We’re proud to announce the new edition of Discovering Statistics and Data!

This new edition pays homage to modern day’s technology-driven data explosion, helping students understand the context behind future statistical concepts to be learned and explaining why the study of statistics is critical.
View a free sample of the new edition of Discovering Statistics and Data.

The text opens by describing the necessity of understanding the data around us, introducing students to what data is, how we measure it, where it comes from, how to visualize it, and what kinds of career opportunities surround its analysis and processing. This focus makes upcoming content more meaningful for students and then challenges them to think with statistics. Request an examination copy.

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NEW features include:

• Greater focus on data – Introductory chapters place a strong emphasis on helping students understand where data comes from, data visualization techniques, “Big Data,” and the problems arising from having large data sets.

• Downloadable data sets – More real data sets are available for download, including over 15 large data sets and one giant data set. 

• More technology integration – Detailed instruction using graphing calculators, Excel, Minitab, and R Statistical language are included.

• Real-world applications – Larger scale chapter projects challenge students and brief, relatable articles engage readers.

• Expanded exercises and examples – Over 60 examples and 200 exercises, including new conceptual questions, have been added.

• Pedagogy modernization – GAISE guidelines were carefully considered and incorporated, and the most current P-value significance testing recommendations published by the ASA for guidance on hypothesis testing are included.

• Virtual simulations and games – Students develop conceptual understanding and statistical literacy through hands-on interactives and simulations.

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Table of Contents:

1. Statistics and Problem Solving

The Meaning of Data
Statistics as a Career
The Data Explosion
Modern Computing, Networks, and Statistics
Big Data
Introduction to Statistical Thinking
Descriptive vs. Inferential Statistics
The Consequences of Statistical Illiteracy

2. Data, Reality, and Problem Solving

Collecting Data
Data Classification
Time Series Data vs. Cross-Sectional Data
Data Resources

3. Visualizing Data

Frequency Distributions
Displaying Qualitative Data Graphically
Constructing Frequency Distributions for Quantitative Data
Histograms and Other Graphical Displays of
Quantitative Data
Analyzing Graphs

4. Describing and Summarizing Data from One Variable

Measures of Location
Measures of Dispersion
Measures of Relative Position, Box Plots, and Outliers
Data Subsetting
Analyzing Grouped Data
Proportions and Percentages

5. Discovering Relationships

Scatterplots and Correlation
Fitting a Linear Model
Evaluating the Fit of a Linear Model
Fitting a Linear Time Trend
Scatterplots for More Than Two Variables

6. Probability, Randomness, and Uncertainty

Introduction to Probability
Addition Rules for Probability
Multiplication Rules for Probability
Combinations and Permutations
Combining Probability and Counting Techniques
Bayes’ Theorem

7. Discrete Probability Distributions

Types of Random Variables
Discrete Random Variables
The Discrete Uniform Distribution
The Binomial Distribution
The Poisson Distribution
The Hypergeometric Distribution

8. Continuous Probability Distributions

The Uniform Distribution
The Normal Distribution
The Standard Normal Distribution
Applications of the Normal Distribution
Assessing Normality
Approximations to Other Distributions

9. Samples and Sampling Distributions

Random Samples and Sampling Distributions
The Distribution of the Sample Mean and the Central Limit Theorem
The Distribution of the Sample Proportion
Other Forms of Sampling

10. Estimation: Single Samples

Point Estimation of the Population Mean
Interval Estimation of the Population Mean
Estimating the Population Proportion
Estimating the Population Standard Deviation or Variance
Confidence Intervals Based on Resampling (Bootstrapping) (Courseware only)

11. Hypothesis Testing: Single Samples

Introduction to Hypothesis Testing
Testing a Hypothesis about a Population Mean
The Relationship between Confidence Interval
Estimation and Hypothesis Testing
Testing a Hypothesis about a Population Proportion
Testing a Hypothesis about a Population Standard Deviation or Variance
Practical Significance vs. Statistical Significance

12. Inferences about Two Samples

Inference about Two Means: Independent Samples
Inference about Two Means: Dependent Samples (Paired Difference)
Inference about Two Population Proportions
Inference about Two Population Standard Deviations or Variances

13. Regression, Inference, and Model Building

Assumptions of the Simple Linear Model
Inference Concerning β1
Inference Concerning the Model’s Prediction

14. Multiple Regression

The Multiple Regression Model
The Coefficient of Determination and Adjusted R2
Interpreting the Coefficients of the Multiple Regression Model
Inference Concerning the Multiple Regression Model and its Coefficients
Inference Concerning the Model’s Prediction
Multiple Regression Models with Qualitative Independent Variables

15. Analysis of Variance (ANOVA)

One-Way ANOVA
Two-Way ANOVA: The Randomized Block Design
Two-Way ANOVA: The Factorial Design

16. Looking for Relationships in Qualitative Data

The Chi-Square Distribution
The Chi-Square Test for Goodness of Fit
The Chi-Square Test for Association

17. Nonparametric Tests

The Sign Test
The Wilcoxon Signed-Rank Test
The Wilcoxon Rank-Sum Test
The Rank Correlation Test
The Runs Test for Randomness
The Kruskal-Wallis Test

18. Statistical Process Control (Courseware only)

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Request an examination copy.

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