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Just the Essentials of Elementary Statistics (with InfoTrac and CD-ROM) / Edition 3

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Overview

Written by two of the most respected leaders in statistics education, JUST THE ESSENTIALS OF ELEMENTARY STATISTICS, Third Edition, is the "essentials" version of Johnson and Kuby's market-leading Eighth Edition of ELEMENTARY STATISTICS. This version is tailored to students who need a working knowledge of statistics, but do not have a strong background in mathematics. The text promotes learning, understanding, and motivation by presenting statistics in a context that relates to personal experiences. Statistics is presented as a useful tool in understanding the world around us through real world applications in areas such as business, economics, engineering, and the physical and natural sciences.

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Editorial Reviews

From The Critics
The new edition of a textbook for a one-semester introductory statistics course. The material has a strong focus on interpreting computer output. Chapters cover probability and inferential statistics. The CD-ROM contains interactive exercises, data sets, and other related material. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Booknews
New edition of a text that presents a concise introduction to statistics that can be covered in a one-term course. It presents not only descriptive and inferential concepts, but also their real-world applications in such fields as the physical and social sciences, business, economics, and engineering. Eleven chapters cover descriptive statistics, probability, and inferential statistics. They include case studies, increased focus on interpreting computer output, exercises, and many worked-out illustrations. Includes a CD-ROM, Minitab, for use with those exercises intended to be solved with the aid of a computer. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780534384722
  • Publisher: Cengage Learning
  • Publication date: 4/30/2002
  • Series: Available Titles CengageNOW Series
  • Edition description: Older Edition
  • Edition number: 3
  • Pages: 592
  • Product dimensions: 7.92 (w) x 9.96 (h) x 0.90 (d)

Meet the Author

Robert R. Johnson is Professor of Mathematics Emeritus and a former chair of the Mathematics Department at Monroe Community College. He received his B.S. from SUNY Cortland and his M.A. from University of Northern Iowa, both in mathematics; and has studied statistics at University of Iowa and Rochester Institute of Technology. Bob was the author of ELEMENTARY STATISTICS and JUST THE ESSENTIALS OF STATISTICS until being joined by co-author Patricia Kuby. They also co-author STAT. Professor Johnson has given several presentations about the "teaching of statistics" and the use of MINITAB in teaching statistics at various conferences and workshops. He used computers and MINITAB for over 30 years to aid in teaching statistics. He was also an active advocate for writing across the curriculum. Organizing the Beyond the Formula Statistics Conferences for teachers of Introductory Statistics was a passion from 1997 through 2005.

Patricia J. Kuby is Professor of Mathematics at Monroe Community College in Rochester, New York. Prior to coming to MCC, she taught at the Rochester Institute of Technology and worked as a statistician and programmer at General Motors. Patricia has been a co-author of ELEMENTARY STATISTICS since the eighth edition, JUST THE ESSENTIALS OF ELEMENTARY STATISTICS since the ninth edition and STAT. She has also written the accompanying Instructor's Resource Manuals and Student Solutions Manuals. Patricia is an active advocate for incorporating MINITAB and Interactive Applets into online and on-campus statistics classes and has given presentations on each of these software packages, as well as the integration of a Student Response System (clickers) in a statistics class. While at RIT, Patricia received the Excellence in Adjunct Teaching Award. She also received the Monroe Community College 2004/2005 Writing Across the Curriculum Outstanding Faculty Award for the integration of writing components into her statistics courses and the 2007/2008 SUNY Chancellor's Award for Excellence in Teaching. An MCC graduate, Patricia received a B.S. in Mathematics and an M.S. in Quality and Applied Statistics from Rochester Institute of Technology.

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Table of Contents

Part 1: DESCRIPTIVE STATISTICS. 1. Statistics. Chapter Case Study: Americans, Heres Looking at You. What Is Statistics? Introduction to Basic Terms. Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. Return to Chapter Case Study. 2. Descriptive Analysis and Presentation of Single-Variable Data. Chapter Case Study: What Do People Do When They Are on the Internet? Graphic Presentation of Data. Graphs, Pareto Diagrams, and Stem-and-Leaf Displays. Frequency Distributions and Histograms. Numerical Descriptive Statistics. Measures of Central Tendency. Measures of Dispersion. Mean and Standard Deviation of Frequency Distribution. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Return to Chapter Case Study. 3. Descriptive Analysis and Presentation of Bivariate Data. Chapter Case Study: Duncan Wins First MVP. Bivariate Data. Linear Correlation. Linear Regression. Return to Chapter Case Study. Part 2: PROBABILITY. 4. Probability. Chapter Case Study: Statistics Students Favorite Candy. Concepts of Probability. The Nature of Probability. Probability of Events. Simple Sample Spaces. Rules of Probability. Calculating Probabilities of Compound Events. Mutually Exclusive Events and the Addition Rule. Independence, the Multiplication Rule, and Conditional Probability. Combining the Rules of Probability. Return to Chapter Case Study. 5. Probability Distributions (Discrete Variables). Chapter Case Study: Family Values and Family Togetherness. Random Variables. Probability Distributions of a Discrete Random Variable. Mean and Variance of a Discrete Probability Distribution. The Binomial Probability Distribution. Mean and Standard Deviation of the Binomial Distribution. Return to Chapter Case Study. 6. Normal Probability Distributions. Chapter Case Study: Aptitude Tests and Their Interpretation. Normal Probability Distributions. The Standard Normal Distributions. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. Return to Chapter Case Study. 7. Sample Variability. Chapter Case Study: The U.S. Census and Sampling It. Sampling Distributions. The Sampling Distribuation of Sample Means. Application of the Sampling Distribuation of Sample Means. Return to Chapter Case Study. Part 3: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Chapter Case Study: Were They Shorter Back Then? The Nature of Estimation. Estimation of Mean µ (o Known). The Nature of Hypothesis Testing. Hypothesis Test of Mean µ (σ Known): A Probability-Value Approach. Hypothesis test of Mean µ (σ Known): A Classical Approach. Return to Chapter Case Study. 9. Inferences Involving One Population. Chapter Case Study Get Enough Daily Exercise? Inferences About Mean µ (σ Known). Inferences About the Binomial Probability of Success. Inferences About the Variance and Standard Deviation. Return to Chapter Case Study. 10. Inferences Involving Two Populations. Chapter Case Study: Students, Credit Cards, and Debt Dependent and Independent Samples. Inferences Concerning the Mean Difference Using Two Dependent Samples. Inferences Concerning the Difference Between Means Using Two Independent Samples. Inferences Concerning the Difference Between Proportions Using Two Independent Samples. Inferences Concerning the Ratio of Variances Using Two Independent Samples. Return to Chapter Case Study. Part 4: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chapter Case Study: Cooling Your Mouth After a Great Hot Taste. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. Return to Chapter Case Study. 12. Analysis of Variance. Chapter Case Study: Time Spent Reading the Newspaper. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. Return to Chapter Case Study. 13. Linear Correlation and Regression Analysis. Chapter Case Study: Wheat! Beautiful Golden Wheat! Linear Correlation Analysis. Inferences About the Linear Correlation Coefficient. Linear Regression Analysis. Inferences Concerning the Slope of the Regression Line. Confidence Interval Estimates for Regression. Understanding the Relationship Between Correlation and Regression. Return to Chapter Case Study. 14. Elements of Nonparametric Statistics. Chapter Case Study: Teenagers Attitudes. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Return to Chapter Case Study. Appendices. Appendix A: Basic Principles of Counting. Appendix B: Tables. Answers to Selected Exercises. Answers to Chapter Practice Tests. Computer and Calculator Instructions. Index. Credits. Formula Card.

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