LEADER 05391nam 22006733u 450 001 9910830382903321 005 20230721030243.0 010 $a1-281-09412-9 010 $a9786611094126 010 $a0-470-12844-5 010 $a0-470-12843-7 035 $a(CKB)1000000000377260 035 $a(EBL)319281 035 $a(SSID)ssj0000204320 035 $a(PQKBManifestationID)11199469 035 $a(PQKBTitleCode)TC0000204320 035 $a(PQKBWorkID)10187959 035 $a(PQKB)11053039 035 $a(MiAaPQ)EBC319281 035 $a(OCoLC)181345645 035 $a(EXLCZ)991000000000377260 100 $a20131014d2007|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModern Engineering Statistics$b[electronic resource] 210 $aHoboken $cWiley$d2007 215 $a1 online resource (608 p.) 300 $aDescription based upon print version of record. 311 $a0-470-08187-2 327 $aModern Engineering Statistics; Contents; Preface; 1. Methods of Collecting and Presenting Data; 1.1 Observational Data and Data from Designed Experiments; 1.2 Populations and Samples; 1.3 Variables; 1.4 Methods of Displaying Small Data Sets; 1.4.1 Stem-and-Leaf Display; 1.4.2 Time Sequence Plot and Control Chart; 1.4.3 Lag Plot; 1.4.4 Scatter Plot; 1.4.5 Digidot Plot; 1.4.6 Dotplot; 1.5 Methods of Displaying Large Data Sets; 1.5.1 Histogram; 1.5.2 Boxplot; 1.6 Outliers; 1.7 Other Methods; 1.8 Extremely Large Data Sets: Data Mining; 1.9 Graphical Methods: Recommendations; 1.10 Summary 327 $aReferencesExercises; 2. Measures of Location and Dispersion; 2.1 Estimating Location Parameters; 2.2 Estimating Dispersion Parameters; 2.3 Estimating Parameters from Grouped Data; 2.4 Estimates from a Boxplot; 2.5 Computing Sample Statistics with MINITAB; 2.6 Summary; Reference; Exercises; 3. Probability and Common Probability Distributions; 3.1 Probability: From the Ethereal to the Concrete; 3.1.1 Manufacturing Applications; 3.2 Probability Concepts and Rules; 3.2.1 Extension to Multiple Events; 3.2.1.1 Law of Total Probability and Bayes' Theorem; 3.3 Common Discrete Distributions 327 $a3.3.1 Expected Value and Variance3.3.2 Binomial Distribution; 3.3.2.1 Testing for the Appropriateness of the Binomial Model; 3.3.3 Hypergeometric Distribution; 3.3.4 Poisson Distribution; 3.3.4.1 Testing for the Appropriateness of the Poisson Model; 3.3.5 Geometric Distribution; 3.4 Common Continuous Distributions; 3.4.1 Expected Value and Variance; 3.4.2 Determining Probabilities for Continuous Random Variables; 3.4.3 Normal Distribution; 3.4.3.1 Software-Aided Normal Probability Computations; 3.4.3.2 Testing the Normality Assumption; 3.4.4 t-Distribution; 3.4.5 Gamma Distribution 327 $a3.4.5.1 Chi-Square Distribution3.4.5.2 Exponential Distribution; 3.4.6 Weibull Distribution; 3.4.7 Smallest Extreme Value Distribution; 3.4.8 Lognormal Distribution; 3.4.9 F Distribution; 3.5 General Distribution Fitting; 3.6 How to Select a Distribution; 3.7 Summary; References; Exercises; 4. Point Estimation; 4.1 Point Estimators and Point Estimates; 4.2 Desirable Properties of Point Estimators; 4.2.1 Unbiasedness and Consistency; 4.2.2 Minimum Variance; 4.2.3 Estimators Whose Properties Depend on the Assumed Distribution; 4.2.4 Comparing Biased and Unbiased Estimators 327 $a4.3 Distributions of Sampling Statistics4.3.1 Central Limit Theorem; 4.3.1.1 Illustration of Central Limit Theorem; 4.3.2 Statistics with Nonnormal Sampling Distributions; 4.4 Methods of Obtaining Estimators; 4.4.1 Method of Maximum Likelihood; 4.4.2 Method of Moments; 4.4.3 Method of Least Squares; 4.5 Estimating ?; 4.6 Estimating Parameters Without Data; 4.7 Summary; References; Exercises; 5. Confidence Intervals and Hypothesis Tests-One Sample; 5.1 Confidence Interval for ?: Normal Distribution, ? Not Estimated from Sample Data; 5.1.1 Sample Size Determination; 5.1.2 Interpretation and Use 327 $a5.1.3 General Form of Confidence Intervals 330 $aAn introductory perspective on statistical applications in the field of engineering Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a 606 $aEngineering - Statistical methods 606 $aEngineering 606 $aEngineering$xStatistical methods 606 $aEngineering & Applied Sciences$2HILCC 606 $aApplied Mathematics$2HILCC 615 4$aEngineering - Statistical methods. 615 4$aEngineering. 615 0$aEngineering$xStatistical methods 615 7$aEngineering & Applied Sciences 615 7$aApplied Mathematics 676 $a519.5 676 $a620.0072 700 $aRyan$b Thomas P$0117276 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910830382903321 996 $aModern engineering statistics$91071327 997 $aUNINA