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STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Instead of assuming that students will "pick it up along the way," Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

Roxy Peck, California Polytechnic State University, San Luis Obispo

Roxy Peck is Emerita Associate Dean of the College of Science and Mathematics and Professor of Statistics Emerita at California Polytechnic State University, San Luis Obispo. A faculty member at Cal Poly from 1979 until 2009, Roxy served for six years as Chair of the Statistics Department before becoming Associate Dean, a position she held for 13 years. She received an M.S. in Mathematics and a Ph.D. in applied statistics from the University of California, Riverside. Roxy is nationally known in the area of statistics education, and she was presented with the Lifetime Achievement Award in Statistics Education at the U.S. Conference on Teaching Statistics in 2009. In 2003, she received the American Statistical Association’s Founder’s Award, recognizing her contributions to K–12 and undergraduate statistics education. She is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Roxy served for five years as the Chief Reader for the Advanced Placement (AP) Statistics Exam and has chaired the American Statistical Association’s Joint Committee with the National Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K–12 and the Section on Statistics Education. In addition to her texts in introductory statistics, Roxy is also co-editor of “Statistical Case Studies: A Collaboration Between Academe and Industry” and a member of the editorial board for “Statistics: A Guide to the Unknown, 4th Edition.” Outside the classroom, Roxy likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs and heads to Arizona and New Mexico whenever she can find the time.
  • A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes' Rule type probability calculations. However, it's done in a way that eliminates the need for the symbolism and formulas, which are a roadblock for many students.
  • Are You Ready to Move On?--Students Test Their Understanding: Prior to moving to the next chapter, "Are You Ready to Move On?" questions allow students to confirm that they have achieved the chapter learning objectives. Like the problem sets for each section, this collection of exercises is developmental--assessing all of the chapter learning objectives and serving as a comprehensive end-of-chapter review.
  • Exploring the Big Ideas--Real-Data Algorithmic Sampling Exercises: Most chapters contain extended sampling-based, real-data exercises at the end of the chapter. Each student goes to the companion website *(* and gets a different random sample of data from a population. The student then uses that sample to answer the questions. These unique exercises are designed to teach about sampling variability and provide a vehicle for rich classroom discussions of this important statistical concept.
  • Simple Design: Recent research shows that many of the "features" in current textbooks are not really helpful to students. In fact, cartoons, sidebars, historical notes, fake post-it notes in the margins, etc. actually distract students and interfere with learning. STATISTICS: LEARNING FROM DATA has a simple, clean design that minimizes clutter and maximizes student understanding.
  • Data Analysis Software: JMP™ data analysis software is included with each new textbook at no additional charge.
  • Technology Notes: Technology Notes at the end of most chapters give students helpful hints and guidance on completing tasks associated with a particular chapter. The following technologies are included in the notes: JMP™, MINITAB®, SPSS®, Microsoft® Excel® 2007, TI-83/84, and TI-Nspire. They include display screens to help students visualize and better understand the steps. More complete technology manuals are also available on the text's website.
  • Chapter Activities--Engaging Students in Hands-On Activities: There is a growing body of evidence indicating that students learn best when they are actively engaged. Chapter activities guide students' thinking about important ideas and concepts.
  • Chapter on Overview of Statistical Inference (Chapter 7): This short chapter focuses on the things students need to think about in order to select an appropriate method of analysis. In most texts, these considerations are "hidden" in the discussion that occurs when a new method is introduced. Discussing these considerations up front in the form of four key questions that need to be answered before choosing an inference method makes it easier for students to make correct choices.
  • An Organization That Reflects the Data Analysis Process: Students are introduced early to the idea that data analysis is a process that begins with careful planning, followed by data collection, data description using graphical and numerical summaries, data analysis, and finally interpretation of results. The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference.
  • Inference for Proportions before Inference for Means: The book makes it possible to develop the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread). In addition, inferential procedures for proportions are based on the normal distribution, allowing students to focus on the new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution.
  • Separate Treatment of Inference Based on Experiment Data (Chapter 14): Many statistical studies involve collecting data via experimentation. The same inference procedures used to estimate or test hypotheses about population parameters also are used to estimate or test hypotheses about treatment effects. However, the necessary assumptions are slightly different (for example, random assignment replaces the assumption of random selection), as is the wording of conclusions. Treating both cases together tends to confuse students; this text makes the distinction clear.
  • Chapter Learning Objectives--Keeping Students Informed about Expectations: The learning objectives explicitly state the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice.
  • Preview--Motivation for Learning: Each chapter opens with a Preview and Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows. These relevant and current examples provide a context in which one or more questions are proposed for further investigation. The context is revisited in the chapter once students have the necessary understanding to more fully address the questions posed.
  • Real Data That Motivates and Engages: Examples and exercises with overly simple settings don't allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, and other published sources. They cover a wide range of disciplines and subject areas of interest to today's students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment.
  • Exercises Organized into Developmental Sets to Structure the Out-of-Class Experience: End-of-section exercises are presented in two developmental sets. The exercises in each set work together to assess all of the learning objectives for the section. Additional section exercises are included for those who want more practice.
Learning from Data. Statistics--It''s All About Variability. The Data Analysis Process. Goals for Student Learning. The Structure of the Chapters that Follow.
1. Collecting Data in Reasonable Ways.
Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable?
2. Graphical Methods for Describing Data Distributions.
Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and Time-Series Plots. Graphical Displays in the Media.
3. Numerical Methods for Describing Data Distributions.
Selecting Appropriate Numerical Summaries. Describing Center and Spread for Data Distributions that are Approximately Symmetric. Describing Center and Spread for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles.
4. Describing Bivariate Numerical Data.
Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making Predictions--Putting it all Together. Bonus Material on Logistic Regression (Online).
5. Probability.
Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional).
6. Random Variables and Probability Distributions.
Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. The Normal Distribution. Checking for Normality. The Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Counting Rules, The Poisson Distribution (Online).
7. An Overview of Statistical Inference--Learning from Data.
Statistical Inference--What We Can Learn From Data. Selecting an Appropriate Method--Four Key Questions. A Five-Step Process for Statistical Inference.
8. Sampling Variability and Sampling Distributions.
Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning From Data.
9. Estimating a Population Proportion.
Selecting an Estimator. Estimating a Population Proportion--Margin of Error. A Large-Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error.
10. Asking and Answering Questions about a Population Proportion.
Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing--An Informal Example. A Procedure for Carrying Out a Hypothesis Test. Large-Sample Hypothesis Tests for a Population Proportion.
11. Asking and Answering Questions about the Difference between Two Population Proportions.
Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions.
12. Asking and Answering Questions about a Population Mean.
Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean.
13. Asking and Answering Questions about the Difference between Two Population Means.
Testing Hypotheses about the Difference between Two Population Means Using Independent Samples. Testing Hypotheses about the Difference between Two Population Means Using Paired Samples. Estimating the Difference between Two Population Means.
14. Learning from Experiment Data.
Variability and Random Assignment. Testing Hypotheses about Differences in Treatment Effects. Estimating a Difference in Treatment Effects.
15. Learning from Categorical Data.
Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table.
16. Understanding Relationships--Numerical Data Part 2 (Online).
The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy.
17. Asking and Answering Questions about More Than Two Means (Online).
The Analysis of Variance--Single-Factor ANOVA and the F Test. Multiple Comparisons.
Appendix: ANOVA Computations.
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