem Sampling Distributions Arising from the Normal Random Samples from Two Independent Normal Distributions Normal Q-Q Plots Summary Appendix Suggested Further Reading Statistical Inference Inference about a Single Mean Single Population Mean with Unknown Variance Two Sample t-test: Independent Samples Two Sample t-test: Dependent (Paired) Samples Analysis of Variance Chi-Square Tests Inference about Proportions Appendix Suggested Further Reading Regression for Predictive Model Building Simple Linear Regression Multiple Linear Regression ANOVA for Multiple Linear Regression Hypotheses of Interest in Multiple Linear Regression Interaction Regression Diagnostics Regression Model Building Other Regression Techniques Logistic Regression Interpreting Logistic Regression Model Interpretation and Inference for Logistic Regression Goodness of Fit for the Logistic Regression Model Hosmer-Lemeshow Statistics Classification Table and ROC Curve Suggested Further Reading Decision Trees Algorithm for Tree-Based Methods Impurity Measures Pruning a Tree Aggregation Method: Bagging Random Forest Variable Importance Decision Tree and Interaction among Predictors Suggested Further Reading Data Mining and Multivariate Methods Dimension Reduction Technique: Principal Component Analysis Factor Analysis Classification Problem Discriminant Analysis Clustering Problem Suggested Further Reading Modeling Time Series Data for Forecasting Characteristics and Components of Time Series Data Time Series Decomposition Autoregression Models Forecasting Time Series Data Other Time Series Suggested Further Reading e and Bivariate Visualization Multivariate Visualization Mapping Techniques Scopes and Challenges of Visualization Suggested Further Reading Probability Basic Set Theory The Classical Definition of Probability Counting Rules Axiomatic Definition of Probability Conditional Probability and Independence The Bayes Theorem Comprehensive Example Appendix Suggested Further Reading Random Variables and Probability Distributions Discrete and Continuous Random Variables Some Special Discrete Distributions Distribution Functions Bivariate and Multivariate Distributions Expectation Appendix Suggested Further Reading Continuous Random Variables The PDF and the CDF Special Continuous Distributions Expectation The Normal Distribution Continuous Bivariate Distributions Independence The Bivariate Normal Distribution Sampling Distributions The Central Limit Theorem Sampling Distributions Arising from the Normal Random Samples from Two Independent Normal Distributions Normal Q-Q Plots Summary Appendix Suggested Further Reading Statistical Inference Inference about a Single Mean Single Population Mean with Unknown Variance Two Sample t-test: Independent Samples Two Sample t-test: Dependent (Paired) Samples Analysis of Variance Chi-Square Tests Inference about Proportions Appendix Suggested Further Reading Regression for Predictive Model Building Simple Linear Regression Multiple Linear Regression ANOVA for Multiple Linear Regression Hypotheses of Interest in Multiple Linear Regression Interaction Regression Diagnostics Regression Model Building Other Regression Techniques Logistic Regression Interpreting Logistic Regression Model Interpretation and Inference for Logistic Regression Goodness of Fit for the Logistic Regression Model Hosmer-Lemeshow Statistics Classification Table and ROC Curve Suggested Further Reading Decision Trees Algorithm for Tree-Based Methods Impurity Measures Pruning a Tree Aggregation Method: Bagging Random Forest Variable Importance Decision Tree and Interaction among Predictors Suggested Further Reading Data Mining and Multivariate Methods Dimension Reduction Technique: Principal Component Analysis Factor Analysis Classification Problem Discriminant Analysis Clustering Problem Suggested Further Reading Modeling Time Series Data for Forecasting Characteristics and Components of Time Series Data Time Series Decomposition Autoregression Models Forecasting Time Series Data Other Time Series Suggested Further ReadingDistributions Expectation The Normal Distribution Continuous Bivariate Distributions Independence The Bivariate Normal Distribution Sampling Distributions The Central Limit Theorem Sampling Distributions Arising from the Normal Random Samples from Two Independent Normal Distributions Normal Q-Q Plots Summary Appendix Suggested Further Reading Statistical Inference Inference about a Single Mean Single Population Mean with Unknown Variance Two Sample t-test: Independent Samples Two Sample t-test: Dependent (Paired) Samples Analysis of Variance Chi-Square Tests Inference about Proportions Appendix Suggested Further Reading Regression for Predictive Model Building Simple Linear Regression Multiple Linear Regression ANOVA for Multiple Linear Regression Hypotheses of Interest in Multiple Linear Regression Interaction Regression Diagnostics Regression Model Building Other Regression Techniques Logistic Regression Interpreting Logistic Regression Model Interpretation and Inference for Logistic Regression Goodness of Fit for the Logistic Regression Model Hosmer-Lemeshow Statistics Classification Table and ROC Curve Suggested Further Reading Decision Trees Algorithm for Tree-Based Methods Impurity Measures Pruning a Tree Aggregation Method: Bagging Random Forest Variable Importance Decision Tree and Interaction among Predictors Suggested Further Reading Data Mining and Multivariate Methods Dimension Reduction Technique: Principal Component Analysis Factor Analysis Classification Problem Discriminant Analysis Clustering Problem Suggested Further Reading Modeling Time Series Data for Forecasting Characteristics and Components of Time Series Data Time Series Decomposition Autoregression Models Forecasting Time Series Data Other Time Series Suggested Further Readingple Linear Regression ANOVA for Multiple Linear Regression Hypotheses of Interest in Multiple Linear Regression Interaction Regression Diagnostics Regression Model Building Other Regression Techniques Logistic Regression Interpreting Logistic Regression Model Interpretation and Inference for Logistic Regression Goodness of Fit for the Logistic Regression Model Hosmer-Lemeshow Statistics Classification Table and ROC Curve Suggested Further Reading Decision Trees Algorithm for Tree-Based Methods Impurity Measures Pruning a Tree Aggregation Method: Bagging Random Forest Variable Importance Decision Tree and Interacti.