Fundamentals of Probability and Statistics for EngineersThis textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features:
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Contents
BASIC PROBABILITY CONCEPTS | 7 |
RANDOM VARIABLES AND PROBABILITY | 37 |
EXPECTATIONS AND MOMENTS | 75 |
FUNCTIONS OF RANDOM VARIABLES | 119 |
SOME IMPORTANT DISCRETE DISTRIBUTIONS | 161 |
SOME IMPORTANT CONTINUOUS DISTRIBUTIONS | 191 |
OBSERVED DATA AND GRAPHICAL REPRESENTATION | 247 |
PARAMETER ESTIMATION | 259 |
ChiSquared Distribution with n Degrees of Freedom | 371 |
D2 Distribution with Sample Size n | 372 |
References | 373 |
COMPUTER SOFTWARE | 375 |
ANSWERS TO SELECTED PROBLEMS | 379 |
Chapter 3 | 380 |
Chapter 4 | 381 |
Chapter 5 | 382 |
MODEL VERIFICATION | 315 |
LINEAR MODELS AND LINEAR REGRESSION | 335 |
TABLES | 365 |
Poisson Mass Function | 367 |
Standardized Normal Distribution Function | 369 |
Students t Distribution with n Degrees of Freedom | 370 |
Chapter 6 | 384 |
Chapter 7 | 385 |
Chapter 10 | 386 |
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Common terms and phrases
Answer arrivals assume asymptotic Bernoulli trials binomial distribution Chapter characteristic function chi-squared distribution coefficient components confidence interval Consider continuous random variable defined by Equation degrees of freedom denote Determine the pdf Determine the probability discrete random variable elsewhere Example exponential distribution failure frequency diagram function pdf fx(x fxy(x fy(y gamma distribution given by Equation gives Hence histogram hypothesized distribution independent random variables integral joint probability jpdf jpmf Let X1 linear regression maximum likelihood mean and variance moments normal distribution normal random variable obtained outcomes ox(t P(AB Poisson distribution population probability density function probability distribution function probability mass function Problem properties Px(k Px(x random variables X1 result sample space sample values Section shown in Figure Theorem Type-I unbiased estimator uniformly distributed X₁ X1 and X2 Y₁
References to this book
Six Sigma Performance Measurement System: Prozesscontrolling als ... Serkan Tavasli Limited preview - 2007 |