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.

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.

## 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)*

Want to know more? Contact us at sales@hawkeslearning.com!