Statistics: Unlocking the Power of Data, 2e Wileyplus (Next Generation) + Loose-Leaf
2nd Edition
Published on 19. June 2018
Book
Paperback/Softback
978-1-119-49132-3 (ISBN)
Description
Before you purchase, check with your instructor or review your course syllabus to ensure that your instructor requires WileyPLUS. If your course ID starts with an "A" your class is using the next generation of WileyPLUS. This packages includes a loose-leaf edition of Statistics: Unlocking the Power of Data, 2nd Edition, a registration code for WileyPLUS (next generation), and 6 months access to the eTextbook edition as part of the course (accessible online and offline).
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Statistics: Unlocking the Power of Data is designed for use in an introductory statistics course. The focus throughout is on data analysis, and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data.
For customer technical support, please visit http://www.wileyplus.com/support. WileyPLUS registration cards are only included with new products. Used and rental products may not include WileyPLUS registration cards.
Statistics: Unlocking the Power of Data is designed for use in an introductory statistics course. The focus throughout is on data analysis, and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data.
More details
Edition
2nd ed.
Language
English
Place of publication
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 272 mm
Width: 211 mm
Thickness: 30 mm
Weight
1581 gr
ISBN-13
978-1-119-49132-3 (9781119491323)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Content
Preface ix
Unit A: Data 1
Chapter 1. Collecting Data 2
1.1. The Structure of Data 4
1.2. Sampling from a Population 16
1.3. Experiments and Observational Studies 29
Chapter 2. Describing Data 46
2.1. Categorical Variables 48
2.2. One Quantitative Variable: Shape and Center 63
2.3. One Quantitative Variable: Measures of Spread 77
2.4. Boxplots and Quantitative/Categorical Relationships 93
2.5. Two Quantitative Variables: Scatterplot and Correlation 106
2.6. Two Quantitative Variables: Linear Regression 123
2.7. Data Visualization and Multiple Variables 137
Unit A: Essential Synthesis 161
Review Exercises 174
Projects Online
Unit B: Understanding Inference 193
Chapter 3. Confidence Intervals 194
3.1. Sampling Distributions 196
3.2. Understanding and Interpreting Confidence Intervals 213
3.3. Constructing Bootstrap Confidence Intervals 228
3.4. Bootstrap Confidence Intervals using Percentiles 242
Chapter 4. Hypothesis Tests 256
4.1. Introducing Hypothesis Tests 258
4.2. Measuring Evidence with P-values 272
4.3. Determining Statistical Significance 288
4.4. A Closer Look at Testing 303
4.5. Making Connections 318
Unit B: Essential Synthesis 341
Review Exercises 351
Projects Online
Unit C: Inference with Normal and t-Distributions 369
Chapter 5. Approximating with a Distribution 370
5.1. Hypothesis Tests Using Normal Distributions 372
5.2. Confidence Intervals Using Normal Distributions 387
Chapter 6. Inference for Means and Proportions 402
6.1. Inference for a Proportion
6.1-D Distribution of a Proportion 404
6.1-CI Confidence Interval for a Proportion 407
6.1-HT Hypothesis Test for a Proportion 414
6.2. Inference for a Mean
6.2-D Distribution of a Mean 419
6.2-CI Confidence Interval for a Mean 424
6.2-HT Hypothesis Test for a Mean 433
6.3. Inference for a Difference in Proportions
6.3-D Distribution of a Difference in Proportions 438
6.3-CI Confidence Interval for a Difference in Proportions 441
6.3-HT Hypothesis Test for a Difference in Proportions 446
6.4. Inference for a Difference in Means
6.4-D Distribution of a Difference in Means 452
6.4-CI Confidence Interval for a Difference in Means 455
6.4-HT Hypothesis Test for a Difference in Means 461
6.5. Paired Difference in Means 468
Unit C: Essential Synthesis 477
Review Exercises 489
Projects Online
Unit D: Inference for Multiple Parameters 505
Chapter 7. Chi-Square Tests for Categorical Variables 506
7.1. Testing Goodness-of-Fit for a Single Categorical Variable 508
7.2. Testing for an Association between Two Categorical Variables 523
Chapter 8. ANOVA to Compare Means 538
8.1. Analysis of Variance 540
8.2. Pairwise Comparisons and Inference after ANOVA 563
Chapter 9. Inference for Regression 574
9.1. Inference for Slope and Correlation 576
9.2. ANOVA for Regression 591
9.3. Confidence and Prediction Intervals 603
Chapter 10. Multiple Regression 610
10.1. Multiple Predictors 612
10.2. Checking Conditions for a Regression Model 624
10.3. Using Multiple Regression 633
Unit D: Essential Synthesis 647
Review Exercises 661
Projects Online
The Big Picture: Essential Synthesis 669
Exercises for the Big Picture: Essential Synthesis 683
Chapter P. Probability Basics 688
P.1. Probability Rules 690
P.2. Tree Diagrams and Bayes' Rule 702
P.3. Random Variables and Probability Functions 709
P.4. Binomial Probabilities 716
P.5. Density Curves and the Normal Distribution 724
Appendix A. Chapter Summaries 737
Appendix B. Selected Dataset Descriptions 749
Partial Answers 761
Index
General Index 783
Data Index 786
Unit A: Data 1
Chapter 1. Collecting Data 2
1.1. The Structure of Data 4
1.2. Sampling from a Population 16
1.3. Experiments and Observational Studies 29
Chapter 2. Describing Data 46
2.1. Categorical Variables 48
2.2. One Quantitative Variable: Shape and Center 63
2.3. One Quantitative Variable: Measures of Spread 77
2.4. Boxplots and Quantitative/Categorical Relationships 93
2.5. Two Quantitative Variables: Scatterplot and Correlation 106
2.6. Two Quantitative Variables: Linear Regression 123
2.7. Data Visualization and Multiple Variables 137
Unit A: Essential Synthesis 161
Review Exercises 174
Projects Online
Unit B: Understanding Inference 193
Chapter 3. Confidence Intervals 194
3.1. Sampling Distributions 196
3.2. Understanding and Interpreting Confidence Intervals 213
3.3. Constructing Bootstrap Confidence Intervals 228
3.4. Bootstrap Confidence Intervals using Percentiles 242
Chapter 4. Hypothesis Tests 256
4.1. Introducing Hypothesis Tests 258
4.2. Measuring Evidence with P-values 272
4.3. Determining Statistical Significance 288
4.4. A Closer Look at Testing 303
4.5. Making Connections 318
Unit B: Essential Synthesis 341
Review Exercises 351
Projects Online
Unit C: Inference with Normal and t-Distributions 369
Chapter 5. Approximating with a Distribution 370
5.1. Hypothesis Tests Using Normal Distributions 372
5.2. Confidence Intervals Using Normal Distributions 387
Chapter 6. Inference for Means and Proportions 402
6.1. Inference for a Proportion
6.1-D Distribution of a Proportion 404
6.1-CI Confidence Interval for a Proportion 407
6.1-HT Hypothesis Test for a Proportion 414
6.2. Inference for a Mean
6.2-D Distribution of a Mean 419
6.2-CI Confidence Interval for a Mean 424
6.2-HT Hypothesis Test for a Mean 433
6.3. Inference for a Difference in Proportions
6.3-D Distribution of a Difference in Proportions 438
6.3-CI Confidence Interval for a Difference in Proportions 441
6.3-HT Hypothesis Test for a Difference in Proportions 446
6.4. Inference for a Difference in Means
6.4-D Distribution of a Difference in Means 452
6.4-CI Confidence Interval for a Difference in Means 455
6.4-HT Hypothesis Test for a Difference in Means 461
6.5. Paired Difference in Means 468
Unit C: Essential Synthesis 477
Review Exercises 489
Projects Online
Unit D: Inference for Multiple Parameters 505
Chapter 7. Chi-Square Tests for Categorical Variables 506
7.1. Testing Goodness-of-Fit for a Single Categorical Variable 508
7.2. Testing for an Association between Two Categorical Variables 523
Chapter 8. ANOVA to Compare Means 538
8.1. Analysis of Variance 540
8.2. Pairwise Comparisons and Inference after ANOVA 563
Chapter 9. Inference for Regression 574
9.1. Inference for Slope and Correlation 576
9.2. ANOVA for Regression 591
9.3. Confidence and Prediction Intervals 603
Chapter 10. Multiple Regression 610
10.1. Multiple Predictors 612
10.2. Checking Conditions for a Regression Model 624
10.3. Using Multiple Regression 633
Unit D: Essential Synthesis 647
Review Exercises 661
Projects Online
The Big Picture: Essential Synthesis 669
Exercises for the Big Picture: Essential Synthesis 683
Chapter P. Probability Basics 688
P.1. Probability Rules 690
P.2. Tree Diagrams and Bayes' Rule 702
P.3. Random Variables and Probability Functions 709
P.4. Binomial Probabilities 716
P.5. Density Curves and the Normal Distribution 724
Appendix A. Chapter Summaries 737
Appendix B. Selected Dataset Descriptions 749
Partial Answers 761
Index
General Index 783
Data Index 786