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Other titles in the Demystified series:
Engineering Statistics Demystified (Demystified)by Larry J. Stephens
Synopses & Reviews
It's safe to infer that YOU'LL MASTER ENGINEERING STATISTICS
Learn how to implement statistics in engineering applications — even if you don't have formal training, unlimited time, or a genius IQ. Engineering Statistics Demystified offers an effective, enlightening, and entertaining way to learn this complex subject.
Written by statistics professor Larry J. Stephens, this book introduces you to several statistical software programs and encourages you to use these programs along with the book. You'll study probability distributions for discrete and continuous random variables as well as sampling distributions, and make statistical inferences concerning means, variances, and proportions. Packed with examples from the featured statistical programs and including end-of-chapter exercises and two final exams, this book will teach you the fundamentals of engineering statistics in no time at all.
This hands-on, self-teaching guide offers:
Simple enough for a beginner, but challenging enough for an advanced student, Engineering Statistics Demystified is your shortcut to mastering this challenging topic.
Clueless? Feel Like a Dummy? Get Demystified!
This versatile reference offers solid coverage of the basics oftraditional engineering statistics and also incorporates examplesfrom the most popular statistical software programs,making it equally valuable to professionals.
About the Author
Larry Stephens is a professor of mathematicsat the University of Nebraska and has also worked for NASA. Heis the author of McGraw-Hill's Advanced Statistics Demystified andSchaum's Outline of Beginning Statistics.
Table of Contents
1. Treatment of data1.1 Pareto Diagrams, Dot Diagrams, Stem-and-Leaf Displays, Histograms1.2 Descriptive measures1.3 Quartiles and other Percentiles1.4 The Calculation of 2. Probability2.1 Sample Spaces and Events2.2 Counting2.3 Probability2.4 The Axioms of Probability2.5 Some Elementary Theorems2.6 Conditional Probability2.7 Bayes’ Theorem2.8 Mathematical Expectation and Decision Making3. Probability Distributions3.1 Random Variables3.2 The Binomial Distribution3.3 The Hypergeometric Distribution3.4 The Mean and Variance of a Probability Distribution3.5 Chebyshev’s Theorem3.6 The Geometric Distribution3.7 The Multinomial Distribution3.8 Simulation4. Probability Densities4.1 Continuous Random Variables4.2 The Normal Distribution4.3 The Normal Approximation to the Binomial Distribution4.4 Other Probability Densities4.5 The Uniform Distribution4.6 The Log-Normal Distribution4.7 The Gamma Distribution4.8 The Beta Distribution4.9 The Weibull Distribution4.10 Joint Distributions – Discrete and Continuous4.11 Checking Data for Normality4.12 Transforming Observations to Near Normality4.13 Simulation5. Sampling Distribution5.1 Populations and Samples5.2 The sampling Distribution of the Mean (s Known)5.3 The sampling Distribution of the Mean (s Unknown)5.4 The sampling Distribution of the Proportion5.5 The sampling Distribution of the Variance6. Inferences Concerning Means6.1 Point Estimation6.2 Interval Estimation6.3 Tests of Hypotheses6.4 Null Hypotheses and Tests of Hypotheses6.5 Hypotheses Concerning One Mean6.6 The Relation Between Tests and Confidence Intervals6.7 Operating Characteristic Curves6.8 Inferences Concerning Two Means6.9 Randomization and Pairing7. Inferences Concerning Variances7.1 The Estimation of Variances7.2 Hypotheses Concerning One Variance7.3 Hypotheses Concerning Two Variances8. Inferences Concerning Proportions8.1 Estimation of Proportions8.2 Hypotheses Concerning One Proportion8.3 Hypotheses Concerning Two Proportions8.4 Hypotheses Concerning Several Proportions8.5 The Analysis of r x c Tables8.6 Goodness of Fit
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