# Statistics: Learning from Data 2nd edition

Roxy Peck and Tom Short
Publisher: Cengage Learning

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• Peck Statistics: Learning from Data 2e

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• Chapter 1: Collecting Data In Reasonable Ways
• 1: Stats in Practice Video Question (1)
• 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: Stats in Practice Video Question (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 (11)
• 2.4: Displaying Bivariate Numerical Data: Scatterplots and Time Series Plots (6)
• 2.5: Graphical Displays in the Media (6)
• 2.6: Avoid These Common Mistakes
• 2: Review Exercises (6)
• 2: JMP Simulations (9)
• 2: Concept Questions (29)
• 2: Labs (5)
• 2: Test Bank (83)

• Chapter 3: Numerical Methods for Describing Data Distributions
• 3: Stats in Practice Video Question (1)
• 3.1: Selecting Appropriate Numerical Summaries (7)
• 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: JMP Simulations (11)
• 3: Concept Questions (24)
• 3: Labs (6)
• 3: Test Bank (81)

• Chapter 4: Describing Bivariate Numerical Data
• 4: Stats in Practice Video Question (1)
• 4.1: Correlation (10)
• 4.2: Linear Regression: Fitting a Line to Bivariate Data (9)
• 4.3: Assessing the Fit of a Line (11)
• 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: JMP Simulations (13)
• 4: Concept Questions (12)
• 4: Labs (6)
• 4: Test Bank (45)

• Chapter 5: Probability
• 5: Stats in Practice Video Question (1)
• 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: Stats in Practice Video Questions (2)
• 6.1: Random Variables (6)
• 6.2: Probability Distributions for Discrete Random Variables (8)
• 6.3: Probability Distributions for Continuous Random Variables (11)
• 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: Concept Questions (30)
• 6: Labs (10)
• 6: Test Bank (62)

• Chapter 7: An Overview of Statistical Inference—Learning from Data
• 7: Stats in Practice Video Question (1)
• 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: Stats in Practice Video Question (1)
• 8.1: Statistics and Sampling Variability (7)
• 8.2: The Sampling Distribution of a Sample Proportion (8)
• 8.3: How Sampling Distributions Support Learning from Data (7)
• 8: Review Exercises (6)
• 8: Concept Questions (2)
• 8: Labs (6)

• Chapter 9: Estimating a Population Proportion
• 9: Stats in Practice Video Question (1)
• 9.1: Selecting an Estimator (7)
• 9.2: Estimating a Population Proportion—Margin of Error (12)
• 9.3: A Large-Sample Confidence Interval for a Population Proportion (20)
• 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: JMP Simulations (4)
• 9: Concept Questions (6)
• 9: Labs (6)

• 10: Stats in Practice Video Question (1)
• 10.1: Hypotheses and Possible Conclusions (8)
• 10.2: Potential Errors in Hypothesis Testing (8)
• 10.3: The Logic of Hypothesis Testing—An Informal Example (4)
• 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: JMP Simulations (6)
• 10: Concept Questions (8)
• 10: Labs (6)
• 10: Test Bank (24)

• 11: Stats in Practice Video Question (1)
• 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) (4)
• 11.5: Avoid These Common Mistakes
• 11: Review Exercises (4)
• 11: JMP Simulations (4)
• 11: Concept Questions (6)
• 11: Labs (6)

• 12: Stats in Practice Video Question (1)
• 12.1: The Sampling Distribution of the Sample Mean (14)
• 12.2: A Confidence Interval for a Population Mean (16)
• 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: JMP Simulations (6)
• 12: Concept Questions (22)
• 12: Labs (6)
• 12: Test Bank (126)

• 13: Stats in Practice Video Question (1)
• 13.1: Two Samples: Paired versus Independent Samples (2)
• 13.2: Learning About a Difference in Population Means Using Paired Samples (20)
• 13.3: Learning About a Difference in Population Means Using Independent Samples (20)
• 13.4: Inference for Two Means Using Data from an Experiment (18)
• 13.5: Simulation-Based Inference for Two Means (Optional) (8)
• 13.6: Avoid These Common Mistakes
• 13: Review Exercises (9)
• 13: JMP Simulations (20)
• 13: Concept Questions (12)
• 13: Labs (6)
• 13: Test Bank (94)

• Chapter 14: Learning from Categorical Data
• 14: Stats in Practice Video Questions (2)
• 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: Stats in Practice Video Questions (1)
• 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 (9)
• 15: Review Exercises (3)
• 15: Concept Questions
• 15: Labs (6)

• 16: Stats in Practice Video Questions (1)
• 16.1: The Analysis of Variance—Single-Factor ANOVA and the F Test (10)
• 16.2: Multiple Comparisons (6)
• 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 MindTap Reader eBook now supported by HTML5 (non-flashed based) includes embedded media assets for a more integrated study experience
• New WebAssign Student User Experience that empowers learning at all levels with an upgraded, modern student interface

### 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 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.

### Tools to Explore Real Data with Technology

• 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

##### 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.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.SIP 1 001
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.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 39 001 002 003 005 007 009 010 011 012 014 016.MI 016.MI.SA 017 018.MI 018.MI.SA 020 024 025 026 027 028 030 031 033 036 037 038 042 043 044 045 047 049 051 052 054 056 057 059
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.SIP 1 001
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.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 50 001 003 004 005 007 010 011 012.MI 012.MI.SA 013.MI 013.MI.SA 014 015 017.MI 017.MI.SA 018 021 025 026.MI 026.MI.SA 028 029 030.MI 030.MI.SA 031.MI 031.MI.SA 032 033 035 036 037 039 041.MI 041.MI.SA 043 044 045 048 050.MI 050.MI.SA 051 054 055.MI 055.MI.SA 057 058.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 8 062 063 064 065 067 068 069 071
3.SIP 1 001
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.CQ 12 001 002 003 004 005 006 007 008 009 010 011 012
4.E 30 001 003 005 009 010 013 014 017 019.MI 019.MI.SA 021 022 024 025 029 031 032 036 037 039 042 044 045 046.MI 046.MI.SA 049 051 053 054 056
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 3 075 077 079
4.R 8 057 058 060 062 067 068 070 071
4.SIP 1 001
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.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.SIP 1 001
5.TB 4 001 002 003 004
Chapter 6: Random Variables and Probability Distributions
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 77 001 003 004 006 008 009 010 011 013 015 018.MI 018.MI.SA 019.MI 019.MI.SA 021 022 023.MI 023.MI.SA 024 025 026 027 028 030.MI 030.MI.SA 032 033 036.MI 036.MI.SA 038 040.MI 040.MI.SA 041.MI 041.MI.SA 042 044 047.MI 047.MI.SA 048 050.MI 050.MI.SA 051 054.MI 054.MI.SA 055 060.MI 060.MI.SA 061 063 070 071.MI 071.MI.SA 072 074 075.MI 075.MI.SA 077 078 080 081.MI 081.MI.SA 083 084 085.MI 085.MI.SA 087 088 090.MI 090.MI.SA 091 093.MI 093.MI.SA 094 095.MI 095.MI.SA 096 098
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 8 101 103 105 106 108 109 111 114
6.SIP 2 001 002
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.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
7.SIP 1 001
Chapter 8: Sampling Variability and Sampling Distributions
8.CQ 2 001 002
8.E 22 001 002 003 007 010 013 014 016 017 019.MI 019.MI.SA 022 023 025 028 029 030 031 034 035 038 040
8.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
8.R 6 041 042 045 049 050 052
8.SIP 1 001
Chapter 9: Estimating a Population Proportion
9.CQ 6 001 002 003 004 005 006
9.E 49 002 004 005 008 010 014 016 017 019 020 022 024.MI 024.MI.SA 025 026 028 031.MI 031.MI.SA 033 034 036 037 038 039 040 042.MI 042.MI.SA 044 046 047 048 049 050 051 053 055 057 058 060 062 064.MI 064.MI.SA 066.MI 066.MI.SA 067 069 070 072 074
9.JMP 4 001 002 003 004
9.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
9.R 8 078 079 081 082 084 085 087 090
9.SIP 1 001
10.CQ 8 001 002 003 004 005 006 007 008
10.E 50 002 004 005 006 009 010 014 016 018 019 021 023 025 026 030 031 032 033 035 036 038 039 041.MI 041.MI.SA 042 047 048 049 051.MI 051.MI.SA 052.MI 052.MI.SA 055 056 057 058 061 063 067 069.MI 069.MI.SA 071 073.MI 073.MI.SA 074 075 076 078 079 080
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 9 081 082 083 085 087 088 090 092 094
10.SIP 1 001
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.CQ 6 001 002 003 004 005 006
11.E 31 001 002 004.MI 004.MI.SA 005 006.MI 006.MI.SA 009 010 011 013.MI 013.MI.SA 016 018.MI 018.MI.SA 019.MI 019.MI.SA 020 023 024.MI 024.MI.SA 025 027.MI 027.MI.SA 029 031 032 033 035 036 039
11.JMP 4 001 002 003 004
11.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
11.R 4 041 043 046 047
11.SIP 1 001
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 51 001.MI 001.MI.SA 003 005 006.MI 006.MI.SA 007 008 010.MI 010.MI.SA 014.MI 014.MI.SA 016 018 020 021 022 024 026 027 028 030 031.MI 031.MI.SA 032.MI 032.MI.SA 033 036 038 039 041 042 043 044.MI 044.MI.SA 045 047 049.MI 049.MI.SA 050 051 056 058.MI 058.MI.SA 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 7 068 069 071 072 074 076 077
12.SIP 1 001
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.CQ 12 001 002 003 004 005 006 007 008 009 010 011 012
13.E 68 001 002 005.MI 005.MI.SA 007 009 010.MI 010.MI.SA 013 014.MI 014.MI.SA 016 017.MI 017.MI.SA 020 022 026 027.MI 027.MI.SA 029 030.MI 030.MI.SA 032 033 035 036 038.MI 038.MI.SA 040 043 044 046.MI 046.MI.SA 047 048 049 050 053 054 056 057 058 059 060 062.MI 062.MI.SA 063 065 066 068 070 071 072 073 074 075 077.MI 077.MI.SA 078 079 081 082 083 084 087 088 089 090
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 9 091 092 094 095 096 098 099 102 104
13.SIP 1 001
13.TB 94 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094
Chapter 14: Learning from Categorical Data
14.CQ 8 001 002 003 004 005 006 007 008
14.E 30 001 002.MI 002.MI.SA 003.MI 003.MI.SA 004 007 008 010 011.MI 011.MI.SA 012 014 015.MI 015.MI.SA 017 019.MI 019.MI.SA 020 021 023 026 032 033.MI 033.MI.SA 034 035.MI 035.MI.SA 036 038
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 7 040 041 042 044 045 047 048
14.SIP 2 001 002
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.E 31 001 002 003.MI 003.MI.SA 006.MI 006.MI.SA 007 009 010 011.MI 011.MI.SA 013 016 017 018.MI 018.MI.SA 020 021 022 026 027 029 030 031 033 035 037 038 039 040 043
15.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
15.R 3 046 047 050
15.SIP 1 001