Introduction to Probability and Statistics

J.Susan Milton is professor Emeritus of Satatics at Radford University. Dr. Milton recieved the B.S. degree from Western Carolina University, the M.A. degree from the University of North Carolina at Chapel Hill, and the Ph.D degree in Statistics from Virginia Polytechnic Institute and State university. She is a Danforth Associate and is a recipient of the Radford University Foundation Award for Excellence in Teaching. Dr. Milton is the author of Statistical Methods in the Biological and Health Sciences as well as Introduction to statistics, Probability with the Essential Analysis, and a first Course in the Theory of Linear Statistical Models. Jesse C. arnold is a Professor of Statistics at Virginia Polytechnic Insitute and state University. Dr. arnold received the B.S. Degree from Southeastern state University, and the M.A and Ph. D degrees in statistics from Florida state university. He served as head of Statistics department for ten years, is a fellow of the American Statistical Association, and elected member of the International Statistics Institute. Ha has served as President of the International Biometric Society (Eastern North American Region) and Chairman of the statistical Educational Section of the American Statistical Association.

Chapter 1 - Introduction to Probability and Counting<p/>1.1 Interpreting Probabilities<p/>1.2 Sample Spaces and Events<p/>1.3 Permutations and Combinations<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 2 - Some Probability Laws<p/>2.1 Axioms of Probability<p/>2.2 Conditional Probability<p/>2.3 Independence and the Multiplication Rule<p/>2.4 Bayes' Theorem<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 3 - Discrete Distributions<p/>3.1 Random Variables<p/>3.2 Discrete Probablility Densities<p/>3.3 Expectation and Distribution Parameters<p/>3.4 Geometric Distribution and the Moment Generating Function<p/>3.5 Binomial Distribution<p/>3.6 Negative Binomial Distribution<p/>3.7 Hypergeometric Distribution<p/>3.8 Poisson Distribution<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 4 - Continuous Distributions<p/>4.1 Continuous Densities<p/>4.2 Expectation and Distribution Parameters<p/>4.3 Gamma, Exponential, and Chi-Squared Distributions<p/>4.4 Normal Distribution<p/>4.5 Normal Probability Rule and Chebyshev's Inequality<p/>4.6 Normal Approximation to the Binomial Distribution<p/>4.7 Weibull Distribution and Reliability<p/>4.8 Transformation of Variables<p/>4.9 Simulating a Continuous Distribution<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 5 - Joint Distributions<p/>5.1 Joint Densities and Independence<p/>5.2 Expectation and Covariance<p/>5.3 Correlation<p/>5.4 Conditional Densities and Regression<p/>5.5 Transformation of Variables<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 6 - Descriptive Statistics<p/>6.1 Random Sampling<p/>6.2 Picturing the Distribution<p/>6.3 Sample Statistics<p/>6.4 Boxplots<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 7 - Estimation<p/>7.1 Point Estimation<p/>7.2 The Method of Moments and Maximum Likelihood<p/>7.3 Functions of Random Variables--Distribution of X<p/>7.4 Interval Estimation and the Central Limit Theorem<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 8 - Inferences on the Mean and Variance of a Distribution<p/>8.1 Interval Estimation of Variability<p/>8.2 Estimating the Mean and the Student-t Distribution<p/>8.3 Hypothesis Testing<p/>8.4 Significance Testing<p/>8.5 Hypothesis and Significance Tests on the Mean<p/>8.6 Hypothesis Test on the Variance<p/>8.7 Alternative Nonparametric Methods<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 9 - Inferences on Proportions<p/>9.1 Estimating Proportions<p/>9.2 Testing Hypothesis on a Proportion<p/>9.3 Comparing Two Proportions Estimation<p/>9.4 Coparing Two Proportions: Hypothesis Testing<p/>Chapter Summary<p/>Exercises<p/>Review Exercises<p/>Chapter 10 - Comparing Two Means and Two Variances<p/>10.1 Point Estimation: Independent Samples<p/>10.2 Comparing Variances: The F Distribution<p/>10.3 Comparing Means: Variances Equal (Pooled Test)<p/>10.4 Comparing Means: Variances Unequal<p/>10.5 Compairing Means: Paried Data<p/>10.6 Alternative No