Introduction To Probability And Statistics

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Key Features New and retained examples and exercises are chosen specifically for students in engineering and computer science. Students can work on problems within a context that is familiar and interesting to them. Do you have lots of Engineering and Computer Science majors? Do you like to use specific examples that relate to their field of study? Students gain understanding of the logic behind the techniques as well as practice using them due to the authors' approach. Because of this, students are better able to retain the information and are better prepared to apply the techniques in this and future courses. Are you looking for a text that helps to reinforce the logic along with the techniques? Data sets for examples and problems are provided in electronic format for use with the most popular statistical packages. Students do not have to waste valuable time entering data and data-entry errors are eliminated. Do you like to use Data sets? Do you find that problems arise when students have to manually enter the data sets? References to statistical analysis packages have been added. This enables students to see how the techniques are applied by using these popular packages. Would you like your students to see references to popular statistical analysis packages? Student Solutions Manual and an Instructor's Manual are available. Some of the more difficult derivations have been moved to an appendix. Gives the book a more applied feel while retaining the conceptual material for those who wish to use it. Discussion of Tukey's method of paired comparisons and a section on the use of tolerance limits in quality control About The Author 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 Table Of Contents Chapter 1 Introduction to Probability and Counting Chapter 2 Some ProbabilityLaws Chapter 3 Discrete Distributions Chapter 4 Continuous Distributions Chapter 5 Joint Distributions Chapter 6 Descriptive Statistics Chapter 7 Estimation Chapter 8 Inferences on the Mean and Variance of a Distribution Chapter 9 Inferences on Proportions Chapter 10 Comparing Two Means and Two Variances Chapter 11 Sample Linear Regression and Correlation Chapter 12 Multiple Linear Regression Models Chapter 13 Analysis of Variance Chapter 14 Factorial Experiments Chapter 15 Categorical Data Chapter 16 Statistical Quality Control Appendixes Appendix A Statistical Tables Appendix B Answers to Selected Problems Appendix C Selected Derivations