Introduction to Probability and Its Applications, International Edition

1. PROBABILITY IN THE WORLD AROUND US. Why Study Probability? Deterministic and Probabilistic Models. Modeling Reality. Deterministic Models. Probabilistic Models. Applications in Probability. A Brief Historical Note. A Look Ahead.2. FOUNDATIONS OF PROBABILITY.Understanding Randomness: An Intuitive Notion of Probability. Randomness with Known Structure. Randomness with Unknown Structure. Sampling a Finite Universe. Sample Space and Events. Definition of Probability. Counting Rules Useful in Probability. More Counting Rules Useful in Probability. Summary.3. CONDITIONAL PROBABILITY AND INDEPENDENCE. Conditional Probability. Independence. Theorem of Total Probability and Bayes Rule. Odds, Odds Ratios, and Relative Risk. Summary.4. DISCRETE PROBABILITY DISTRIBUTIONS. Random Variables and Their Probability Distributions. Expected Values of Random Variables. The Bernoulli Distribution. The Binomial Distribution. Probability Function. Mean and Variance. History and Applications. The Geometric Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. The Negative Binomial Distribution. Probability Function. Mean and Variance. An Alternate Parameterization: Number of Trials Versus Number of Failures. History and Applications. The Poisson Distribution. Probability Function. Mean and Variance. History and Applications. The Hypergeometric Distribution. The Probability Function. Mean and Variance. History and Applications. The Moment-generating Function. The Probability-generating Function. Markov Chains. Summary.5. CONTINUOUS PROBABILITY DISTRIBUTIONS. Continuous Random Variables and Their Probability Distributions. Expected Values of Continuous Random Variables. The Uniform Distribution. Probability Density Function. Mean and Variance. History and Applications. The Exponential Distribution. Probability Density Function. Mean and Variance. Properties. History and Applications. The Gamma Distribution.

Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects. Linda J. Young, Ph.D., Oklahoma State University, is a Professor of Statistics at the University of Florida where she teaches, consults, and conducts research on statistical methods in public health, agricultural, environmental, and ecological settings. Dr. Young previously was a faculty member at Oklahoma State University and the University of Nebraska. Her more than 100 publications have appeared in 49 different journals, and was a recipient of the American Statistical Association's Founders Award. She has been editor of the Journal of Agricultural, Biological and Environmental Statistics and associate editor for Biometrics and Sequential Analysis. She has served as President of the Eastern North American Region of the American Statistical Association, Vice-President of the American Statistical Association, Chair of the Committee of Presidents of Statistical Societies, member of the National Institute of Statistical Science's Board of Directors, fellow of the American Statistical Association, and elected member of the International Statistical Institute. She has also served on numerous panels for the