MARC details
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07719nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20210120162022.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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210108b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780134210223 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
VITAP |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23rd |
| Classification number |
519.5 DE V |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
10470 |
| Personal name |
De Veaux, Richard D. |
| 245 ## - TITLE STATEMENT |
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| Title |
Intro Stats / |
| Statement of responsibility, etc. |
Richard D. De Veaux, Paul F. Velleman and David E. Bock |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
5th Ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Boston, USA |
| Name of publisher, distributor, etc. |
Pearson Education Inc. |
| Date of publication, distribution, etc. |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxvii,xxvii, 703p. : ill. ; A-1 to A-68; 28cm |
| 500 ## - GENERAL NOTE |
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| General note |
It includes Appendixes and Subject Index pages. |
| 521 ## - TARGET AUDIENCE NOTE |
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| Target audience note |
Encourages statistical thinking using technology, innovative methods, and a sense of humor<br/><br/>Inspired by the 2016 GAISE Report revision, Intro Stats, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data, while maintaining the book’s core concepts, coverage, and most importantly, readability.<br/><br/>By using technology and simulations to demonstrate variability at critical points throughout the course, the authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century.<br/>Also available with MyLab Statistics<br/><br/>MyLab™ Statistics is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world. Learn more about MyLab Statistics. <br/> Table of Contents<br/><br/>PART I: EXPLORING AND UNDERSTANDING DATA<br/><br/>1. Stats Starts here<br/><br/> 1.1 What Is Statistics?<br/> 1.2. Data<br/> 1.3 Variables<br/> 1.4 Models<br/><br/>2. Displaying and Describing Data<br/><br/> 2.1 Summarizing and Displaying a Categorical Variable<br/> 2.2 Displaying a Quantitative variable<br/> 2.3 Shape<br/> 2.4 Center<br/> 2.5 Spread<br/><br/>3. Relationships Between Categorical Variables — Contingency Tables<br/><br/> 3.1 Contingency tables<br/> 3.2 Conditional distributions<br/> 3.3 Displaying Contingency Tables<br/> 3.4 Three Categorical Variables<br/><br/>4. Understanding and Comparing Distributions<br/><br/> 4.1 Displays for Comparing Groups<br/> 4.2 Outliers<br/> 4.3 Re-Expressing Data: A First Look<br/><br/>5. The Standard Deviation as a Ruler and the Normal Model<br/><br/> 5.1 Using the standard deviation to Standardize Values<br/> 5.2 Shifting and scaling<br/> 5.3 Normal models<br/> 5.4 Working with Normal Percentiles<br/> 5.5 Normal Probability Plots<br/><br/>Part I Review<br/><br/><br/>PART II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES<br/><br/>6. Scatterplots, Association, and Correlation<br/><br/> 6.1 Scatterplots<br/> 6.2 Correlation<br/> 6.3 Warning: Correlation ≠ Causation<br/> 6.4 *Straightening Scatterplots<br/><br/>7. Linear Regression<br/><br/> 7.1 Least Squares: The Line of “Best Fit”<br/> 7.2 The Linear model<br/> 7.3 Finding the least squares line<br/> 7.4 Regression to the Mean<br/> 7.5 Examining the Residuals<br/> 7.6 R2–The Variation Accounted for by the Model<br/> 7.7 Regression Assumptions and Conditions<br/><br/>8. Regression Wisdom<br/><br/> 8.1 Examining Residuals<br/> 8.2 Extrapolation: Reaching Beyond the Data<br/> 8.3 Outliers, Leverage, and Influence<br/> 8.4 Lurking Variables and Causation<br/> 8.5 Working with Summary Values<br/> 8.6 * Straightening Scatterplots–The Three Goals<br/> 8.7 * Finding a Good Re-Expression<br/><br/>9. Multiple Regression<br/><br/> 9.1 What Is Multiple Regression?<br/> 9.2 Interpreting Multiple Regression Coefficients<br/> 9.3 The Multiple Regression Model–Assumptions and Conditions<br/> 9.4 Partial Regression Plots<br/> 9.5 Indicator Variables<br/><br/>Part II Review<br/><br/><br/>PART III: GATHERING DATA<br/><br/>10. Sample Surveys<br/><br/> 10.1 The Three Big Ideas of Sampling<br/> 10.2 Populations and Parameters<br/> 10.3 Simple Random Samples<br/> 10.4 Other Sampling Designs<br/> 10.5 From the Population to the Sample: You Can’t Always Get What You Want<br/> 10.6 The valid survey<br/> 10.7 Common Sampling Mistakes, or How to Sample Badly<br/><br/>11. Experiments and Observational Studies<br/><br/> 11.1 Observational Studies<br/> 11.2 Randomized, Comparative Experiments<br/> 11.3 The Four Principles of Experiment Design<br/> 11.4 Control Groups<br/> 11.5 Blocking<br/> 11.6 Confounding<br/><br/>Part III Review<br/><br/><br/>PART IV INFERENCE FOR ONE PARAMETER<br/><br/>12. From Randomness to Probability <br/><br/> 12.1 Random phenomena<br/> 12.2 Modeling Probability<br/> 12.3 Formal Probability<br/> 12.4. Conditional Probability and the General Multiplication Rule<br/> 12.5 Independence<br/> 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees<br/> 12.7 *Reversing the Conditioning: Bayes’ Rule<br/><br/>13. Sampling Distributions and Confidence Intervals for Proportions<br/><br/> 13.1 The Sampling Distribution for a Proportion<br/> 13.2 When Does the Normal Model Work? Assumptions and Conditions<br/> 13.3 A Confidence Interval for a Proportion<br/> 13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?<br/> 13.5 Margin of Error: Certainty vs. Precision <br/> 13.6 *Choosing your Sample Size<br/><br/>14. Confidence Intervals for Means<br/><br/> 14.1 The Central Limit Theorem<br/> 14.2 A Confidence interval for the Mean<br/> 14.3 Interpreting confidence intervals<br/> 14.4 *Picking our Interval up by our Bootstraps<br/> 14.5 Thoughts about Confidence Intervals <br/><br/>15. Testing Hypotheses<br/><br/> 15.1 Hypotheses<br/> 15.2 P-values<br/> 15.3 The Reasoning of Hypothesis Testing<br/> 15.4 A Hypothesis Test for the Mean<br/> 15.5 Intervals and Tests<br/> 15.6 P-Values and Decisions: What to Tell About a Hypothesis Test<br/><br/>16. More About Tests and Intervals <br/><br/> 16.1 Interpreting P-values<br/> 16.2 Alpha Levels and Critical Values<br/> 16.3 Practical vs Statistical Significance<br/> 16.4 Errors<br/><br/>Part IV Review<br/><br/><br/>PART V: INFERENCE FOR RELATIONSHIPS<br/><br/>17. Comparing Groups<br/><br/> 17.1 A Confidence Interval for the Difference Between Two Proportions<br/> 17.2 Assumptions and Conditions for Comparing Proportions<br/> 17.3 The Two-Sample z-Test: Testing the Difference Between Proportions<br/> 17.4 A Confidence Interval for the Difference Between Two Means<br/> 17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means<br/> 17.6 Randomization-Based Tests and Confidence Intervals for Two Means<br/> 17.7 *Pooling<br/> 17.8 *The Standard Deviation of a Difference<br/><br/>18. Paired Samples and Blocks<br/><br/> 18.1 Paired Data<br/> 18.2 Assumptions and Conditions<br/> 18.3 Confidence Intervals for Matched Pairs<br/> 18.4 Blocking<br/><br/>19. Comparing Counts<br/><br/> 19.1 Goodness-of-Fit Tests<br/> 19.2 Chi-Square Tests of Homogeneity<br/> 19.3 Examining the Residuals<br/> 19.4 Chi-Square Test of Independence<br/><br/>20. Inferences for Regression<br/><br/> 20.1 The Regression Model<br/> 20.2 Assumptions and Conditions<br/> 20.3 Regression Inference and Intuition<br/> 20.4 The Regression Table <br/> 20.5 Multiple Regression Inference<br/> 20.6 Confidence and Prediction Intervals<br/> 20.7 *Logistic Regression<br/><br/>Part V Review<br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
10471 |
| Topical term or geographic name entry element |
Statistics—Textbooks; |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
10472 |
| Personal name |
Velleman, Paul F. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
10473 |
| Personal name |
Bock, David E. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://www.pearson.com/us/higher-education/product/De-Veaux-Intro-Stats-5th-Edition/9780134210223.html">https://www.pearson.com/us/higher-education/product/De-Veaux-Intro-Stats-5th-Edition/9780134210223.html</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Reference Book |
| Edition |
5th ed. |
| Classification part |
519.5 |
| Call number suffix |
DE V |
