# Statistics: Learning from Data with Corequisite Support Book 2nd edition

Roxy Peck and Tom Short
Publisher: Cengage Learning

## Course Packs

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## Textbook Resources

Additional instructional and learning resources are available with the textbook, and might include testbanks, slide presentations, online simulations, videos, and documents.

• College Success Toolkit
• Math Mindset
• Peck Statistics: Learning from Data 2e + Peck Statistics Companion 1e with SALT - Updated March 2021

Access is contingent on use of this textbook in the instructor's classroom.

• Chapter 1: Collecting Data In Reasonable Ways
• 1: Concept Explorations (3)
• 1.1: Statistics—It's All About Variability
• 1.2: Statistical Studies: Observation and Experimentation (9)
• 1.3: Collecting Data: Planning an Observational Study (9)
• 1.4: Collecting Data—Planning an Experiment (9)
• 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 (6)
• 1: Concept Questions (28)
• 1: Labs (6)
• 1: Test Bank (44)

• Chapter 2: Graphical Methods for Describing Data Distributions
• 2: Concept Explorations (3)
• 2: SALT Tutorial - Supporting Sections 2.2, 2.3, and 2.4 (1)
• 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 (13)
• 2.4: Displaying Bivariate Numerical Data: Scatterplots and Time Series Plots (7)
• 2.5: Graphical Displays in the Media (6)
• 2.6: Avoid These Common Mistakes
• 2: Review Exercises (6)
• 2: Select Your Scenario (beta) - Supporting Sections 2.2, 2.3, and 2.4 (1)
• 2: JMP Simulations (9)
• 2: Concept Questions (29)
• 2: Labs (5)
• 2: Test Bank (83)

• Chapter 3: Numerical Methods for Describing Data Distributions
• 3: Concept Explorations (4)
• 3: SALT Tutorial - Supporting Sections 3.4 and 3.5 (2)
• 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: Select Your Scenario (beta) - Supporting Sections 3.4 and 3.5 (2)
• 3: JMP Simulations (11)
• 3: Concept Questions (24)
• 3: Labs (6)
• 3: Test Bank (81)

• Chapter 4: Describing Bivariate Numerical Data
• 4: Concept Explorations (3)
• 4: SALT Tutorial
• 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: Avoid These Common Mistakes
• 4: Review Exercises (8)
• 4: Online Exercises (3)
• 4: Select Your Scenario (beta)
• 4: JMP Simulations (13)
• 4: Concept Questions (12)
• 4: Labs (6)
• 4: Test Bank (45)

• Chapter 5: Probability
• 5: Concept Explorations (2)
• 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: Concept Questions (24)
• 5: Labs (5)
• 5: Test Bank (4)

• Chapter 6: Random Variables and Probability Distributions
• 6: Concept Explorations (4)
• 6: SALT Tutorial - Supporting Sections 6.5 and 6.7 (1)
• 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: Mean and Standard Deviation of a Random Variable (7)
• 6.5: Normal Distributions (17)
• 6.6: Checking for Normality (4)
• 6.7: Binomial and Geometric Distributions (Optional) (17)
• 6.8: Using the Normal Distribution to Approximate a Discrete Distribution (Optional) (7)
• 6: Review Exercises (8)
• 6: Online Exercises (8)
• 6: Select Your Scenario (beta) - Supporting Sections 6.5 and 6.7 (1)
• 6: Concept Questions (30)
• 6: Labs (10)
• 6: Test Bank (62)

• Chapter 7: An Overview of Statistical Inference—Learning from Data
• 7: Concept Explorations (2)
• 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: Concept Questions
• 7: Test Bank

• Chapter 8: Sampling Variability and Sampling Distributions
• 8: Concept Explorations (2)
• 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: Concept Questions (2)
• 8: Labs (6)

• Chapter 9: Estimating a Population Proportion
• 9: Concept Explorations (3)
• 9: SALT Tutorial - Supporting Sections 9.2 and 9.3
• 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: Bootstrap Confidence Intervals for a Population Proportion (Optional) (4)
• 9.6: Avoid These Common Mistakes
• 9: Review Exercises (8)
• 9: Select Your Scenario (beta) - Supporting Sections 9.2 and 9.3
• 9: JMP Simulations (4)
• 9: Concept Questions (6)
• 9: Labs (6)

• 10: Concept Explorations (3)
• 10: SALT Tutorial - Supporting Section 10.5 (1)
• 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: Randomization Tests and Exact Binomial Tests for One Proportion (Optional) (5)
• 10.7: Avoid These Common Mistakes
• 10: Review Exercises (9)
• 10: Select Your Scenario (beta) - Supporting Section 10.5 (1)
• 10: JMP Simulations (6)
• 10: Concept Questions (8)
• 10: Labs (6)
• 10: Test Bank (24)

• 11: Concept Explorations (3)
• 11: SALT Tutorial - Supporting Sections 11.1 and 11.2
• 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: Simulation-Based Inference for Two Proportions (Optional) (5)
• 11.5: Avoid These Common Mistakes
• 11: Review Exercises (4)
• 11: Select Your Scenario (beta) - Supporting Sections 11.1 and 11.2
• 11: JMP Simulations (4)
• 11: Concept Questions (6)
• 11: Labs (6)

• 12: Concept Explorations (1)
• 12: SALT Tutorial - Supporting Sections 12.1, 12.2, and 12.3 (2)
• 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: Simulation-Based Inference for One Mean (Optional) (7)
• 12.5: Avoid These Common Mistakes
• 12: Review Exercises (7)
• 12: Select Your Scenario (beta) - Supporting Sections 12.1, 12.2, and 12.3 (2)
• 12: JMP Simulations (6)
• 12: Concept Questions (22)
• 12: Labs (6)
• 12: Test Bank (126)

• 13: Concept Explorations (1)
• 13: SALT Tutorial - Supporting Sections 13.2 and 13.3 (2)
• 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: Simulation-Based Inference for Two Means (Optional) (10)
• 13.6: Avoid These Common Mistakes
• 13: Review Exercises (9)
• 13: Select Your Scenario (beta) - Supporting Sections 13.2 and 13.3 (2)
• 13: JMP Simulations (20)
• 13: Concept Questions (12)
• 13: Labs (6)
• 13: Test Bank (93)

• Chapter 14: Learning from Categorical Data
• 14: Concept Explorations (3)
• 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: JMP Simulations (6)
• 14: Concept Questions (8)
• 14: Labs (6)
• 14: Test Bank (41)

• Chapter 15: Understanding Relationships—Numerical Data
• 15: Concept Explorations (1)
• 15: SALT Tutorial
• 15.1: The Simple Linear Regression Model (11)
• 15.2: Inferences Concerning the Slope of the Population Regression Line (12)
• 15.3: Checking Model Adequacy (11)
• 15: Review Exercises (3)
• 15: Select Your Scenario (beta)
• 15: Concept Questions
• 15: Labs (6)

• 16: Concept Explorations (2)
• 16.1: The Analysis of Variance—Single-Factor ANOVA and the F Test (13)
• 16.2: Multiple Comparisons (8)
• 16: Review Exercises (2)
• 16: JMP Simulations (4)
• 16: Concept Questions
• 16: Labs (5)
• 16: Test Bank (35)

• Chapter PJT: Project
• PJT.1: Project (4)

Statistics: Learning From Data, 2nd edition, 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 WebAssign component for this text engages students with an interactive eBook and several other resources.

#### New for Spring '21

• Additional SALT Tutorial Questions added to help your students understand how to use SALT in their WebAssign assignments. Students are provided with scaffolded instruction not only on the content, but how to use SALT to compute and analyze data.
• Additional Select Your Scenario Questions were added. Select Your Scenario problems provide students with 3 different contexts to choose from. They select the scenario most relevant to them, and then solve the problem. Regardless of which scenario the student chooses, they will be required to answer questions demonstrating knowledge of a learning objective, making them the perfect questions to assign toward the end of a chapter.
• New Concept Videos were added. Concept Videos are 7-10 minutes in length and are designed to help students with big picture understanding of statistics.
• New Concept Video Questions were added providing students with a concept video along with two to three comprehension questions.
• 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. Course Packs include Stats in Practice Video Questions, Labs, and Project Milestones.
• 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.
• 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

### Questions to Help Students Gain Interest and Assess Conceptual Understanding

• Stats in Practice Video Questions (SIP) show students how Statistics applies in the real world. Short and current news videos introduce each module. Each video is accompanied by multiple-choice and discussion questions, so that students can understand real-world context of what they're learning and stay engaged throughout the whole module.
• Concept Questions (CQ) provide a new way of engaging with non-computational questions. Students enter a free response before they choose a multiple-choice answer, closing the gap between homework and test preparedness.
• Concept Video Questions (CV) provide students with a concept video along with two to three comprehension questions.
• Select Your Scenario (SYS) problems provide students with 3 different contexts to choose from. They select the scenario most relevant to them, and then solve the problem. Regardless of which scenario the student chooses, they will be required to answer questions demonstrating knowledge of a learning objective, making them the perfect questions to assign toward the end of a chapter.

### 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.
• SALT Tutorial Questions (ST): Help your students understand how to use SALT in their WebAssign assignments. Students are provided with scaffolded instruction not only on the content, but how to use SALT to compute and analyze data.
• 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.
• Labs (Lab): Students can perform real statistical analysis in class or online with premade and module-specific Stats Labs. Require students to use the instructor-selected data analysis tool to analyze a real data set, pulling together knowledge learned from that module and previous material to facilitate whole-picture learning.
• Project Milestones (PJT): Allow one place for students to ideate, collaborate, and submit a longer-term project. The four sequential milestones are:
1. Research Design
2. Gather Data
3. Analyze Data
4. Present Results

## 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
MI - Master It
MI.SA - Stand Alone Master It
JMP - Simulation Question by JMP
CQ - Concept Question
Lab - Lab
PJT - Project Milestone
TB - Test Bank
SIP - Stats in Practice Video Question
R - Chapter Review Exercise
O - Online Exercise
S - SALT
ST - SALT Tutorial

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

Group Quantity Questions
Chapter PJT: Project
PJT.1 4 001 002 003 004
Chapter 1: Collecting Data In Reasonable Ways
1.CE 3 001.CV 001.SIP 002.CV
1.CQ 28 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028
1.E 35 002 003 004 006 007 008 010 012 013 015 018 019 020 022 024 025 027 028 031 034 036 037 039 041 042 044 045 046 047 050 056 057 058 059 060
1.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
1.R 6 061 063 066 068 072 074
1.TB 44 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044
Chapter 2: Graphical Methods for Describing Data Distributions
2.CE 3 001.CV 001.SIP 002.CV
2.CQ 29 001 002 003 004 005 006 007 008 010 011 012 013 014 015 016 017 021 023 024 025 026 027 028 029 030 031 032 035 036
2.E 42 001 002 003 005 007 009 010 011 012 014 016.MI 016.MI.SA 017 018.MI 018.MI.SA 020 024.MI.S 024.MI.SA.S 025.S 026 027 028 030.MI 030.MI.SA 031 033 036 037 038 042.S 043 044.S 045 047.MI 047.MI.SA 049 051 052.S 054 056 057 059
2.JMP 9 001 002 003 004 005 006 007 008 009
2.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
2.R 6 060 061 063 065 066 069
2.ST 1 001.S
2.SYS 1 001.S
2.TB 83 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083
Chapter 3: Numerical Methods for Describing Data Distributions
3.CE 4 001.CV 001.SIP 002.CV 003.CV
3.CQ 24 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024
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 11 001 002 003 004 005 006 007 008 009 010 011
3.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
3.R 8 062.S 063.S 064.S 065.S 067.S 068.S 069.S 071
3.ST 2 001.S 002.S
3.SYS 2 001.S 002.S
3.TB 81 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081
Chapter 4: Describing Bivariate Numerical Data
4.CE 3 001.CV 001.SIP 002.CV
4.CQ 12 001 002 003 004 005 006 007 008 009 010 011 012
4.E 35 001 003 005.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
4.JMP 13 001 002 003 004 005 006 007 008 009 010 011 012 013
4.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
4.O 3 075 077.S 079.S
4.R 8 057 058 060.S 062.S 067.S 068 070.S 071
4.TB 45 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045
Chapter 5: Probability
5.CE 2 001.CV 001.SIP
5.CQ 24 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024
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.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
5.R 7 082 085 087 088 091 094 095
5.TB 4 001 002 003 004
Chapter 6: Random Variables and Probability Distributions
6.CE 4 001.CV 001.SIP 002.CV 002.SIP
6.CQ 30 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030
6.E 80 001 003 004 006 008.MI 008.MI.SA 009 010 011 013 015 018.MI 018.MI.SA 019.MI 019.MI.SA 021.MI 021.MI.SA 022 023.MI 023.MI.SA 024 025 026 027.MI 027.MI.SA 028 030.MI 030.MI.SA 032 033 036.MI 036.MI.SA 038 040.MI 040.MI.SA 041.MI.S 041.MI.SA 042.S 044.S 047.MI.S 047.MI.SA 048.S 050.MI.S 050.MI.SA 051.S 054.MI.S 054.MI.SA 055 060.MI.S 060.MI.SA 061.S 063.S 070.S 071.MI.S 071.MI.SA 072.S 074.S 075.MI.S 075.MI.SA 077.S 078 080.S 081.MI.S 081.MI.SA 083 084 085.MI 085.MI.SA 087.S 088 090.MI.S 090.MI.SA 091 093.MI.S 093.MI.SA.S 094.S 095.MI.S 095.MI.SA 096.S 098.S
6.Lab 10 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 002.Excel 002.JMP 002.Minitab 002.R 002.SPSS
6.O 8 116 117.MI 117.MI.SA 122.MI 122.MI.SA 123 125 126
6.R 8 101 103 105 106 108 109.S 111.S 114.S
6.ST 1 001.S
6.SYS 1 001.S
6.TB 62 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062
Chapter 7: An Overview of Statistical Inference—Learning from Data
7.CE 2 001.CV 001.SIP
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.CE 2 001.CV 001.SIP
8.CQ 2 001 002
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.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
8.R 6 041 042 045 049 050 052
Chapter 9: Estimating a Population Proportion
9.CE 3 001.CV 001.SIP 002.CV
9.CQ 6 001 002 003 004 005 006
9.E 54 002 004 005.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 069 070 072 074
9.JMP 4 001 002 003 004
9.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
9.R 8 078 079 081 082.S 084.S 085 087 090.S
10.CE 3 001.CV 001.SIP 002.CV
10.CQ 8 001 002 003 004 005 006 007 008
10.E 54 002 004 005 006 009.MI 009.MI.SA 010 014.MI 014.MI.SA 016 018 019 021.MI 021.MI.SA 023 025 026 030 031 032.S 033.MI.S 033.MI.SA 035.S 036 038 039 041.MI 041.MI.SA 042 047 048 049 051.MI.S 051.MI.SA.S 052.MI.S 052.MI.SA.S 055.S 056.S 057 058 061 063.S 067.S 069.MI.S 069.MI.SA.S 071.S 073.MI.S 073.MI.SA.S 074.S 075 076 078 079 080.S
10.JMP 6 001 002 003 004 005 006
10.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
10.R 9 081 082 083 085 087 088.S 090 092.S 094.S
10.ST 1 001.S
10.SYS 1 001.S
10.TB 24 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024
11.CE 3 001.CV 001.SIP 002.CV
11.CQ 6 001 002 003 004 005 006
11.E 32 001.S 002.S 004.MI.S 004.MI.SA 005.S 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 032 033 035.MI 035.MI.SA 036 039.S
11.JMP 4 001 002 003 004
11.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
11.R 4 041.S 043.S 046.S 047.S
12.CE 1 001.SIP
12.CQ 22 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022
12.E 53 001.MI 001.MI.SA 003 005 006.MI 006.MI.SA 007 008 010.MI.S 010.MI.SA.S 014.MI.S 014.MI.SA.S 016.S 018.S 020.MI.S 020.MI.SA 021 022.S 024.S 026.MI.S 026.MI.SA.S 027.S 028.S 030 031.MI.S 031.MI.SA.S 032.MI.S 032.MI.SA 033.S 036 038 039.S 041.S 042.S 043.S 044.MI.S 044.MI.SA.S 045.S 047.S 049.MI.S 049.MI.SA.S 050.S 051.S 056.S 058.MI.S 058.MI.SA.S 060.MI 060.MI.SA 061 062 063 065 067
12.JMP 6 001 002 003 004 005 006
12.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
12.R 7 068.S 069 071 072 074.S 076.S 077.S
12.ST 2 001.S 002.S
12.SYS 2 001.S 002.S
12.TB 126 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
13.CE 1 001.SIP
13.CQ 12 001 002 003 004 005 006 007 008 009 010 011 012
13.E 75 001 002 005.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 081.MI 081.MI.SA 082.MI 082.MI.SA 083 084 087 088 089 090
13.JMP 20 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020
13.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
13.R 9 091 092 094.S 095.S 096.S 098.S 099.S 102 104.S
13.ST 2 001.S 002.S
13.SYS 2 001.S 002.S
13.TB 93 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094
Chapter 14: Learning from Categorical Data
14.CE 3 001.CV 001.SIP 002.SIP
14.CQ 8 001 002 003 004 005 006 007 008
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.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
14.R 7 040.S 041.S 042.S 044.S 045.S 047.S 048
14.TB 41 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041
Chapter 15: Understanding Relationships—Numerical Data
15.CE 1 001.SIP
15.E 34 001 002 003.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.MI 016.MI.SA 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
15.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
15.R 3 046 047 050