STAT (with Review Cards and Bind-in Printed Access Card)

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Overview

Created through a "reader-tested, expert-approved" review process, STAT is an extremely concise, visually appealing new book that introduces essential statistical concepts without any delays or distractions. This brief, affordable paperback includes a full suite of learning aids to accommodate your busy lifestyle, including chapter-by-chapter study cards, self-quizzes to help you review the most important concepts, downloadable flash cards, and interactive video, features that let you learn wherever you are, whenever you have time. From its abbreviated, no-nonsense title to its useful content and engaging style, STAT is the perfect general introductory statistics book for modern learners.

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Product Details

  • ISBN-13: 9780495560517
  • Publisher: Cengage Learning
  • Publication date: 2/6/2009
  • Edition description: Student
  • Pages: 358
  • Product dimensions: 8.40 (w) x 10.80 (h) x 0.80 (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. He is the co-author of JUST THE ESSENTIALS OF ELEMENTARY STATISTICS, Ninth Edition. 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 has been using computers and MINITAB for over 30 years to aid in teaching statistics. He is also an active advocate for writing across the curriculum. Organizing the Beyond the Formula Statistics Conferences for teachers of Introductory Statistics has been a passion since 1997.

Patricia J. Kuby is Associate 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. While at RIT, Patricia received the Excellence in Adjunct Teaching Award. Patricia is co-author of JUST THE ESSENTIALS OF ELEMENTARY STATISTICS, Ninth Edition. She has also written the accompanying Instructor's Resource Manual and Statistical Tutor. Patricia is an active advocate for incorporating MINITAB® and CyberStats into online and on-campus statistics classes and has given presentations on each of these software packages. 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. 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

1. Statistics. What is Statistics? Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. 2. Descriptive Analysis and Presentation of Single-Variable Data. Graphical 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. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Mean and Standard Deviation of Frequency Distribution (Optional). 3. Descriptive Analysis and Presentation of Bivariate Data. Bivariate Data. Linear Correlation. Linear Regression. Part II: PROBABILITY. 4. Probability. Probability of Events. Conditional Probability of Events. Rules of Probability. Mutually Exclusive Events. Independent Events. Mutually Exclusive, Independent Events—A Relationship? 5. Probability Distributions (Discrete Variables). Random Variables. Probability Distribution 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. 6. Normal Probability Distributions. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. 7. Sample Variability. Sampling Distributions. The Sampling Distribution of Sample Means. Application of the Sampling Distribution of Sample Means. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. The Nature of Estimation. Estimation of a Mean µ (s known). The Nature of Hypothesis Testing. Hypothesis Test of Mean µ (s Known): A Probability Value Approach. Hypothesis Test of Mean µ (s Known): A Classical Approach. 9. Inferences Involving One Population. Inferences About Mean µ (s Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. 10. Inferences Involving Two Populations. 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 Variances Using Two Independent Samples. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. 12. Analysis of Variance. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. 13. Linear Correlation and Regression. 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. 14. Elements of Nonparametric Statistics. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation.

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