Statistics, Updated Edition, 13th edition

  • James T. McClave, 
  • Terry T Sincich

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Statistics gives you a complete introduction to statistics emphasizing inference and integrating real data. The authors stress the development of statistical thinking, the assessment of credibility and the value of data-driven inferences. Practice exercises help you grasp the mechanics of statistical techniques.

Published by Pearson (July 15th 2020) - Copyright © 2021

ISBN-13: 9780136881285

Subject: Introductory Statistics

Category: Statistics, Updated Edition

Table of contents

Table of Contents

  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
  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
  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)
    • 3.8 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
  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
  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
  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-bar) 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
  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
  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
  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
  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
  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
  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
  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
  14. Nonparametric Statistics (available online)
    • 14.1 Introduction: Distribution-Free Tests
    • 14.2 Single-Population Inferences
    • 14.3 Comparing Two Populations: Independent Samples
    • 14.4 Comparing Two Populations: Paired Difference Experiment
    • 14.5 Comparing Three or More Populations: Completely Randomized Design
    • 14.6 Comparing Three or More Populations: Randomized Block Design
    • 14.7 Rank Correlation
      • Statistics in Action: Pollutants at a Housing Development: A Case of Mishandling Small Samples
      • Using Technology: MINITAB: Nonparametric Tests

APPENDIX A: Summation Notation


APPENDIX C: Calculation Formulas for Analysis of Variance

Short Answers to Selected Odd-Numbered Exercises


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