Discovering Statistics and Data
by James S. Hawkes
This text introduces students to the modernday data revolution and future career opportunities surrounding data analysis and processing. Students learn how to measure data, where it comes from, how to visualize it, and how it applies to their everyday lives.
New features include expanded exercises and examples; virtual simulations and games; over 15 large, real data sets for download; modernized pedagogy, including GAISE guidelines; and more.
Companion Website
A curated list of resources for this title is available at stat.hawkeslearning.com. This companion website features:

Free, realworld data sets

Stepbystep instructions for TI calculators, Excel, Minitab^{®}, SPSS, JMP, and Rguroo

Chapter projects that apply lessons to reallife scenarios

Links to data visualization resources

External videos explaining key topics
Bundling options include Minitab^{®}, SPSS, JMP, and Rguroo.
Formats: Software, Textbook, eBook
Product  ISBN 

Software + eBook  978‑1‑64277‑015‑5 
Software + eBook + Textbook  978‑1‑64277‑011‑7 
Table of Contents

Chapter 1: Statistics and Problem Solving
 1.1 The Meaning of Data
 1.2 Statistics as a Career
 1.3 The Data Explosion
 1.4 The Fusion of Data, Computing, and Statistics
 1.5 Big Data
 1.6 Introduction to Statistical Thinking
 1.7 Descriptive vs. Inferential Statistics
 1.8 The Consequences of Statistical Illiteracy

Chapter 2: Data, Reality, and Problem Solving
 2.1 The Lords of Data
 2.2 Data Classification
 2.3 Time Series Data vs. CrossSectional Data

Chapter 3: Visualizing Data
 3.1 Frequency Distributions
 3.2 Displaying Qualitative Data Graphically
 3.3 Constructing Frequency Distributions for Quantitative Data
 3.4 Histograms and Other Graphical Displays of Quantitative Data
 3.5 Analyzing Graphs

Chapter 4: Describing and Summarizing Data from One Variable
 4.1 Measures of Location
 4.2 Measures of Dispersion
 4.3 Measures of Relative Position, Box Plots, and Outliers
 4.4 Data Subsetting
 4.5 Analyzing Grouped Data
 4.6 Proportions and Percentages

Chapter 5: Discovering Relationships
 5.1 Scatterplots and Correlation
 5.2 Fitting a Linear Model
 5.3 Evaluating the Fit of a Linear Model
 5.4 Fitting a Linear Time Trend
 5.5 Scatterplots for More Than Two Variables

Chapter 6: Probability, Randomness, and Uncertainty
 6.1 Introduction to Probability
 6.2 Addition Rules for Probability
 6.3 Multiplication Rules for Probability
 6.4 Combinations and Permutations
 6.5 Bayes’ Theorem

Chapter 7: Discrete Probability Distributions
 7.1 Types of Random Variables
 7.2 Discrete Random Variables
 7.3 The Discrete Uniform Distribution
 7.4 The Binomial Distribution
 7.5 The Poisson Distribution
 7.6 The Hypergeometric Distribution

Chapter 8: Continuous Probability Distributions
 8.1 The Uniform Distribution
 8.2 The Normal Distribution
 8.3 The Standard Normal Distribution
 8.4 Applications of the Normal Distribution
 8.5 Assessing Normality
 8.6 Approximation to the Binomial Distribution

Chapter 9: Samples and Sampling Distributions
 9.1 Random Samples
 9.2 Introduction to Sampling Distributions
 9.3 The Distribution of the Sample Mean and the
 Central Limit Theorem
 9.4 The Distribution of the Sample Proportion
 9.5 Other Forms of Sampling

Chapter 10: Estimation: Single Samples
 10.1 Point Estimation of the Population Mean
 10.2 Interval Estimation of the Population Mean
 10.3 Estimating the Population Proportion
 10.4 Estimating the Population Standard Deviation or Variance

Chapter 11: Hypothesis Testing: Single Samples
 11.1 Introduction to Hypothesis Testing
 11.2 Testing a Hypothesis about a Population Mean with σ Known and Unknown
 11.3 The Relationship Between Confidence Interval Estimation and Hypothesis Testing
 11.4 Testing a Hypothesis about a Population Proportion
 11.5 Testing a Hypothesis about a Population Standard Deviation or Variance
 11.6 Practical Significance vs. Statistical Significance

Chapter 12: Inferences about Two Samples
 12.1 Inference about Two Means: Independent Samples
 12.2 Inference about Two Means: Dependent Samples (Paired Difference)
 12.3 Inference about Two Population Proportions

Chapter 13: Regression, Inference, and Model Building
 13.1 Assumptions of the Simple Linear Model
 13.2 Inference Concerning β_{1}
 13.3 Inference Concerning the Model’s Prediction

Chapter 14: Multiple Regression
 14.1 The Multiple Regression Model
 14.2 The Coefficient of Determination and Adjusted R^{2}
 14.3 Interpreting the Coefficients of the Multiple Regression Model
 14.4 Inference Concerning the Multiple Regression Model and Its Coefficients
 14.5 Inference Concerning the Model’s Prediction
 14.6 Multiple Regression Models with Qualitative Independent Variables

Chapter 15: Analysis of Variance (ANOVA)
 15.1 OneWay ANOVA
 15.2 TwoWay ANOVA: The Randomized Block Design
 15.3 TwoWay ANOVA: The Factorial Design

Chapter 16: Looking for Relationships in Qualitative Data
 16.1 The ChiSquare Distribution
 16.2 The ChiSquare Test for Goodness of Fit
 16.3 The ChiSquare Test for Association

Chapter 17: Nonparametric Tests
 17.1 The Sign Test
 17.2 The Wilcoxon SignedRank Test
 17.3 The Wilcoxon RankSum Test
 17.4 The Rank Correlation Test
 17.5 The Runs Test for Randomness
 17.6 The KruskalWallis Test