Statistics, Updated Edition

Statistics, Updated Edition, 13th edition

  • James T. McClave, 
  • Terry T Sincich

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Overview

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: Statistics, Introductory

Category: Introductory Statistics

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 14-48

Statistics in Action: Pollutants at a Housing Development: A Case of Mishandling Small Samples 14-2
Using Technology: MINITAB: Nonparametric Tests 14-65

APPENDIX A: Summation Notation
APPENDIX B: Tables
APPENDIX C: Calculation Formulas for Analysis of Variance

Short Answers to Selected Odd-Numbered Exercises 
Index 

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