Practical Statistics for Data Scientists. 50+ Essential Concepts Using R and Python. 2nd Edition

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• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Exploratory Data Analysis
  • Elements of Structured Data
    • Further Reading
  • Rectangular Data
    • Data Frames and Indexes
    • Nonrectangular Data Structures
    • Further Reading
  • Estimates of Location
    • Mean
    • Median and Robust Estimates
      • Outliers
    • Example: Location Estimates of Population and Murder Rates
    • Further Reading
  • Estimates of Variability
    • Standard Deviation and Related Estimates
    • Estimates Based on Percentiles
    • Example: Variability Estimates of State Population
    • Further Reading
  • Exploring the Data Distribution
    • Percentiles and Boxplots
    • Frequency Tables and Histograms
    • Density Plots and Estimates
    • Further Reading
  • Exploring Binary and Categorical Data
    • Mode
    • Expected Value
    • Probability
    • Further Reading
  • Correlation
    • Scatterplots
    • Further Reading
  • Exploring Two or More Variables
    • Hexagonal Binning and Contours (Plotting Numeric Versus Numeric Data)
    • Two Categorical Variables
    • Categorical and Numeric Data
    • Visualizing Multiple Variables
    • Further Reading
  • Summary
• 2. Data and Sampling Distributions
  • Random Sampling and Sample Bias
    • Bias
    • Random Selection
    • Size Versus Quality: When Does Size Matter?
    • Sample Mean Versus Population Mean
    • Further Reading
  • Selection Bias
    • Regression to the Mean
    • Further Reading
  • Sampling Distribution of a Statistic
    • Central Limit Theorem
    • Standard Error
    • Further Reading
  • The Bootstrap
    • Resampling Versus Bootstrapping
    • Further Reading
  • Confidence Intervals
    • Further Reading
  • Normal Distribution
    • Standard Normal and QQ-Plots
  • Long-Tailed Distributions
    • Further Reading
  • Students t-Distribution
    • Further Reading
  • Binomial Distribution
    • Further Reading
  • Chi-Square Distribution
    • Further Reading
  • F-Distribution
    • Further Reading
  • Poisson and Related Distributions
    • Poisson Distributions
    • Exponential Distribution
    • Estimating the Failure Rate
    • Weibull Distribution
    • Further Reading
  • Summary
• 3. Statistical Experiments and Significance Testing
  • A/B Testing
    • Why Have a Control Group?
    • Why Just A/B? Why Not C, D,?
    • Further Reading
  • Hypothesis Tests
    • The Null Hypothesis
    • Alternative Hypothesis
    • One-Way Versus Two-Way Hypothesis Tests
    • Further Reading
  • Resampling
    • Permutation Test
    • Example: Web Stickiness
    • Exhaustive and Bootstrap Permutation Tests
    • Permutation Tests: The Bottom Line for Data Science
    • Further Reading
  • Statistical Significance and p-Values
    • p-Value
    • Alpha
      • p-value controversy
      • Practical significance
    • Type 1 and Type 2 Errors
    • Data Science and p-Values
    • Further Reading
  • t-Tests
    • Further Reading
  • Multiple Testing
    • Further Reading
  • Degrees of Freedom
    • Further Reading
  • ANOVA
    • F-Statistic
    • Two-Way ANOVA
    • Further Reading
  • Chi-Square Test
    • Chi-Square Test: A Resampling Approach
    • Chi-Square Test: Statistical Theory
    • Fishers Exact Test
    • Relevance for Data Science
    • Further Reading
  • Multi-Arm Bandit Algorithm
    • Further Reading
  • Power and Sample Size
    • Sample Size
    • Further Reading
  • Summary
• 4. Regression and Prediction
  • Simple Linear Regression
    • The Regression Equation
    • Fitted Values and Residuals
    • Least Squares
    • Prediction Versus Explanation (Profiling)
    • Further Reading
  • Multiple Linear Regression
    • Example: King County Housing Data
    • Assessing the Model
    • Cross-Validation
    • Model Selection and Stepwise Regression
    • Weighted Regression
    • Further Reading
  • Prediction Using Regression
    • The Dangers of Extrapolation
    • Confidence and Prediction Intervals
  • Factor Variables in Regression
    • Dummy Variables Representation
    • Factor Variables with Many Levels
    • Ordered Factor Variables
  • Interpreting the Regression Equation
    • Correlated Predictors
    • Multicollinearity
    • Confounding Variables
    • Interactions and Main Effects
  • Regression Diagnostics
    • Outliers
    • Influential Values
    • Heteroskedasticity, Non-Normality, and Correlated Errors
    • Partial Residual Plots and Nonlinearity
  • Polynomial and Spline Regression
    • Polynomial
    • Splines
    • Generalized Additive Models
    • Further Reading
  • Summary
• 5. Classification
  • Naive Bayes
    • Why Exact Bayesian Classification Is Impractical
    • The Naive Solution
    • Numeric Predictor Variables
    • Further Reading
  • Discriminant Analysis
    • Covariance Matrix
    • Fishers Linear Discriminant
    • A Simple Example
    • Further Reading
  • Logistic Regression
    • Logistic Response Function and Logit
    • Logistic Regression and the GLM
    • Generalized Linear Models
    • Predicted Values from Logistic Regression
    • Interpreting the Coefficients and Odds Ratios
    • Linear and Logistic Regression: Similarities and Differences
      • Fitting the model
    • Assessing the Model
      • Analysis of residuals
    • Further Reading
  • Evaluating Classification Models
    • Confusion Matrix
    • The Rare Class Problem
    • Precision, Recall, and Specificity
    • ROC Curve
    • AUC
    • Lift
    • Further Reading
  • Strategies for Imbalanced Data
    • Undersampling
    • Oversampling and Up/Down Weighting
    • Data Generation
    • Cost-Based Classification
    • Exploring the Predictions
    • Further Reading
  • Summary
• 6. Statistical Machine Learning
  • K-Nearest Neighbors
    • A Small Example: Predicting Loan Default
    • Distance Metrics
    • One Hot Encoder
    • Standardization (Normalization, z-Scores)
    • Choosing K
    • KNN as a Feature Engine
  • Tree Models
    • A Simple Example
    • The Recursive Partitioning Algorithm
    • Measuring Homogeneity or Impurity
    • Stopping the Tree from Growing
      • Controlling tree complexity in R
      • Controlling tree complexity in Python
    • Predicting a Continuous Value
    • How Trees Are Used
    • Further Reading
  • Bagging and the Random Forest
    • Bagging
    • Random Forest
    • Variable Importance
    • Hyperparameters
  • Boosting
    • The Boosting Algorithm
    • XGBoost
    • Regularization: Avoiding Overfitting
    • Hyperparameters and Cross-Validation
  • Summary
• 7. Unsupervised Learning
  • Principal Components Analysis
    • A Simple Example
    • Computing the Principal Components
    • Interpreting Principal Components
    • Correspondence Analysis
    • Further Reading
  • K-Means Clustering
    • A Simple Example
    • K-Means Algorithm
    • Interpreting the Clusters
    • Selecting the Number of Clusters
  • Hierarchical Clustering
    • A Simple Example
    • The Dendrogram
    • The Agglomerative Algorithm
    • Measures of Dissimilarity
  • Model-Based Clustering
    • Multivariate Normal Distribution
    • Mixtures of Normals
    • Selecting the Number of Clusters
    • Further Reading
  • Scaling and Categorical Variables
    • Scaling the Variables
    • Dominant Variables
    • Categorical Data and Gowers Distance
    • Problems with Clustering Mixed Data
  • Summary
• Bibliography
• Index

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