A concise, affordable, but thorough and effective text, STAT provides exactly what you need to learn general introductory stats via an engaging text and full suite of digital learning aids, including interactive video and audio reviews.
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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.
Part I: DESCRIPTIVE STATISTICS. 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 ? (? known). The Nature of Hypothesis Testing. Hypothesis Test of Mean ? (? Known): A Probability Value Approach. Hypothesis Test of Mean ? (? Known): A Classical Approach. 9. Inferences Involving One Population. Inferences About Mean ? (? 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.