Probability and Statistics for Engineering and the Sciences with Modeling using R

William P. Fox, Rodney X. Sturdivant

• Publisher: Chapman and Hall/CRC
• Year: 2022
• ISBN:
  9781000825589
• Edition: 1
• Topic: Mathematik
• Subtopic: Wahrscheinlichkeitsrechnung & Statistiken

Probability and statistics courses are more popular than ever. Regardless of your major or your profession, you will most likely use concepts from probability and statistics often in your career.

The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course.

The book focuses on engineering examples and applications, while also including social sciences and more examples. Depending on the chapter flows, a course can be tailored for students at all levels and background.

Over many years of teaching this course, the authors created problems based on real data, student projects, and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both.

R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work, in class, or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Dedication
• Contents
• Preface
• Acknowledgments
• 1 Introduction to Statistical Modeling and Models and R
• 2 Introduction to Data
• 3 Statistical Measures
• 4 Classical Probability
• 5 Discrete Distributions
• 6 Continuous Probability Models
• 7 Other Continuous Distribution (Some Calculus Required): Triangular, Unnamed, Beta, Gamma
• 8 Sampling Distributions
• 9 Estimating Parameters
• 10 One Sample Hypothesis Testing
• 11 Inferences Based on Two Samples
• 12 Reliability Modeling (Modified and Adapted from Military Reliability Modeling by Fox and Horton)
• 13 Introduction to Regression Techniques
• 14 Advanced Regression Models: Nonlinear, Sinusoidal, and Binary Logistics Regression Using R
• 15 ANOVA in R
• 16 Two-Way ANCOVA Using R
• Appendix A Labs/Projects
• Appendix B Answers to Selected Exercises
• Index