
Mind on Statistics
Utts, Jessica

Últimas novedades estadística

MIND ON STATISTICS emphasizes the conceptual development of statistical ideas and seeks to find meaning in data. Authors Jessica Utts and Robert Heckard satisfy students’ natural curiosity by actively engaging them with inspiring questions and explaining statistical topics in the context of excellent examples and case studies. MIND ON STATISTICS balances the spirit of statistical literacy with the statistical methodology taught in general introductory statistics courses. The authors built the book on two learning premises: (1) New material is much easier to learn and remember if it is related to something interesting or previously known; (2) New material is easier to learn if students actively ask questions and find the answers for themselves. More than any other text available, MIND ON STATISTICS motivates students to develop their statistical intuition by focusing on analyzing data and interpreting results, rather than on mathematical formulation. A wide range of interesting and real examples provides further motivation for students to learn about statistics.
Features
Compelling examples and "real life" case studies are relevant and help to motivate and engage students in the topic. This reinforces the premise of the text that something is easier to learn if it can be related to something interesting or previously learned. The book helps students expand their "frame of reference." Unique and motivating opening and closing chapters draw students into the course and the subject matter. These chapters clearly illustrate that statistical methods have contributed to our understanding of health, psychology, commerce, ecology, politics, music, lifestyle choices, and dozens of other topics. Thought Questions provide students with opportunities to engage in and explore a topic further. Students discover and verify important ideas for themselves. Hints on how to solve these problems are provided and complete answers are available on the Instructor’’s Suite CDROM. "Technical Notes" appear throughout to provide additional discussion on statistical techniques presented in the text. "In Summary" boxes appear at points within the chapter where students need them most, as opposed to being relegated to the end of a chapter or section. Skillbuilder Applet Activities, found throughout the text, use Java applets from CyberStats to explore the topic and the data further. These applets, which are on the Student’’s Suite CDROM accompanying each new text, give students more opportunity for handson learning and allow students to explore statistics on their own. Skillbuilder Applet Exercises at the end of the chapters make the applets easy to integrate into your teaching. Specific technology manuals providing instructions on how to use SPSS, MINITAB, Excel, R, JMP, and TI83/84 graphing calculator can be found on the Student’’s Suite CDROM. Icons will appear throughout the text near examples that reference the technology manuals, making it easy to integrate into your classroom. A superior supplements package includes the Student’’s Suite CDROM packaged with each new text. The CD features technology manuals which refer to specific examples in MIND ON STATISTICS and provides stepbystep assistance for learning SPSS, MINITAB, Excel, R, JMP, and TI83/84 graphing calculator. In addition, the Student’’s Suite CDROM contains Skillbuilder applets, journal articles referenced in the text, 5 supplemental chapters on additional topics, and data sets needed to complete specific exercises.
1. STATISTICS SUCCESS STORIES AND CAUTIONARY TALES. What is Statistics? Seven Statistical Stories with Morals. Case Study 1.1: Who Are Those Speedy Drivers? Case Study 1.2: Safety in the Skies. Case Study 1.3: Did Anyone Ask You Whom You’’ve Been Dating? Case Study 1.4: Who Are Those Angry Women? Case Study 1.5: Does Prayer Lower Blood Pressure? Case Study 1.6: Does Aspirin Reduce Heart Attack Rates? Case Study 1.7: Does the Internet Increase Loneliness and Depression? The Common Elements of the Seven Stories. 2. TURNING DATA INTO INFORMATION. Raw Data. Types of Variables. Summarizing One or Two Categorical Variables. Exploring Features of Quantitatve Data with Pictures. Numerical Summaries of Quantitative Variables. How to Handle Outliers. Features of BellShaped Distributions. Skillbuilder Applet: The Empirical Rule in Action. 3. SAMPLING: SURVEYS AND HOW TO ASK QUESTIONS. Collecting and Using Sample Data Wisely. Margin of Error, Confidence Intervals, and Sample Size. Choosing a Simple Random Sample. Other Sampling Methods. Difficulties and Disasters in Sampling. Case Study 3.1: The Infamous Literary Digest Poll of 1936. How to Ask Survey Questions. Case Study 3.2: No Opinion of Your Own? Let Politics Decide. Skillbuilder Applet: Random Sampling in Action. 4. GATHERING USEFUL DATA FOR EXAMINING RELATIONSHIPS. Speaking the Language of Research Studies. Case Study 4.1: Lead Exposure and Bad Teeth. Designing a Good Experiment. Case Study 4.2: Kids and Weight Lifting. Case Study 4.3: Quitting Smoking with Nicotine Patches. Designing a Good Observational Study. Case Study 4.4: Baldness and Heart Attacks. Difficulties and Disasters in Experiments and Observational Studies. 5. RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Looking for Patterns with Scatterplots. Describing Linear Patterns with a Regression Line. Measuring Strength and Direction with Correlation. Regression and Correlation Difficulties and Disasters. Correlation Does Not Prove Causation. Skillbuilder Applet: Exploring Correlation. Case Study 5.1: A Weighty Issue. 6. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES. Displaying Relationships Between Categorical Variables. Risk, Relative Risks, and Misleading Statistics About Risk. Case Study 6.1: Is Smoking More Dangerous for Women? The Effect of a Third Variable and Simpson’’s Paradox. Assessing the Statistical Significance of a 2 x 2 Table. Case Study 6.2: Drinking, Driving, and the Supreme Court. 7. PROBABILITY. Random Circumstances. Case Study 7.1: A Hypothetical Story—Alicia Has a Bad Day. Interpretations of Probability. Probability Definitions and Relationships. Basic Rules for Finding Probabilities. Strategies for Finding Complicated Probabilities. Using Simulation to Estimate Probabilities. Coincidences and Intuitive Judgments About Probability. Case Study 7.2: Doin’’ the iPod Shuffle. 8. RANDOM VARIABLES. What is a Random Variable? Discrete Random Variables. Expectations for Random Variables. Binomial Random Variables. Case Study 8.1: Does Caffeine Enhance the Taste of Cola? Continuous Random Variables. Normal Random Variables. Approximating Binomial Distribution Probabilities. Sums, Differences, and Combinations of Random Variables. 9. UNDERSTANDING SAMPLING DISTRIBUTIONS: STATISTICS AS RANDOM VARIABLES. Parameters, Statistics, and Statistical Inference. From Curiosity to Questions About Parameters. SD Module 0: An Overview of Sampling Distributions. SD Module 1: Sampling Distribution for One Sample Proportion. SD Module 2: Sampling Distribution for the Difference in Two Sample Proportions. SD Module 3: Sampling Distribution for One Sample Mean. SD Module 4: Sampling Distribution for the Sample Mean of Paired Differences. SD Module 5: Sampling Distribution for the Difference in Two Sample Means. Preparing for Statistical Inference: Standardized Statistics. Lesson 1: Standardized Statistics for Sampling Distributions. Lesson 2: Standardized Statistics for Proportions. Lesson 3: Standardized Statistics for Means. Generalizations Beyond the Big Five. Skillbuilder Applet: Finding the Pattern in Sample Means. Case Study 9.1: Do Americans Really Vote When They Say They Do? Table: Summary of Sampling Distributions. 10. ESTIMATING PROPORTIONS WITH CONFIDENCE. Introduction. CI Module 0: An Overview of Confidence Intervals. Lesson 1: Understanding Confidence Intervals. Lesson 2: Computing Confidence Intervals for the Five Scenarios. CI Module 1: Confidence Interval for a Population Proportion. Lesson 1: Details of How to Compute a Confidence Interval for a Population Proportion. Lesson 2: Understanding the Formula. Lesson 3: Reconciling Margin of Error and 95% Confidence Intervals. CI Module 2: Confidence Intervals for the Difference in Two Population Proportions. Using Confidence Intervals to Guide Decisions. Case Study 10.1: Extrasensory Perception Works With Movies. Case Study 10.2: Nicotine Patches Versus Zyban. Case Study 10.3: What a Great Personality. 11. ESTIMATING MEANS WITH CONFIDENCE. Introduction to Confidence Intervals for Means. CI Module 3: Confidence Intervals for One Population Mean. Lesson 1: Finding a Confidence Interval for a Mean For Any Sample Size and Any Confidence Level. Lesson 2: Special Case: Approximate 95% Confidence Intervals for Large Samples. CI Module 4: Confidence Interval for the Population Mean of Paired Differences. CI Module 5: Confidence Interval for the Difference in Two Population Means. Lesson 1: The General (Unpooled) Case. Lesson 2: The Equal Variance Assumption and the Pooled Standard Error. Understanding Any Confidence Interval. Case Study 11.1: Confidence Interval for Relative Risk: Case Study 4.4 Revisited. Case Study 11.2: Premenstrual Syndrome? Try Calcium. Skillbuilder Applet: The Confidence Level in Action. Table: Summary of Confidence Interval Procedures. 12. TESTING HYPOTHESES ABOUT PROPORTIONS. Introduction. HT Module 0: An Overview of Hypothesis Testing. Lesson 1: Formulating Hypothesis Statements. Lesson 2: The Logic and Details of Hypothesis Testing. Lesson 3: What Can Go Wrong: The Two Types of Errors and Their Probabilities. HT Module 1: Testing Hypotheses about a Population Proportion. HT Module 2: Testing Hypotheses about the Difference in Two Population Proportions. Sample Size, Statistical Significance and Practical Importance. Case Study 12.1: The Internet and Loneliness: Case Study 1.7 Revisited. Case Study 12.2: An Interpretation of a pValue Not Fit to Print. 13. TESTING HYPOTHESES ABOUT MEANS. Introduction to Hypothesis Tests for Means. HT Module 3: Testing Hypotheses about One Population Mean. HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences. HT Module 5: Testing Hypotheses about the Difference in Two Population Means. Lesson 1: The General (Unpooled) Case. Lesson 2: The Pooled TwoSample tTest. The Relationship Between Significance Tests and Confidence Intervals. Choosing an Appropriate Inference Procedure. Effect Size. Evaluating Significance in Research Reports. Table: Summary of Hypothesis Testing Procedures. 14. INFERENCE ABOUT SIMPLE REGRESSION. Sample and Population Regression Models. Estimating the Standard Deviation for Regression. Inference About the Linear Regression Relationship. Predicting y and Estimating Mean y at a Specific X. Checking Conditions for Using Regression Models for Inference. Case Study 14.1: A Contested Election. 15. MORE INFERENCE ABOUT CATEGORICAL VARIABLES. The ChiSquare Test for TwoWay Tables. Analyzing 2 x 2 Tables. Testing Hypotheses About One Categorical Variable: Goodness of Fit. Case Study 15.1: Do You Mind if I Eat the Blue Ones? 16. ANALYSIS OF VARIANCE. Comparing Means with an ANOVA Ftest. Details of OneWay Analysis of Variance. Other Methods for Comparing Populations. TwoWay Analysis of Variance. 17. TURNING INFORMATION INTO WISDOM. Beyond the Data. Transforming Uncertainty Into Wisdom. Making Personal Decisions. Control of Societal Risks. Understanding Our World. Getting to Know You. Words to the Wise. References. Appendix of Tables. Answers to Selected Exercises. Index.
SUPPLEMENTAL TOPICS (APPEAR ON THE STUDENT SUITE CDROM ONLY). 1. ADDITIONAL DISCRETE RANDOM VARIABLES. Hypergeometric Distribution. Poisson Distribution. Multinomial Distribution. 2. NONPARAMETRIC TESTS OF HYPOTHESES. The Sign Test. The TwoSample RankSum Test. The Wilcoxon SignedRank Test. The KruskalWallis Test. 3. MULTIPLE REGRESSION. The Multiple Linear Regression Model. Inference About Multiple Regression Models. Checking Conditions for Multiple Linear Regression. 4. TWOWAY ANALYSIS OF VARIANCE. Assumptions and Models for TwoWay ANOVA. Testing for Main Effects and Interactions. 5. ETHICS. Ethical Treatment of Human and Animal Participants. Assurance of Quality Data. Appropriate Statistical Analysis. Fair Reporting of Results. Case Study S5.1: Science Fair Project or Fair Science Project. Chapters 913, containing the core material on sampling distributions and statistical inference have been reorganized for maximum flexibility. Each of the topics of sampling distributions, confidence intervals, and hypothesis testing is broken up into six modules. The first module provides an introduction and the remaining five modules each deal with a specific parameter (one mean, one proportion, etc.). This modular format not only emphasizes the similarity among the inference procedures for the five parameters discussed, it also allows instructors to cover this material in any order they choose.Chapters 3 and 4 have been reorganized so that the basic topics of random sampling and surveys come before more complicated studies based on randomized experiments and observational studies.StatisticsNow™ (part of the CengageNOW suite of technology products), featured within chapters, is a robust, personalized online learning companion that helps students gauge their unique study needs and makes the most of their study time by building focused Personalized Learning Plans that reinforce key concepts. PreTests give students an initial assessment of their knowledge. Personalized Learning Plans, based on the students’’ answers to the pretest questions, outline key elements for review.Interactive Video Skillbuilders contain hours of helpful, interactive video instruction. These videos walk your students through key examples from the text, step by step ? giving them a foundation in the skills that they need to know. Video icons located in the margin guide students to view the video on the Skillbuilder CDROM.There are now 1,500 exercises in the text, up from 1,300 in the previous edition. The chapter on sampling distribution has 144 exercises (over 50% more than in the previous edition).Technology tips now appear throughout for MINITAB, SPSS, Excel, JMP, and TI83/84 calculators. Technical manuals are included on the Student’’s Suite CD for these technologies, as well as for JMP and R.Throughout the text, journal article icons will appear next to the examples and case studies that direct students to a journal article, enabling them to see how news stories are formulated from journal articles. The articles referenced appear on the Student’’s Suite CDROM.For the first time, an Activities Workbook is available with MIND ON STATISTICS. The Workbook includes a variety of activities for students to explore individually or in teams. These activities guide students through key features of the text, help them understand statistical concepts, provide handson data collection and interpretation teamwork, include exercises with tips incorporated for solution strategies, and provide bonus dataset activities. {Supplements} {Quotes} Jessica M. Utts Jessica Utts is a Professor of Statistics at the University of California at Davis, where she joined the faculty in 1978. She received her B.A. in Math and Psychology at SUNY Binghamton, and her M.A. and Ph.D. in Statistics at Penn State University. She is the author of SEEING THROUGH STATISTICS (3rd edition, 2005) and the coauthor with Robert Heckard of STATISTICAL IDEAS AND METHODS (1st edition, 2006) both published by Duxbury Press. She is also the EditorinChief of CYBERSTATS, an interactive online introductory statistics course. Jessica has been active in the Statistics Education community at the high school and college level. She served as a member and then chaired the Advanced Placement Statistics Development Committee for six years, and was a member of the American Statistical Association task force that produced the GAISE (Guidelines for Assessment and Instruction in Statistics Education) recommendations for Elementary Statistics courses. She is the recipient of the Academic Senate Distinguished Teaching Award and the Magnar Ronning Award for Teaching Excellence, both at the University of California at Davis. She is also a Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. Beyond statistics education Jessica’s major contributions have been in applying statistics to a variety of disciplines, most notably to parapsychology, the laboratory study of psychic phenomena. She has appeared on numerous television shows, including Larry King Live, ABC Nightline, CNN Morning News and 20/20, and most recently appears in a documentary included on the DVD with the movie "Suspect Zero."
Robert F. Heckard Robert F. Heckard is a senior lecturer in statistics at the Pennsylvania State University, where he has taught for over 30 years. He has taught introductory and intermediate applied statistics to more than 15,000 college students. Bob has been awarded several grants to develop multimedia and webbased instructional materials for teaching statistical concepts. He is the coauthor of STATISTICAL IDEAS AND METHODS (1st edition, 2006, Duxbury Press) and is a coauthor of CYBERSTATS, a webbased introductory course. As a consultant, he is active in the statistical analysis and design of highway safety research and has frequently been a consultant in cancer treatment clinical trials.
HomeInstructors New TitlesGeneral Chemistry TextbookPhysics for the Life Sciences TextbookOrder Inspection CopiesOnline ResourcesRep locatorStudents Online Student ResourcesBookshopsAuthors Review processProposal guidelinesUnited KingdomChange your regionSearch Go AccountingBusiness & TechnologyBusiness CommunicationBusiness EducationBusiness LawBusiness MathematicsBusiness StatisticsCareer Investigation & ReadinessCertificationCommunicationsComputer ApplicationsComputer EducationDecision SciencesEconomicsFinanceIntroduction to BusinessManagementMarketingOffice TechnologyReal EstateReferenceTaxationTechnologyAnthropologyArtCommunication and MediaCounselling/PsychotherapyCriminal JusticeDevelopmental EnglishEnglishEnglish as a Second LanguageFrenchGermanHistoryHuman ServicesItalianModern LanguageMusicPhilosophyPolitical SciencePsychologyReligionSocial WorkSociologySpanishAstronomyChemistryEarth ScienceEngineeringHealthLife SciencesMathematicsNutritionOceanographyPhysicsAssessment, Training, and ProjectsComputer ConceptsComputer ScienceDatabasesGame Design & DevelopmentGraphic CommunicationsHelp Desk/Desktop SupportInternetMedia Arts & DesignMISMusic TechnologyNetworking & SecurityOffice SuitesOperating SystemsPC Repair/A+Presentation ToolsProgrammingProject ManagementSoft SkillsSpreadsheetsWeb Design & DevelopmentWord ProcessingAgriscienceAutomotive and MechanicsAviationCatering and HospitalityCollege SuccessEducationElectronics and EngineeringHair & BeautyLeisure and ToursimNursing, Medical and DentistryPhotography, Multimedia and DesignProfessional Development and Study SkillsTradesCengage Learning EMEAEnglish Language TeachingGlobalHigher EducationLibrary & ReferenceAbout UsCopyright, Terms & ConditionsPrivacy PolicyContact UsCareersWebsite Design by Mulberry Interactive Ltds 
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1. STATISTICS SUCCESS STORIES AND CAUTIONARY TALES. What is Statistics? Seven Statistical Stories with Morals. Case Study 1.1: Who Are Those Speedy Drivers? Case Study 1.2: Safety in the Skies. Case Study 1.3: Did Anyone Ask You Whom You’’ve Been Dating? Case Study 1.4: Who Are Those Angry Women? Case Study 1.5: Does Prayer Lower Blood Pressure? Case Study 1.6: Does Aspirin Reduce Heart Attack Rates? Case Study 1.7: Does the Internet Increase Loneliness and Depression? The Common Elements of the Seven Stories. 2. TURNING DATA INTO INFORMATION. Raw Data. Types of Variables. Summarizing One or Two Categorical Variables. Exploring Features of Quantitatve Data with Pictures. Numerical Summaries of Quantitative Variables. How to Handle Outliers. Features of BellShaped Distributions. Skillbuilder Applet: The Empirical Rule in Action. 3. SAMPLING: SURVEYS AND HOW TO ASK QUESTIONS. Collecting and Using Sample Data Wisely. Margin of Error, Confidence Intervals, and Sample Size. Choosing a Simple Random Sample. Other Sampling Methods. Difficulties and Disasters in Sampling. Case Study 3.1: The Infamous Literary Digest Poll of 1936. How to Ask Survey Questions. Case Study 3.2: No Opinion of Your Own? Let Politics Decide. Skillbuilder Applet: Random Sampling in Action. 4. GATHERING USEFUL DATA FOR EXAMINING RELATIONSHIPS. Speaking the Language of Research Studies. Case Study 4.1: Lead Exposure and Bad Teeth. Designing a Good Experiment. Case Study 4.2: Kids and Weight Lifting. Case Study 4.3: Quitting Smoking with Nicotine Patches. Designing a Good Observational Study. Case Study 4.4: Baldness and Heart Attacks. Difficulties and Disasters in Experiments and Observational Studies. 5. RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES. Looking for Patterns with Scatterplots. Describing Linear Patterns with a Regression Line. Measuring Strength and Direction with Correlation. Regression and Correlation Difficulties and Disasters. Correlation Does Not Prove Causation. Skillbuilder Applet: Exploring Correlation. Case Study 5.1: A Weighty Issue. 6. RELATIONSHIPS BETWEEN CATEGORICAL VARIABLES. Displaying Relationships Between Categorical Variables. Risk, Relative Risks, and Misleading Statistics About Risk. Case Study 6.1: Is Smoking More Dangerous for Women? The Effect of a Third Variable and Simpson’’s Paradox. Assessing the Statistical Significance of a 2 x 2 Table. Case Study 6.2: Drinking, Driving, and the Supreme Court. 7. PROBABILITY. Random Circumstances. Case Study 7.1: A Hypothetical Story—Alicia Has a Bad Day. Interpretations of Probability. Probability Definitions and Relationships. Basic Rules for Finding Probabilities. Strategies for Finding Complicated Probabilities. Using Simulation to Estimate Probabilities. Coincidences and Intuitive Judgments About Probability. Case Study 7.2: Doin’’ the iPod Shuffle. 8. RANDOM VARIABLES. What is a Random Variable? Discrete Random Variables. Expectations for Random Variables. Binomial Random Variables. Case Study 8.1: Does Caffeine Enhance the Taste of Cola? Continuous Random Variables. Normal Random Variables. Approximating Binomial Distribution Probabilities. Sums, Differences, and Combinations of Random Variables. 9. UNDERSTANDING SAMPLING DISTRIBUTIONS: STATISTICS AS RANDOM VARIABLES. Parameters, Statistics, and Statistical Inference. From Curiosity to Questions About Parameters. SD Module 0: An Overview of Sampling Distributions. SD Module 1: Sampling Distribution for One Sample Proportion. SD Module 2: Sampling Distribution for the Difference in Two Sample Proportions. SD Module 3: Sampling Distribution for One Sample Mean. SD Module 4: Sampling Distribution for the Sample Mean of Paired Differences. SD Module 5: Sampling Distribution for the Difference in Two Sample Means. Preparing for Statistical Inference: Standardized Statistics. Lesson 1: Standardized Statistics for Sampling Distributions. Lesson 2: Standardized Statistics for Proportions. Lesson 3: Standardized Statistics for Means. Generalizations Beyond the Big Five. Skillbuilder Applet: Finding the Pattern in Sample Means. Case Study 9.1: Do Americans Really Vote When They Say They Do? Table: Summary of Sampling Distributions. 10. ESTIMATING PROPORTIONS WITH CONFIDENCE. Introduction. CI Module 0: An Overview of Confidence Intervals. Lesson 1: Understanding Confidence Intervals. Lesson 2: Computing Confidence Intervals for the Five Scenarios. CI Module 1: Confidence Interval for a Population Proportion. Lesson 1: Details of How to Compute a Confidence Interval for a Population Proportion. Lesson 2: Understanding the Formula. Lesson 3: Reconciling Margin of Error and 95% Confidence Intervals. CI Module 2: Confidence Intervals for the Difference in Two Population Proportions. Using Confidence Intervals to Guide Decisions. Case Study 10.1: Extrasensory Perception Works With Movies. Case Study 10.2: Nicotine Patches Versus Zyban. Case Study 10.3: What a Great Personality. 11. ESTIMATING MEANS WITH CONFIDENCE. Introduction to Confidence Intervals for Means. CI Module 3: Confidence Intervals for One Population Mean. Lesson 1: Finding a Confidence Interval for a Mean For Any Sample Size and Any Confidence Level. Lesson 2: Special Case: Approximate 95% Confidence Intervals for Large Samples. CI Module 4: Confidence Interval for the Population Mean of Paired Differences. CI Module 5: Confidence Interval for the Difference in Two Population Means. Lesson 1: The General (Unpooled) Case. Lesson 2: The Equal Variance Assumption and the Pooled Standard Error. Understanding Any Confidence Interval. Case Study 11.1: Confidence Interval for Relative Risk: Case Study 4.4 Revisited. Case Study 11.2: Premenstrual Syndrome? Try Calcium. Skillbuilder Applet: The Confidence Level in Action. Table: Summary of Confidence Interval Procedures. 12. TESTING HYPOTHESES ABOUT PROPORTIONS. Introduction. HT Module 0: An Overview of Hypothesis Testing. Lesson 1: Formulating Hypothesis Statements. Lesson 2: The Logic and Details of Hypothesis Testing. Lesson 3: What Can Go Wrong: The Two Types of Errors and Their Probabilities. HT Module 1: Testing Hypotheses about a Population Proportion. HT Module 2: Testing Hypotheses about the Difference in Two Population Proportions. Sample Size, Statistical Significance and Practical Importance. Case Study 12.1: The Internet and Loneliness: Case Study 1.7 Revisited. Case Study 12.2: An Interpretation of a pValue Not Fit to Print. 13. TESTING HYPOTHESES ABOUT MEANS. Introduction to Hypothesis Tests for Means. HT Module 3: Testing Hypotheses about One Population Mean. HT Module 4: Testing Hypotheses about the Population Mean of Paired Differences. HT Module 5: Testing Hypotheses about the Difference in Two Population Means. Lesson 1: The General (Unpooled) Case. Lesson 2: The Pooled TwoSample tTest. The Relationship Between Significance Tests and Confidence Intervals. Choosing an Appropriate Inference Procedure. Effect Size. Evaluating Significance in Research Reports. Table: Summary of Hypothesis Testing Procedures. 14. INFERENCE ABOUT SIMPLE REGRESSION. Sample and Population Regression Models. Estimating the Standard Deviation for Regression. Inference About the Linear Regression Relationship. Predicting y and Estimating Mean y at a Specific X. Checking Conditions for Using Regression Models for Inference. Case Study 14.1: A Contested Election. 15. MORE INFERENCE ABOUT CATEGORICAL VARIABLES. The ChiSquare Test for TwoWay Tables. Analyzing 2 x 2 Tables. Testing Hypotheses About One Categorical Variable: Goodness of Fit. Case Study 15.1: Do You Mind if I Eat the Blue Ones? 16. ANALYSIS OF VARIANCE. Comparing Means with an ANOVA Ftest. Details of OneWay Analysis of Variance. Other Methods for Comparing Populations. TwoWay Analysis of Variance. 17. TURNING INFORMATION INTO WISDOM. Beyond the Data. Transforming Uncertainty Into Wisdom. Making Personal Decisions. Control of Societal Risks. Understanding Our World. Getting to Know You. Words to the Wise. References. Appendix of Tables. Answers to Selected Exercises. Index.
SUPPLEMENTAL TOPICS (APPEAR ON THE STUDENT SUITE CDROM ONLY). 1. ADDITIONAL DISCRETE RANDOM VARIABLES. Hypergeometric Distribution. Poisson Distribution. Multinomial Distribution. 2. NONPARAMETRIC TESTS OF HYPOTHESES. The Sign Test. The TwoSample RankSum Test. The Wilcoxon SignedRank Test. The KruskalWallis Test. 3. MULTIPLE REGRESSION. The Multiple Linear Regression Model. Inference About Multiple Regression Models. Checking Conditions for Multiple Linear Regression. 4. TWOWAY ANALYSIS OF VARIANCE. Assumptions and Models for TwoWay ANOVA. Testing for Main Effects and Interactions. 5. ETHICS. Ethical Treatment of Human and Animal Participants. Assurance of Quality Data. Appropriate Statistical Analysis. Fair Reporting of Results. Case Study S5.1: Science Fair Project or Fair Science Project. Chapters 913, containing the core material on sampling distributions and statistical inference have been reorganized for maximum flexibility. Each of the topics of sampling distributions, confidence intervals, and hypothesis testing is broken up into six modules. The first module provides an introduction and the remaining five modules each deal with a specific parameter (one mean, one proportion, etc.). This modular format not only emphasizes the similarity among the inference procedures for the five parameters discussed, it also allows instructors to cover this material in any order they choose.Chapters 3 and 4 have been reorganized so that the basic topics of random sampling and surveys come before more complicated studies based on randomized experiments and observational studies.StatisticsNow™ (part of the CengageNOW suite of technology products), featured within chapters, is a robust, personalized online learning companion that helps students gauge their unique study needs and makes the most of their study time by building focused Personalized Learning Plans that reinforce key concepts. PreTests give students an initial assessment of their knowledge. Personalized Learning Plans, based on the students’’ answers to the pretest questions, outline key elements for review.Interactive Video Skillbuilders contain hours of helpful, interactive video instruction. These videos walk your students through key examples from the text, step by step ? giving them a foundation in the skills that they need to know. Video icons located in the margin guide students to view the video on the Skillbuilder CDROM.There are now 1,500 exercises in the text, up from 1,300 in the previous edition. The chapter on sampling distribution has 144 exercises (over 50% more than in the previous edition).Technology tips now appear throughout for MINITAB, SPSS, Excel, JMP, and TI83/84 calculators. Technical manuals are included on the Student’’s Suite CD for these technologies, as well as for JMP and R.Throughout the text, journal article icons will appear next to the examples and case studies that direct students to a journal article, enabling them to see how news stories are formulated from journal articles. The articles referenced appear on the Student’’s Suite CDROM.For the first time, an Activities Workbook is available with MIND ON STATISTICS. The Workbook includes a variety of activities for students to explore individually or in teams. These activities guide students through key features of the text, help them understand statistical concepts, provide handson data collection and interpretation teamwork, include exercises with tips incorporated for solution strategies, and provide bonus dataset activities. {Supplements} {Quotes} Jessica M. Utts Jessica Utts is a Professor of Statistics at the University of California at Davis, where she joined the faculty in 1978. She received her B.A. in Math and Psychology at SUNY Binghamton, and her M.A. and Ph.D. in Statistics at Penn State University. She is the author of SEEING THROUGH STATISTICS (3rd edition, 2005) and the coauthor with Robert Heckard of STATISTICAL IDEAS AND METHODS (1st edition, 2006) both published by Duxbury Press. She is also the EditorinChief of CYBERSTATS, an interactive online introductory statistics course. Jessica has been active in the Statistics Education community at the high school and college level. She served as a member and then chaired the Advanced Placement Statistics Development Committee for six years, and was a member of the American Statistical Association task force that produced the GAISE (Guidelines for Assessment and Instruction in Statistics Education) recommendations for Elementary Statistics courses. She is the recipient of the Academic Senate Distinguished Teaching Award and the Magnar Ronning Award for Teaching Excellence, both at the University of California at Davis. She is also a Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the American Association for the Advancement of Science. Beyond statistics education Jessica’s major contributions have been in applying statistics to a variety of disciplines, most notably to parapsychology, the laboratory study of psychic phenomena. She has appeared on numerous television shows, including Larry King Live, ABC Nightline, CNN Morning News and 20/20, and most recently appears in a documentary included on the DVD with the movie "Suspect Zero."
Robert F. Heckard Robert F. Heckard is a senior lecturer in statistics at the Pennsylvania State University, where he has taught for over 30 years. He has taught introductory and intermediate applied statistics to more than 15,000 college students. Bob has been awarded several grants to develop multimedia and webbased instructional materials for teaching statistical concepts. He is the coauthor of STATISTICAL IDEAS AND METHODS (1st edition, 2006, Duxbury Press) and is a coauthor of CYBERSTATS, a webbased introductory course. As a consultant, he is active in the statistical analysis and design of highway safety research and has frequently been a consultant in cancer treatment clinical trials.
HomeInstructors New TitlesGeneral Chemistry TextbookPhysics for the Life Sciences TextbookOrder Inspection CopiesOnline ResourcesRep locatorStudents Online Student ResourcesBookshopsAuthors Review processProposal guidelinesUnited KingdomChange your regionSearch Go AccountingBusiness & TechnologyBusiness CommunicationBusiness EducationBusiness LawBusiness MathematicsBusiness StatisticsCareer Investigation & ReadinessCertificationCommunicationsComputer ApplicationsComputer EducationDecision SciencesEconomicsFinanceIntroduction to BusinessManagementMarketingOffice TechnologyReal EstateReferenceTaxationTechnologyAnthropologyArtCommunication and MediaCounselling/PsychotherapyCriminal JusticeDevelopmental EnglishEnglishEnglish as a Second LanguageFrenchGermanHistoryHuman ServicesItalianModern LanguageMusicPhilosophyPolitical SciencePsychologyReligionSocial WorkSociologySpanishAstronomyChemistryEarth ScienceEngineeringHealthLife SciencesMathematicsNutritionOceanographyPhysicsAssessment, Training, and ProjectsComputer ConceptsComputer ScienceDatabasesGame Design & DevelopmentGraphic CommunicationsHelp Desk/Desktop SupportInternetMedia Arts & DesignMISMusic TechnologyNetworking & SecurityOffice SuitesOperating SystemsPC Repair/A+Presentation ToolsProgrammingProject ManagementSoft SkillsSpreadsheetsWeb Design & DevelopmentWord ProcessingAgriscienceAutomotive and MechanicsAviationCatering and HospitalityCollege SuccessEducationElectronics and EngineeringHair & BeautyLeisure and ToursimNursing, Medical and DentistryPhotography, Multimedia and DesignProfessional Development and Study SkillsTradesCengage Learning EMEAEnglish Language TeachingGlobalHigher EducationLibrary & ReferenceAbout UsCopyright, Terms & ConditionsPrivacy PolicyContact UsCareersWebsite Design by Mulberry Interactive Ltd 

