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Elementary Statistics, 9th edition

Published by Pearson (January 7, 2015) © 2016

  • Neil A. Weiss Arizona State University

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For 1-semester courses in Introductory Statistics.

Statistically significant

Elementary Statistics presents statistics featuring data production and data analysis. Weiss uses careful, detailed explanations to ease the learning process. Parallel presentation of critical-value and P-value approaches to hypothesis testing is a unique design that offers instructors the flexibility to concentrate on one approach, or the opportunity for greater depth in comparing the two. The 9th Edition continues Weiss' tradition of cutting-edge pedagogy, technology and data analysis. It includes hundreds of new and updated exercises with real data from journals, magazines, newspapers and websites.

Hallmark features of this title

  • Real-world examples illustrate every concept. You Try It! exercises follow most worked examples, helping students immediately check their understanding.
  • Interpretation  boxes explain the meaning and significance of statistical results in everyday life and the importance of interpreting results.
  • What Does It Mean? boxes explain definitions, formulas and key facts in plain English, and summarize relevant discussions.
  • Procedure boxes include the “why, when, and how” of statistical methods.
  • Parallel Critical-Value/P-Value Approaches enable instructors to focus on one approach or to compare both approaches.

New and updated features of this title

  • All Technology Center features have been updated to reflect the latest software releases.
  • New Case Studies: roughly 60% of the chapter-opening case studies have been replaced.
  • New and revised Exercises: This edition contains about 2350 high-quality exercises, which far exceeds what is found in typical introductory statistics books. Over 35% of the exercises are new, updated, or modified.
  • Expanded Warm-up Exercises: Hundreds of “warm-up” exercises have been added. These exercises provide context-free problems that allow students to concentrate solely on the relevant concepts before moving on to applied exercises.
  • Chebyshev's Rule and the Empirical Rule: A new optional section of Chapter 3 is dedicated to an examination of Chebyshev's rule and the empirical rule. The empirical rule is further examined in Chapter 6 when the normal distribution is discussed.
  • Modified method for calculating quartiles is more easily accessible to students. A dedicated procedure that provides a step-by-step method for finding the quartiles of a data set has been included.

Highlights of the DIGITAL UPDATE for MyLab Statistics (available for Spring 2022 classes)

Instructors, contact your sales rep to ensure you have the most recent version of the course.

  • INCREASED: MyLab Item coverage with ~300 new items, including new “-P” P-Value Items and StatCrunch® Applet assessment questions.
  • UPDATED: Tutorial videos for statistical software like Excel, R and StatCrunch.
  • NEW: StatCrunch Project items. These MyLab items ask students to read data reports built in StatCrunch, then answer analysis questions about the data and reporting.

Features of MyLab Statistics for the 9th Edition; published 2021

  • Tutorial Exercises: The homework and practice exercises in MyLab Statistics align with the book's exercises, and most regenerate algorithmically for unlimited practice. Exercises offer immediate feedback, guided solutions, sample problems, animations, videos, statistical software tutorial videos and eText clips for extra help at point-of-use.
  • StatTalk Videos: Fun-loving statistician Andrew Vickers demonstrates important statistical concepts through interesting stories and real-life events.
  • Two additional Question Libraries accompany the algorithmically regenerated questions aligned with the book: (1) 450 exercises  in  Getting Ready for Statistics  cover the developmental math topics students need for the course. These can be assigned as a prerequisite to other assignments, if desired. (2) 1000 exercises  in the  Conceptual Question Library  require students to apply their statistical understanding.
  • StatCrunch: MyLab Statistics integrates StatCrunch so students can easily analyze data sets from exercises and the text. Access to www.statcrunch.com is included.
  • (NOTE: Each chapter concludes with Chapter in Review, Review Problems, Focusing on Data Analysis, Case Study Discussion, and Biography.)

  • Technology Resources
  • Data Sources

I: INTRODUCTION

  1. The Nature of Statistics
    • 1.1 Statistics Basics
    • 1.2 Simple Random Sampling
    • 1.3 Other Sampling Designs∗
    • 1.4 Experimental Designs∗

II: DESCRIPTIVE STATISTICS

  1. Organizing Data
    • Case Study: World's Richest People
    • 2.1 Variables and Data
    • 2.2 Organizing Qualitative Data
    • 2.3 Organizing Quantitative Data
    • 2.4 Distribution Shapes
    • 2.5 Misleading Graphs∗
  2. Descriptive Measures
    • Case Study: The Beatles' Song Length
    • 3.1 Measures of Center
    • 3.2 Measures of Variation
    • 3.3 Chebyshev's Rule and the Empirical Rule∗
    • 3.4 The Five-Number Summary; Boxplots
    • 3.5 Descriptive Measures for Populations; Use of Samples
  3. Descriptive Methods in Regression and Correlation
    • Case Study: Healthcare: Spending and Outcomes
    • 4.1 Linear Equations with One Independent Variable
    • 4.2 The Regression Equation
    • 4.3 The Coefficient of Determination
    • 4.4 Linear Correlation

III: PROBABILITY, RANDOM VARIABLES, AND SAMPLING DISTRIBUTIONS

  1. Probability and Random Variables
    • Case Study: Texas Hold ‘em
    • 5.1 Probability Basics
    • 5.2 Events
    • 5.3 Some Rules of Probability
    • 5.4 Discrete Random Variables and Probability Distributions∗
    • 5.5 The Mean and Standard Deviation of a Discrete Random Variable∗
    • 5.6 The Binomial Distribution∗
  2. The Normal Distribution
    • Case Study: Chest Sizes of Scottish Militiamen
    • 6.1 Introducing Normally Distributed Variables
    • 6.2 Areas under the Standard Normal Curve
    • 6.3 Working with Normally Distributed Variables
    • 6.4 Assessing Normality; Normal Probability Plots
  3. The Sampling Distribution of the Sample Mean
    • Case Study: The Chesapeake and Ohio Freight Study
    • 7.1 Sampling Error; the Need for Sampling Distributions
    • 7.2 The Mean and Standard Deviation of the Sample Mean
    • 7.3 The Sampling Distribution of the Sample Mean

IV: INFERENTIAL STATISTICS

  1. Confidence Intervals for One Population Mean
    • Case Study: Bank Robberies: A Statistical Analysis
    • 8.1 Estimating a Population Mean
    • 8.2 Confidence Intervals for One Population Mean When σ Is Known
    • 8.3 Confidence Intervals for One Population Mean When σ Is Unknown
  2. Hypothesis Tests for One Population Mean
    • Case Study: Gender and Sense of Direction
    • 9.1 The Nature of Hypothesis Testing
    • 9.2 Critical-Value Approach to Hypothesis Testing
    • 9.3 P-Value Approach to Hypothesis Testing
    • 9.4 Hypothesis Tests for One Population Mean When σ Is Known
    • 9.5 Hypothesis Tests for One Population Mean When σ Is Unknown 
  3. Inferences for Two Population Means
    • Case Study: Dexamethasone Therapy and IQ
    • 10.1 The Sampling Distribution of the Difference between Two Sample Means for Independent Samples
    • 10.2 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Assumed Equal
    • 10.3 Inferences for Two Population Means, Using Independent Samples: Standard Deviations Not Assumed Equal
    • 10.4 Inferences for Two Population Means, Using Paired Samples
  4. Inferences for Population Proportions
    • Case Study: Arrested Youths
    • 11.1 Confidence Intervals for One Population Proportion
    • 11.2 Hypothesis Tests for One Population Proportion
    • 11.3 Inferences for Two Population Proportions
  5. Chi-Square Procedures
    • Case Study: Eye and Hair Color
    • 12.1 The Chi-Square Distribution
    • 12.2 Chi-Square Goodness-of-Fit Test
    • 12.3 Contingency Tables; Association
    • 12.4 Chi-Square Independence Test
    • 12.5 Chi-Square Homogeneity Test
  6. Analysis of Variance (ANOVA)
    • Case Study: Self-Perception and Physical Activity
    • 13.1 The F-Distribution
    • 13.2 One-Way ANOVA: The Logic
    • 13.3 One-Way ANOVA: The Procedure
  7. Inferential Methods in Regression and Correlation
    • Case Study: Shoe Size and Height
    • 14.1 The Regression Model; Analysis of Residuals
    • 14.2 Inferences for the Slope of the Population Regression Line
    • 14.3 Estimation and Prediction
    • 14.4 Inferences in Correlation
  • *Indicates optional material.

APPENDICES

  1. Statistical Tables
  2. Answers to Selected Exercises

Index

Photo Credits

About our author

The late Neil A. Weiss received his Ph.D. from UCLA and subsequently accepted an assistant professor position at Arizona State University (ASU), where he was ultimately promoted to the rank of full professor. Dr. Weiss taught statistics, probability, and mathematics from the freshman level to the advanced graduate level for more than 30 years.

In recognition of his excellence in teaching, Dr. Weiss received the Dean's Quality Teaching Award from the ASU College of Liberal Arts and Sciences. He also was runner-up twice for the Charles Wexler Teaching Award in the ASU School of Mathematical and Statistical Sciences. Dr.Weiss's comprehensive knowledge and experience ensures that his texts are mathematically and statistically accurate, as well as pedagogically sound.

In addition to his numerous research publications, Dr. Weiss was the author of A Course in Probability (Addison-Wesley, 2006).He also authored or coauthored books in finite mathematics, statistics, and real analysis. His texts, well known for their precision, readability and pedagogical excellence, are used worldwide.

Dr. Weiss was a pioneer of the integration of statistical software into textbooks and the classroom, first providing such integration in the book Introductory Statistics (Addison-Wesley, 1982). Since this first publication, Neil Weiss's work continues to inspire future statisticians and current students of statistics alike.

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