Synopses & Reviews
In their own classrooms, through their popular texts, and in the conferences they lead, Bob Johnson and Pat Kuby have inspired hundreds of thousands of students to see statistics and all its usefulness. This new ADVANTAGE SERIES version of Robert Johnson and Patricia Kuby's ELEMENTARY STATISTICS, 9th Edition represents the 30th anniversary of their flagship title. This comprehensive text translates the language of statistics into approachable everyday terminology through its clear exposition, real-world examples, and interesting, applicable case studies. The authors promote the learning of statistics in a context that relates to personal experience. The flexibility of technology coverage (MINITAB, Excel, and TI-83 output and instructions throughout), the wealth of instructor supplements, and the expanded opportunities for online enrichment make this the easiest text for students to learn from and for teachers to teach from. As part of the ADVANTAGE SERIES, this new version will offer all the quality content you've come to expect from Johnson and Kuby sold to your students at a significantly lower price.
Synopsis
ELEMENTARY STATISTICS, Eighth Edition offers comprehensive coverage of elementary statistics in a language that speaks to today's students. This text goes beyond formulas, offering 'real-world' data and examples that present statistics as a useful tool in real-world applications, e.g. physical and social sciences, business, and engineering. The authors promote the learning of statistics in a context that relates to personal experience. ELEMENTARY STATISTICS features modern ideas by integrating the use of computer output and instruction for every exercise.
Synopsis
ELEMENTARY STATISTICS, Eighth Edition offers comprehensive coverage of elementary statistics in a language that speaks to today's students. This text goes beyond formulas, offering 'real-world' data and examples that present statistics as a useful tool in real-world applications, e.g. physical and social sciences, business, and engineering. The authors promote the learning of statistics in a context that relates to personal experience. ELEMENTARY STATISTICS features modern ideas by integrating the use of computer output and instruction for every exercise.
Description
System requirements for accompanying computer disc: MacIntosh: Power MacIntosh or later, OS 7.5 or higher (must have thread library installed); 4 MB RAM minimum, 8 MB recommended; CD-ROM 4x minimum. Windows: 386/25 or faster processor, Windows 98, 95, 3.1, NT 3.5.1, and higher; 4 MB RAM minimum, 8 MB recommended; CD-ROM 4x minimum.
About 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.
Table of Contents
Part One: DESCRIPTIVE STATISTICS. 1. Statistics. Chapter Case Study. What Is Statistics? Introduction to Basic Terms. Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. Chapter Summary. 2. Descriptive Analysis and Presentation of Single-Variable Data. Chapter Case Study. Graphs, Pareto Diagrams, and Stem-and-Leaf Displays. Frequency Distributions and Histograms. 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. Chapter Summary. 3. Descriptive Analysis and Presentation of Bivariate Data. Chapter Case Study. Bivariate Data. Linear Correlation. Linear Regression. Chapter Summary. Part Two: PROBABILITY. 4. Probability. Chapter Case Study. The Nature of Probability. Probability of Events. Simple Sample Spaces. Rules of Probability. Mutually Exclusive Events and the Addition Rule. Independence, the Multiplication Rule, and Conditional Probability. Combining the Rules of Probability. Chapter Summary. 5. Probability Distributions (Discrete Variables). Chapter Case Study. 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. Chapter Summary. 6. Normal Probability Distributions. Chapter Case Study. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. Chapter Summary. 7. Sample Variability. Chapter Case Study. Sampling Distributions. The Central Limit Theorem. Application of the Central Limit Theorem. Chapter Summary. Part Three: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Chapter Case Study. The Nature of Estimation. Estimation of Mean ? (o Known). The Nature of Hypothesis Testing. Hypothesis Test of Mean ? (o Known): A Probability-Value Approach. Hypothesis test of Mean ? (o Known): A Classical Approach. Chapter Summary. 9. Inferences Involving One Population. Chapter Case Study. Inferences About Mean ? (o Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. Chapter Summary. 10. Inferences Involving Two Populations. Chapter Case Study. Independent and Dependent 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 Variance Using Two Independent Samples. Chapter Summary. Part Four: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chapter Case Study. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. Chapter Summary. 12. Analysis of Variance. Chapter Case Study. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. Chapter Summary. 13. Linear Correlation and Regression Analysis. Chapter Case Study. 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. Chapter Summary. 14. Elements of Nonparametric Statistics. Chapter Case Study. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Chapter Summary. References. Appendix A: Data Sets. Appendix B: Tables. Binomial Probabilities. Probabilities for the Standard Normal Distribution. Critical Values of Students t Distribution. Critical values of the Chi-Square Distribution. Critical Values of the F-Distribution. Answers to Odd-Numbered Exercises. Index.