Statistics, Global Edition

For courses in introductory statistics. A Contemporary Classic Classic, yet contemporary; theoretical, yet applied--McClave & Sincich's Statistics gives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. This new edition is extensively revised with an eye on clearer, more concise language throughout the text and in the exercises. Ideal for one- or two-semester courses in introductory statistics, this text assumes a mathematical background of basic algebra. Flexibility is built in for instructors who teach a more advanced course, with optional footnotes about calculus and the underlying theory. Pearson MyLab Statistics not included. Students, if Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. Pearson MyLab Statistics is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts. Front Cover Applet Correlation Title Page Copyright Page Contents Preface Applications Index Chapter 1 Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 1.6 The Role of Statistics in Critical Thinking and Ethics Statistics in Action: Social Media Network Usage—Are You Linked In? Using Technology: MINITAB: Accessing and Listing Data Chapter 2 Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Measures of Central Tendency 2.4 Numerical Measures of Variability 2.5 Using the Mean and Standard Deviation to Describe Data 2.6 Numerical Measures of Relative Standing 2.7 Methods for Detecting Outliers: Box Plots and z-Scores 2.8 Graphing Bivariate Relationships (Optional) 2.9 Distorting the Truth with Descriptive Statistics Statistics in Action: Body Image Dissatisfaction: Real or Imagined? Using Technology: MINITAB: Describing Data TI-83/TI–84 Plus Graphing Calculator: Describing Data Chapter 3 Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Some Additional Counting Rules (Optional) Bayes’s Rule (Optional) Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning? Using Technology: TI-83/TI-84 Plus Graphing Calculator: Combinations and Permutations Chapter 4 Discrete Random Variables 4.1 Two Types of Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 Expected Values of Discrete Random Variables 4.4 The Binomial Random Variable 4.5 The Poisson Random Variable (Optional) 4.6 The Hypergeometric Random Variable (Optional) Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold? Using Technology: MINITAB: Discrete probabilities TI-83/TI-84 Plus Graphing Calculator: Discrete Random Variables and Probabilities Chapter 5 Continuous Random Variables 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution 5.3 The Normal Distribution 5.4 Descriptive Methods for Assessing Normality 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) 5.6 The Exponential Distribution (Optional) Statistics in Action: Super Weapons Development—Is the Hit Ratio Optimized? Using Technology: MINITAB: Continuous Random Variable Probabilities and Normal Probability Plots TI–83/TI–84 Plus Graphing Calculator: Normal Random Variable and Normal Probability Plots Chapter 6 Sampling Distributions 6.1 The Concept of a Sampling Distribution 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance 6.3 The Sampling Distribution of x and the Central Limit Theorem 6.4 The Sampling Distribution of the Sample Proportion Statistics in Action: The Insomnia Pill: Is It Effective? Using Technology: MINITAB: Simulating a Sampling Distribution Chapter 7 Inferences Based on a Single Sample: Estimation with Confidence Intervals 7.1 Identifying and Estimating the Target Parameter 7.2 Confidence Interval for a Population Mean: Normal (z) Statistic 7.3 Confidence Interval for a Population Mean: Student’s t-Statistic 7.4 Large-Sample Confidence Interval for a Population Proportion 7.5 Determining the Sample Size 7.6 Confidence Interval for a Population Variance (Optional) Statistics in Action: Medicare Fraud Investigations Using Technology: MINITAB: Confidence Intervals TI-83/TI-84 Plus Graphing Calculator: Confidence Intervals Chapter 8 Inferences Based on a Single Sample: Tests of Hypothesis 8.1 The Elements of a Test of Hypothesis 8.2 Formulating Hypotheses and Setting Up the Rejection Region 8.3 Observed Significance Levels:p-Values 8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic 8.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic 8.6 Large-Sample Test of Hypothesis about a Population Proportion 8.7 Calculating Type II Error Probabilities: More about β (Optional) 8.8 Test of Hypothesis about a Population Variance (Optional) Statistics in Action: Diary of a KLEENEX® User—How Many Tissues in a Box? Using Technology: MINITAB: Tests of Hypotheses TI-83/TI-84 Plus Graphing Calculator: Tests of Hypotheses Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 9.1 Identifying the Target Parameter 9.2 Comparing Two Population Means: Independent Sampling 9.3 Comparing Two Population Means: Paired Difference Experiments 9.4 Comparing Two Population Proportions: Independent Sampling 9.5 Determining the Sample Size 9.6 Comparing Two Population Variances: Independent Sampling (Optional) Statistics in Action: ZixIt Corp. v. Visa USA Inc.—A Libel Case Using Technology: MINITAB: Two-Sample Inferences TI-83/TI-84 Plus Graphing Calculator: Two Sample Inferences Chapter 10 Analysis of Variance: Comparing More than Two Means 10.1 Elements of a Designed Study 10.2 The Completely Randomized Design: Single Factor 10.3 Multiple Comparisons of Means 10.4 The Randomized Block Design 10.5 Factorial Experiments: Two Factors Statistics in Action: Voice versus Face Recognition—Does One Follow the Other? Using Technology: MINITAB: Analysis of Variance TI–83/TI–84 Plus Graphing Calculator: Analysis of Variance Chapter 11 Simple Linear Regression 11.1 Probabilistic Models 11.2 Fitting the Model: The Least Squares Approach 11.3 Model Assumptions 11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1 11.5 The Coefficients of Correlation and Determination 11.6 Using the Model for Estimation and Prediction 11.7 A Complete Example Statistics in Action: Can “Dowsers” Really Detect Water? Using Technology: MINITAB: Simple Linear Regression TI-83/TI-84 Plus Graphing Calculator: Simple Linear Regression Chapter 12 Multiple Regression and Model Building 12.1 Multiple-Regression Models PART I: First-Order Models with Quantitative Independent Variables 12.2 Estimating and Making Inferences about the β Parameters 12.3 Evaluating Overall Model Utility 12.4 Using the Model for Estimation and Prediction PART II: Model Building in Multiple Regression 12.5 Interaction Models 12.6 Quadratic and Other Higher Order Models 12.7 Qualitative (Dummy) Variable Models 12.8 Models with Both Quantitative and Qualitative Variables (Optional) 12.9 Comparing Nested Models (Optional) 12.10 Stepwise Regression (Optional) PART III: Multiple Regression Diagnostics 12.11 Residual Analysis: Checking the Regression Assumptions 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Modeling Condominium Sales: What Factors Affect Auction Price? Using Technology: MINITAB: Multiple Regression TI-83/TI-84 Plus Graphing Calculator: Multiple Regression Chapter 13 Categorical Data Analysis 13.1 Categorical Data and the Multinomial Experiment 13.2 Testing Categorical Probabilities: One-Way Table 13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table 13.4 A Word of Caution about Chi-Square Tests Statistics in Action: The Case of the Ghoulish Transplant Tissue Using Technology: MINITAB: Chi-Square Analyses TI-83/TI-84 Plus Graphing Calculator: Chi-Square Analyses Appendix A: Summation Notation Appendix B: Tables Table I Binomial Probabilities Table II Normal Curve Areas Table III Critical Values of t Table IV Critical Values of x2 Table V Percentage Points of the F-Distribution, α = .10 Table VI Percentage Points of the F-Distribution, α = .05 Table VII Percentage Points of the F-Distribution, α = .025 Table VIII Percentage Points of the F-Distribution, α = .01 Table IX Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples Table X Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test Table XI Critical Values of Spearman’s Rank Correlation Coefficient Table XII Critical Values of the Studentized Range, α = .05 Table XIII Critical Values of the Studentized Range, α = .01 Appendix C: Calculation Formulas for Analysis of Variance Short Answers to Selected Odd-Numbered Exercises Index Credits Making Connections Using Data Back Cover For courses in introductory statistics. Classic, yet contemporary; theoretical, yet applied—McClave & Sincich's Statistics gives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasises inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. This new edition is extensively revised with an eye on clearer, more concise language throughout the text and in the exercises. Ideal for one- or two-semester courses in introductory statistics, this text assumes a mathematical background of basic algebra. Flexibility is built in for instructors who teach a more advanced course, with optional footnotes about calculus and the underlying theory. 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