# Statistics: Learning from Data (AP Edition) 2nd edition

Roxy Peck and Tom Short
Publisher: Cengage Learning

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• Peck Statistics: Learning From Data (AP® Edition) 2e with SALT - Updated July 2021
• Peck Statistics: Learning From Data (AP® Edition) 2e: AP® Review
• Peck Statistics: Learning From Data (AP® Edition) 2e: FT5 AP® Review and Practice Tests

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• Chapter 1: Collecting Data In Reasonable Ways
• 1.1: Statistics—It's All About Variability
• 1.2: Statistical Studies: Observation and Experimentation (9)
• 1.3: Collecting Data: Planning an Observational Study (12)
• 1.4: Collecting Data—Planning an Experiment (18)
• 1.5: The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable? (8)
• 1.6: Avoid These Common Mistakes
• 1: Review Exercises (9)
• 1: AP® Review Questions (16)

• Chapter 2: Graphical Methods for Describing Data Distributions
• 2.1: Selecting an Appropriate Graphical Display (10)
• 2.2: Displaying Categorical Data: Bar Charts and Comparative Bar Charts (6)
• 2.3: Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms (14)
• 2.4: Displaying Bivariate Numerical Data: Scatterplots and Time Series Plots (9)
• 2.5: Graphical Displays in the Media (6)
• 2.6: Avoid These Common Mistakes
• 2: Review Exercises (7)
• 2: AP® Review Questions (18)
• 2: JMP Simulations (9)

• Chapter 3: Numerical Methods for Describing Data Distributions
• 3.1: Selecting Appropriate Numerical Summaries (8)
• 3.2: Describing Center and Variability for Data Distributions That Are Approximately Symmetric (10)
• 3.3: Describing Center and Variability for Data Distributions That Are Skewed or Have Outliers (12)
• 3.4: Summarizing a Data Set: Boxplots (9)
• 3.5: Measures of Relative Standing: z-scores and Percentiles (12)
• 3.6: Avoid These Common Mistakes
• 3: Review Exercises (8)
• 3: AP® Review Questions (16)
• 3: JMP Simulations (16)

• Chapter 4: Describing Bivariate Numerical Data
• 4.1: Correlation (11)
• 4.2: Linear Regression: Fitting a Line to Bivariate Data (11)
• 4.3: Assessing the Fit of a Line (13)
• 4.4: Describing Linear Relationships and Making Predictions—Putting It All Together
• 4.5: Modeling Nonlinear Relationships (7)
• 4.6: Avoid These Common Mistakes
• 4: Review Exercises (9)
• 4: AP® Review Questions (17)
• 4: JMP Simulations (14)

• Chapter 5: Probability
• 5.1: Interpreting Probabilities (8)
• 5.2: Calculating Probabilities (8)
• 5.3: Probabilities of More Complex Events: Unions, Intersections, and Complements (12)
• 5.4: Conditional Probability (10)
• 5.5: Calculating Probabilities—A More Formal Approach (Optional) (12)
• 5.6: Probability as a Basis for Making Decisions
• 5.7: Estimating Probabilities Empirically and Using Simulation (Optional) (5)
• 5: Review Exercises (7)
• 5: AP® Review Questions (17)

• Chapter 6: Random Variables and Probability Distributions
• 6.1: Random Variables (7)
• 6.2: Probability Distributions for Discrete Random Variables (8)
• 6.3: Probability Distributions for Continuous Random Variables (13)
• 6.4: The Mean and Standard Deviation of a Random Variable (11)
• 6.5: Normal Distributions (17)
• 6.6: Checking for Normality (4)
• 6.7: Binomial and Geometric Distributions (17)
• 6.8: Using the Normal Distribution to Approximate a Discrete Distribution (7)
• 6: Review Exercises (9)
• 6: AP® Review Questions (17)

• Chapter 7: An Overview of Statistical Inference—Learning from Data
• 7.1: Statistical Inference—What You Can Learn From Data (8)
• 7.2: Selecting an Appropriate Method—Four Key Questions (11)
• 7.3: A Five-Step Process for Statistical Inference
• 7: Review Exercises (8)
• 7: AP® Review Questions (10)

• Chapter 8: Sampling Variability and Sampling Distributions
• 8.1: Statistics and Sampling Variability (7)
• 8.2: The Sampling Distribution of a Sample Proportion (9)
• 8.3: How Sampling Distributions Support Learning from Data (8)
• 8: Review Exercises (6)
• 8: AP® Review Questions (11)

• Chapter 9: Estimating a Population Proportion
• 9.1: Selecting an Estimator (8)
• 9.2: Estimating a Population Proportion—Margin of Error (13)
• 9.3: A Large-Sample Confidence Interval for a Population Proportion (23)
• 9.4: Choosing a Sample Size to Achieve a Desired Margin of Error (6)
• 9.5: Avoid These Common Mistakes
• 9: Review Exercises (8)
• 9: AP® Review Questions (11)

• 10.1: Hypotheses and Possible Conclusions (10)
• 10.2: Potential Errors in Hypothesis Testing (9)
• 10.3: The Logic of Hypothesis Testing—An Informal Example (5)
• 10.4: A Procedure for Carrying Out a Hypothesis Test (5)
• 10.5: Large-Sample Hypothesis Tests for a Population Proportion (20)
• 10.6: Power and the Probability of Type II Error (8)
• 10.7: Avoid These Common Mistakes
• 10: Review Exercises (10)
• 10: AP® Review Questions (11)

• 11.1: Estimating the Difference Between Two Population Proportions (9)
• 11.2: Testing Hypotheses About the Difference Between Two Population Proportions (8)
• 11.3: Inference for Two Proportions Using Data from an Experiment (10)
• 11.4: Avoid These Common Mistakes
• 11: Review Exercises (4)
• 11: AP® Review Questions (12)

• 12.1: The Sampling Distribution of the Sample Mean (14)
• 12.2: A Confidence Interval for a Population Mean (18)
• 12.3: Testing Hypotheses About a Population Mean (14)
• 12.4: Avoid These Common Mistakes
• 12: Review Exercises (7)
• 12: AP® Review Questions (23)
• 12: JMP Simulations (22)

• 13.1: Two Samples: Paired versus Independent Samples (2)
• 13.2: Learning About a Difference in Population Means Using Paired Samples (21)
• 13.3: Learning About a Difference in Population Means Using Independent Samples (22)
• 13.4: Inference for Two Means Using Data from an Experiment (20)
• 13.5: Avoid These Common Mistakes
• 13: Review Exercises (9)
• 13: AP® Review Questions (16)
• 13: JMP Simulations (20)

• Chapter 14: Learning from Categorical Data
• 14.1: Chi-Square Tests for Univariate Categorical Data (16)
• 14.2: Tests for Homogeneity and Independence in a Two-Way Table (14)
• 14.3: Avoid These Common Mistakes
• 14: Review Exercises (7)
• 14: AP® Review Questions (17)
• 14: JMP Simulations (6)

• Chapter 15: Understanding Relationships—Numerical Data
• 15.1: The Simple Linear Regression Model (11)
• 15.2: Inferences Concerning the Slope of the Population Regression Line (11)
• 15.3: Checking Model Adequacy (11)
• 15: Review Exercises (3)
• 15: AP® Review Questions (17)

• Chapter FT5: Fast Track to a 5: Preparing for the AP® Statistics Examination
• FT5.DT: A Diagnostic Test (44)
• FT5.1: Graphical Methods for Describing Data (17)
• FT5.2: Numerical Methods for Describing Data (17)
• FT5.3: Summarizing Bivariate Data (17)
• FT5.4: Collecting Data Sensibly (17)
• FT5.5: Probability (17)
• FT5.6: Random Variables and Probability Distributions (17)
• FT5.7: Sampling Variability and Sampling Distributions (17)
• FT5.8: Estimating Using a Single Sample (17)
• FT5.9: Hypothesis Testing Using a Simple Sample (17)
• FT5.10: Comparing Two Populations or Treatments (17)
• FT5.11: The Analysis of Categorical Data and Goodness-of-Fit Tests (17)
• FT5.12: Inference for Linear Regression and Correlation (17)
• FT5.T1: Practice Test I (45)
• FT5.T2: Practice Test II (45)

Statistics: Learning From Data (AP® Edition), 2nd edition, by Roxy Peck and Tom Short, addresses common problems faced by students and instructors with an innovative approach to elementary statistics. The organization by Learning Objective, focus on real-data examples, and adherence to the Guidelines for Assessment and Instruction in Statistics Education (GAISE), help students learn to think like statisticians. The AP® edition includes exam tips throughout the text along with sections designated to address common errors on the AP® exam.

#### New for Spring '21

• Questions updated with SALT (Statistical Analysis and Learning Tool), including questions with frequency data and questions requiring the implementation of a Chi-Square test.

#### New for Fall '20 / Spring '21 Academic Year

• SALT (Statistical Analysis and Learning Tool) is a data analysis tool for introductory level statistics courses that helps students gain improved conceptual understanding of statistics through visualization and analysis of datasets. SALT can be used on its own or as a tool to answer SALT-enabled questions in WebAssign.
• #### Instructor Introduction - Statistical Analysis and Learning Tool (SALT) | WebAssign

The Statistical Analysis and Learning Tool (SALT) is designed by statisticians, for statisticians, to help you get introductory students deeply engaged in da...

### Instructor Product Features

• Course Packs with ready-to-use assignments were built by subject matter experts specifically for this textbook. They are designed to save you time and can be easily customized to meet your teaching goals.
• Test Banks: A pool of over 1,000 assessments for use in quizzes, tests, and exams.
• Instructor Resources include Instructional Lecture Videos, hosted by Dana Mosely. These topic-specific videos provide explanations of key concepts, examples, and applications in a lecture-based format. Lecture PowerPoint slides are also available.

### Student Learning Tools

• Read It links under each question quickly jump to the corresponding section of a complete, interactive eBook that lets students highlight and take notes as they read.
• Watch It links provide step-by-step instruction with short, engaging videos that are ideal for visual learners.
• Master It Tutorials show students how to solve a similar problem in multiple steps by providing direction along with derivation, so the student understands the concepts and reasoning behind the problem solving.
• AP® Exam Practice (AP) questions at the end of each chapter help prepare students for the AP® Statistics exam.
• The Fast Track to a Five (FT5) AP® test preparation guide includes strategies for taking the exam, a diagnostic test so that students can assess their level of preparedness, chapter-review sections with self-study questions in AP® format, and two complete practice tests in AP® format.
• Student Resources include Data Analysis Tool Instructions / Tech Guides for the below software. Can be used stand-alone or in conjunction with assessment items (Homework, Labs, or Project Milestones).
• TI-83/84 and TI-Nspire Calculator
• Excel
• JMP
• Minitab
• SPSS
• R

### Tools to Explore Real Data with Technology

• The Statistical Analysis and Learning Tool (SALT) is designed by statisticians, for statisticians, to help you get introductory students deeply engaged in data manipulation, analysis, and interpretation without getting bogged down in complex computations.
• Simulation Questions by JMP (JMP): Have your students understand concepts by utilizing real data. Students must discover the answer to guided questions by interacting with a simulation of real data in our JMP interactive applet within WebAssign.

## Questions Available within WebAssign

Most questions from this textbook are available in WebAssign. The online questions are identical to the textbook questions except for minor wording changes necessary for Web use. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

##### Question Group Key
FT5 - Fast Track to a Five
AP - AP Exam Practice
MI - Master It
MI.SA - Stand Alone Master It
JMP - Simulation Question by JMP
R - Chapter Review Exercise
S - SALT

##### Question Availability Color Key
BLACK questions are available now
GRAY questions are under development

Group Quantity Questions
Chapter FT5: Fast Track to a 5: Preparing for the AP® Statistics Examination
FT5.DT 44 FR.001 FR.002 FR.003 FR.004 FR.005 FR.006 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022-024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
FT5.T1 45 FR.001 FR.002 FR.003 FR.004 FR.005 FR.006 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
FT5.T2 45 FR.001 FR.002 FR.003 FR.004 FR.005 FR.006 001 002 003 004 005 006 007 008 009 010 011 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040
FT5.1 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.2 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.3 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.4 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.5 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.6 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.7 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.8 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.9 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.10 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.11 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
FT5.12 17 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
Chapter 1: Collecting Data In Reasonable Ways
1.AP 16 FR.001 FR.002 001 002 003 004 005 006 007 008 009 010 011 012 013 014
1.E 47 002 003 004 006 007 008 010 012 013 015 018 019 020 021 023 025 026 027 028 030 033 035 036 037 039 041 042 044 045 046 047 049 050 051 053 054 056 057 058 059 060 063 069 070 071 072 073
1.R 9 074 076 079 080 082 083 084 088 090
Chapter 2: Graphical Methods for Describing Data Distributions
2.AP 18 FR.003 FR.004 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016
2.E 45 001 002 003 005 007 009 010 011 012 014 016.MI.S 016.MI.SA 017 018.MI.S 018.MI.SA 020.S 024.MI.S 024.MI.SA 025.S 026 027 028 030 031.MI 031.MI.SA 032 037 038 039 040 044 045 046.S 047 048.S 049 051.MI 051.MI.SA 053 055 056.S 058 060 061 063
2.JMP 9 001 002 003 004 005 006 007 008 009
2.R 7 064 065 067 069 070 073 076
Chapter 3: Numerical Methods for Describing Data Distributions
3.AP 16 FR.005 FR.006 001 002 003 004 005 006 007 008 009 010 011 012 013 014
3.E 51 001.MI.S 001.MI.SA 003.S 004.S 005.S 007 010 011.S 012.MI.S 012.MI.SA 013.MI.S 013.MI.SA 014.S 015 017.MI.S 017.MI.SA 018.S 021 025.S 026.MI.S 026.MI.SA 028.S 029.S 030.MI.S 030.MI.SA 031.MI.S 031.MI.SA 032.S 033 035.S 036.S 037.S 039.S 041.MI.S 041.MI.SA 043.S 044 045 048.S 050.MI 050.MI.SA 051 054 055.MI 055.MI.SA 057.S 058.MI 058.MI.SA 059.MI 059.MI.SA 061
3.JMP 16 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016
3.R 8 062.S 063.S 064.S 065.S 067.S 068.S 069.S 071
3.ST 3 003.S 004.S 005.S
Chapter 4: Describing Bivariate Numerical Data
4.AP 17 FR.007 FR.008 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
4.E 42 001 003 005.S 009 010 013.S 014.S 017.MI 017.MI.SA 019.MI 019.MI.SA 021 022 024.S 025.MI.S 025.MI.SA 029.S 031.MI.S 031.MI.SA 032.S 036 037 039.S 042.MI.S 042.MI.SA 044.S 045.S 046.MI.S 046.MI.SA 049 051 053.S 054.MI.S 054.MI.SA 056 057 058 059 060 061 062 063
4.JMP 14 001 002 003 004 005 006 007 008 009 010 011 012 013 014
4.R 9 064 065 067.S 069.S 074.S 075 077.S 078 080
Chapter 5: Probability
5.AP 17 FR.009 FR.010 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
5.E 55 001 003.MI 003.MI.SA 006.MI 006.MI.SA 008 009.MI 009.MI.SA 011 012 014 018.MI 018.MI.SA 019 020.MI 020.MI.SA 022 024.MI 024.MI.SA 026 034.MI 034.MI.SA 036 037.MI 037.MI.SA 038.MI 038.MI.SA 041 043 045 046 048 050.MI 050.MI.SA 052 053.MI 053.MI.SA 055 057 058 059 060.MI 060.MI.SA 062 063 064.MI 064.MI.SA 067.MI 067.MI.SA 068 072.MI 072.MI.SA 073 074 076
5.R 7 082 085 087 088 091 094 095
Chapter 6: Random Variables and Probability Distributions
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6.R 9 105 107 109 110 112 113.S 115.S 118.S 119
6.ST 2 002.S 003.S
6.SYS 2 002.S 003.S
Chapter 7: An Overview of Statistical Inference—Learning from Data
7.AP 10 001 002 003 004 005 006 007 008 009 010
7.E 19 002 003 004 006 008 009 011 012 016 017 019 022 023 024 025 026 027 028 029
7.R 8 030 031 032 034 035 037 039 041
Chapter 8: Sampling Variability and Sampling Distributions
8.AP 11 FR.013 001 002 003 004 005 006 007 008 009 010
8.E 24 001 002 003 007 010 013 014 016 017 019.MI 019.MI.SA 022 023 025.MI 025.MI.SA 028 029 030 031.S 034 035.MI 035.MI.SA 038.S 040
8.R 6 041 042 045 049 050 052
Chapter 9: Estimating a Population Proportion
9.AP 11 FR.014 001 002 003 004 005 006 007 008 009 010
9.E 50 002 004 005.MI 005.MI.SA 008 010 014 016 017.MI 017.MI.SA 019.S 020 022 024.MI.S 024.MI.SA 025.S 026 028 031.MI 031.MI.SA 033 034.MI 034.MI.SA 036 037.MI 037.MI.SA 038.MI.S 038.MI.SA.S 039 040.S 042.MI.S 042.MI.SA 044 046 047 048.S 049 050.S 051.S 053.S 055.S 057 058.S 060.S 062.S 064.MI.S 064.MI.SA 066.MI.S 066.MI.SA 067.S
9.R 8 070 071 073 074.S 076.S 077 079 082.S
10.AP 11 FR.015 001 002 003 004 005 006 007 008 009 010
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10.R 10 083 084 085 087 089 090.S 092 094.S 096.S 097
11.AP 12 FR.016 001 002 003 004 005 006 007 008 009 010 011
11.E 27 001.S 002.S 004.MI.S 004.MI.SA 005 006.MI.S 006.MI.SA 009.S 010.S 011 013.MI.S 013.MI.SA 016 018.MI.S 018.MI.SA 019.MI.S 019.MI.SA 020.S 023.S 024.MI.S 024.MI.SA 025.S 027.MI.S 027.MI.SA 029 031.S 032
11.R 4 033.S 035 038.S 039.S
11.ST 1 001.S
12.AP 23 FR.017 FR.018 FR.019 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
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12.JMP 22 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022
12.R 7 060.S 061 063 064 066.S 068.S 069.S
13.AP 16 FR.020 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
13.E 65 001 002 005.MI.S 005.MI.SA.S 007.S 009 010.MI.S 010.MI.SA.S 013.MI.S 013.MI.SA 014.MI.S 014.MI.SA.S 016.S 017.MI.S 017.MI.SA.S 020.S 022.S 026 027.MI.S 027.MI.SA.S 029.S 030.MI.S 030.MI.SA.S 032.S 033.S 035.S 036.MI.S 036.MI.SA.S 038.MI.S 038.MI.SA.S 040.S 043.S 044.S 046.MI.S 046.MI.SA.S 047.S 048.S 049.S 050.S 053.S 054.S 056.S 057.MI.S 057.MI.SA.S 058.S 059 060 062.MI.S 062.MI.SA.S 063.S 065.MI.S 065.MI.SA.S 066.S 068 070 071.S 072.MI.S 072.MI.SA.S 073 074 075 077.MI.S 077.MI.SA.S 078.S 079.S
13.JMP 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
13.R 9 081 082 084.S 085.S 086.S 088.S 089.S 092 094.S
Chapter 14: Learning from Categorical Data
14.AP 17 FR.021 FR.022 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
14.E 30 001.S 002.MI.S 002.MI.SA 003.MI.S 003.MI.SA.S 004.S 007.S 008.S 010.S 011.MI.S 011.MI.SA.S 012.S 014.S 015.MI.S 015.MI.SA.S 017.S 019.MI.S 019.MI.SA 020.S 021 023.S 026.S 032.S 033.MI.S 033.MI.SA 034.S 035.MI.S 035.MI.SA.S 036.S 038.S
14.JMP 6 001 002 003 004 005 006
14.R 7 040.S 041.S 042.S 044.S 045.S 047.S 048
Chapter 15: Understanding Relationships—Numerical Data
15.AP 17 FR.023 FR.024 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015
15.E 33 001 002 003.MI.S 003.MI.SA.S 006.MI.S 006.MI.SA.S 007 009.S 010.S 011.MI 011.MI.SA 013 016 017 018.MI 018.MI.SA 020 021 022 026.S 027 029 030 031 033.MI 033.MI.SA 035 037 038 039 040.MI 040.MI.SA 043.S
15.R 3 046 047 050
Total 1448