{"product_id":"applied-statistics-9781119551522","title":"Applied Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eInstructs readers on how to use methods of statistics and experimental design with R software\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eApplied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one''s ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Statistics: Theory and Problem Solutions with R\u003c\/i\u003e includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott''s \u003ci\u003eMathematical Statistics\u003c\/i\u003e via that book''s theoretical backgroundtaking the lessons learned from there to ano\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The \u003c\/b\u003e\u003cb\u003eR-Package, Sampling Procedures, and Random Variables 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Statistical Software Package R 1\u003c\/p\u003e \u003cp\u003e1.3 Sampling Procedures and Random Variables 4\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Point Estimation \u003c\/b\u003e\u003cb\u003e11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 11\u003c\/p\u003e \u003cp\u003e2.2 Estimating Location Parameters 12\u003c\/p\u003e \u003cp\u003e2.2.1 Maximum Likelihood Estimation of Location Parameters 17\u003c\/p\u003e \u003cp\u003e2.2.2 Estimating Expectations from Censored Samples and Truncated Distributions 20\u003c\/p\u003e \u003cp\u003e2.2.3 Estimating Location Parameters of Finite Populations 23\u003c\/p\u003e \u003cp\u003e2.3 Estimating Scale Parameters 24\u003c\/p\u003e \u003cp\u003e2.4 Estimating Higher Moments 27\u003c\/p\u003e \u003cp\u003e2.5 Contingency Tables 29\u003c\/p\u003e \u003cp\u003e2.5.1 Models of Two-Dimensional Contingency Tables 29\u003c\/p\u003e \u003cp\u003e2.5.1.1 Model I 29\u003c\/p\u003e \u003cp\u003e2.5.1.2 Model II 29\u003c\/p\u003e \u003cp\u003e2.5.1.3 Model III 30\u003c\/p\u003e \u003cp\u003e2.5.2 Association Coefficients for 2 ×2 Tables 30\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Testing Hypotheses – One- and Two-Sample Problems \u003c\/b\u003e\u003cb\u003e39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 The One-Sample Problem 41\u003c\/p\u003e \u003cp\u003e3.2.1 Tests on an Expectation 41\u003c\/p\u003e \u003cp\u003e3.2.1.1 Testing the Hypothesis on the Expectation of a Normal Distribution with Known Variance 41\u003c\/p\u003e \u003cp\u003e3.2.1.2 Testing the Hypothesis on the Expectation of a Normal Distribution with Unknown Variance 47\u003c\/p\u003e \u003cp\u003e3.2.2 Test on the Median 51\u003c\/p\u003e \u003cp\u003e3.2.3 Test on the Variance of a Normal Distribution 54\u003c\/p\u003e \u003cp\u003e3.2.4 Test on a Probability 56\u003c\/p\u003e \u003cp\u003e3.2.5 Paired Comparisons 57\u003c\/p\u003e \u003cp\u003e3.2.6 Sequential Tests 59\u003c\/p\u003e \u003cp\u003e3.3 The Two-Sample Problem 63\u003c\/p\u003e \u003cp\u003e3.3.1 Tests on Two Expectations 63\u003c\/p\u003e \u003cp\u003e3.3.1.1 The Two-Sample \u003ci\u003et\u003c\/i\u003e-Test 63\u003c\/p\u003e \u003cp\u003e3.3.1.2 The Welch Test 66\u003c\/p\u003e \u003cp\u003e3.3.1.3 The Wilcoxon Rank Sum Test 70\u003c\/p\u003e \u003cp\u003e3.3.1.4 Definition of Robustness and Results of Comparing Tests by Simulation 72\u003c\/p\u003e \u003cp\u003e3.3.1.5 Sequential Two-Sample Tests 74\u003c\/p\u003e \u003cp\u003e3.3.2 Test on Two Medians 76\u003c\/p\u003e \u003cp\u003e3.3.2.1 Rationale 77\u003c\/p\u003e \u003cp\u003e3.3.3 Test on Two Probabilities 78\u003c\/p\u003e \u003cp\u003e3.3.4 Tests on Two Variances 79\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Confidence Estimations – One- and Two-Sample Problems \u003c\/b\u003e\u003cb\u003e83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 83\u003c\/p\u003e \u003cp\u003e4.2 The One-Sample Case 84\u003c\/p\u003e \u003cp\u003e4.2.1 A Confidence Interval for the Expectation of a Normal Distribution 84\u003c\/p\u003e \u003cp\u003e4.2.2 A Confidence Interval for the Variance of a Normal Distribution 91\u003c\/p\u003e \u003cp\u003e4.2.3 A Confidence Interval for a Probability 93\u003c\/p\u003e \u003cp\u003e4.3 The Two-Sample Case 96\u003c\/p\u003e \u003cp\u003e4.3.1 A Confidence Interval for the Difference of Two Expectations – Equal Variances 96\u003c\/p\u003e \u003cp\u003e4.3.2 A Confidence Interval for the Difference of Two Expectations – Unequal Variances 98\u003c\/p\u003e \u003cp\u003e4.3.3 A Confidence Interval for the Difference of Two Probabilities 100\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Analysis of Variance (ANOVA) – Fixed Effects Models \u003c\/b\u003e\u003cb\u003e105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 105\u003c\/p\u003e \u003cp\u003e5.1.1 Remarks about Program Packages 106\u003c\/p\u003e \u003cp\u003e5.2 Planning the Size of an Experiment 106\u003c\/p\u003e \u003cp\u003e5.3 One-Way Analysis of Variance 108\u003c\/p\u003e \u003cp\u003e5.3.1 Analysing Observations 109\u003c\/p\u003e \u003cp\u003e5.3.2 Determination of the Size of an Experiment 112\u003c\/p\u003e \u003cp\u003e5.4 Two-Way Analysis of Variance 115\u003c\/p\u003e \u003cp\u003e5.4.1 Cross-Classification (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e) 115\u003c\/p\u003e \u003cp\u003e5.4.1.1 Parameter Estimation 117\u003c\/p\u003e \u003cp\u003e5.4.1.2 Testing Hypotheses 119\u003c\/p\u003e \u003cp\u003e5.4.2 Nested Classification (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) 131\u003c\/p\u003e \u003cp\u003e5.5 Three-Way Classification 134\u003c\/p\u003e \u003cp\u003e5.5.1 Complete Cross-Classification (\u003ci\u003eA\u003c\/i\u003e×\u003ci\u003eB \u003c\/i\u003e×\u003ci\u003eC\u003c\/i\u003e) 135\u003c\/p\u003e \u003cp\u003e5.5.2 Nested Classification (\u003ci\u003eC \u003c\/i\u003e\u003ci\u003e≺B\u003c\/i\u003e\u003ci\u003e≺A\u003c\/i\u003e) 144\u003c\/p\u003e \u003cp\u003e5.5.3 Mixed Classifications 147\u003c\/p\u003e \u003cp\u003e5.5.3.1 Cross-Classification between Two Factors where One of Them Is Sub-Ordinated to a Third Factor ((\u003ci\u003eB\u003c\/i\u003e\u003ci\u003e≺A\u003c\/i\u003e)x\u003ci\u003eC\u003c\/i\u003e) 148\u003c\/p\u003e \u003cp\u003e5.5.3.2 Cross-Classification of Two Factors, in which a Third Factor is Nested (\u003ci\u003eC\u003c\/i\u003e\u003ci\u003e≺\u003c\/i\u003e(\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)) 153\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Analysis of Variance –Models with Random Effects \u003c\/b\u003e\u003cb\u003e159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 159\u003c\/p\u003e \u003cp\u003e6.2 One-Way Classification 159\u003c\/p\u003e \u003cp\u003e6.2.1 Estimation of the Variance Components 160\u003c\/p\u003e \u003cp\u003e6.2.1.1 ANOVA Method 160\u003c\/p\u003e \u003cp\u003e6.2.1.2 Maximum Likelihood Method 164\u003c\/p\u003e \u003cp\u003e6.2.1.3 \u003ci\u003eREML \u003c\/i\u003e– Estimation 166\u003c\/p\u003e \u003cp\u003e6.2.2 Tests of Hypotheses and Confidence Intervals 169\u003c\/p\u003e \u003cp\u003e6.2.3 Expectation and Variances of the ANOVA Estimators 174\u003c\/p\u003e \u003cp\u003e6.3 Two-Way Classification 176\u003c\/p\u003e \u003cp\u003e6.3.1 Two-Way Cross Classification 176\u003c\/p\u003e \u003cp\u003e6.3.2 Two-Way Nested Classification 182\u003c\/p\u003e \u003cp\u003e6.4 Three-Way Classification 186\u003c\/p\u003e \u003cp\u003e6.4.1 Three-Way Cross-Classification with Equal Sub-Class Numbers 186\u003c\/p\u003e \u003cp\u003e6.4.2 Three-Way Nested Classification 192\u003c\/p\u003e \u003cp\u003e6.4.3 Three-Way Mixed Classifications 195\u003c\/p\u003e \u003cp\u003e6.4.3.1 Cross-Classification Between Two Factors Where One of Them is Sub-Ordinated to a Third Factor ((\u003ci\u003eB\u003c\/i\u003e\u003ci\u003e≺A\u003c\/i\u003e)×\u003ci\u003eC\u003c\/i\u003e) 195\u003c\/p\u003e \u003cp\u003e6.4.3.2 Cross-Classification of Two Factors in Which a Third Factor is Nested (\u003ci\u003eC\u003c\/i\u003e\u003ci\u003e≺\u003c\/i\u003e(\u003ci\u003eA\u003c\/i\u003e×\u003ci\u003eB\u003c\/i\u003e)) 197\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Analysis of Variance –Mixed Models \u003c\/b\u003e\u003cb\u003e201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 201\u003c\/p\u003e \u003cp\u003e7.2 Two-Way Classification 201\u003c\/p\u003e \u003cp\u003e7.2.1 Balanced Two-Way Cross-Classification 201\u003c\/p\u003e \u003cp\u003e7.2.2 Two-Way Nested Classification 214\u003c\/p\u003e \u003cp\u003e7.3 Three-Way Layout 223\u003c\/p\u003e \u003cp\u003e7.3.1 Three-Way Analysis of Variance – Cross-Classification \u003ci\u003eA \u003c\/i\u003e× \u003ci\u003eB \u003c\/i\u003e× \u003ci\u003eC \u003c\/i\u003e223\u003c\/p\u003e \u003cp\u003e7.3.2 Three-Way Analysis of Variance – Nested Classification \u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e\u003ci\u003e≻C \u003c\/i\u003e230\u003c\/p\u003e \u003cp\u003e7.3.2.1 Three-Way Analysis of Variance – Nested Classification – Model III – Balanced Case 230\u003c\/p\u003e \u003cp\u003e7.3.2.2 Three-Way Analysis of Variance – Nested Classification – Model IV – Balanced Case 232\u003c\/p\u003e \u003cp\u003e7.3.2.3 Three-Way Analysis of Variance – Nested Classification – Model V – Balanced Case 234\u003c\/p\u003e \u003cp\u003e7.3.2.4 Three-Way Analysis of Variance – Nested Classification – Model VI – Balanced Case 236\u003c\/p\u003e \u003cp\u003e7.3.2.5 Three-Way Analysis of Variance – Nested Classification – Model VII – Balanced Case 237\u003c\/p\u003e \u003cp\u003e7.3.2.6 Three-Way Analysis of Variance – Nested Classification – Model VIII – Balanced Case 238\u003c\/p\u003e \u003cp\u003e7.3.3 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)\u003ci\u003e≻C \u003c\/i\u003e239\u003c\/p\u003e \u003cp\u003e7.3.3.1 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)\u003ci\u003e≻C \u003c\/i\u003eModel III 239\u003c\/p\u003e \u003cp\u003e7.3.3.2 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)\u003ci\u003e≻C \u003c\/i\u003eModel IV 242\u003c\/p\u003e \u003cp\u003e7.3.3.3 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)\u003ci\u003e≻C \u003c\/i\u003eModel V 243\u003c\/p\u003e \u003cp\u003e7.3.3.4 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e× \u003ci\u003eB\u003c\/i\u003e)\u003ci\u003e≻C \u003c\/i\u003eModel VI 245\u003c\/p\u003e \u003cp\u003e7.3.4 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003e247\u003c\/p\u003e \u003cp\u003e7.3.4.1 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003eModel III 247\u003c\/p\u003e \u003cp\u003e7.3.4.2 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003eModel IV 249\u003c\/p\u003e \u003cp\u003e7.3.4.3 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003eModel V 251\u003c\/p\u003e \u003cp\u003e7.3.4.4 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003eModel VI 253\u003c\/p\u003e \u003cp\u003e7.3.4.5 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003emodel VII 254\u003c\/p\u003e \u003cp\u003e7.3.4.6 Three-Way Analysis of Variance – Mixed Classification – (\u003ci\u003eA\u003c\/i\u003e\u003ci\u003e≻B\u003c\/i\u003e) ×\u003ci\u003eC \u003c\/i\u003eModel VIII 255\u003c\/p\u003e \u003cp\u003eReferences 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Regression Analysis \u003c\/b\u003e\u003cb\u003e257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 257\u003c\/p\u003e \u003cp\u003e8.2 Regression with Non-Random Regressors – Model I of Regression 262\u003c\/p\u003e \u003cp\u003e8.2.1 Linear and Quasilinear Regression 262\u003c\/p\u003e \u003cp\u003e8.2.1.1 Parameter Estimation 263\u003c\/p\u003e \u003cp\u003e8.2.1.2 Confidence Intervals and Hypotheses Testing 274\u003c\/p\u003e \u003cp\u003e8.2.2 Intrinsically Non-Linear Regression 282\u003c\/p\u003e \u003cp\u003e8.2.2.1 The Asymptotic Distribution of the Least Squares Estimators 283\u003c\/p\u003e \u003cp\u003e8.2.2.2 The Michaelis–Menten Regression 285\u003c\/p\u003e \u003cp\u003e8.2.2.3 Exponential Regression 290\u003c\/p\u003e \u003cp\u003e8.2.2.4 The Logistic Regression 298\u003c\/p\u003e \u003cp\u003e8.2.2.5 The Bertalanffy Function 306\u003c\/p\u003e \u003cp\u003e8.2.2.6 The Gompertz Function 312\u003c\/p\u003e \u003cp\u003e8.2.3 Optimal Experimental Designs 315\u003c\/p\u003e \u003cp\u003e8.2.3.1 Simple Linear and Quasilinear Regression 316\u003c\/p\u003e \u003cp\u003e8.2.3.2 Intrinsically Non-linear Regression 317\u003c\/p\u003e \u003cp\u003e8.2.3.3 The Michaelis-Menten Regression 319\u003c\/p\u003e \u003cp\u003e8.2.3.4 Exponential Regression 319\u003c\/p\u003e \u003cp\u003e8.2.3.5 The Logistic Regression 320\u003c\/p\u003e \u003cp\u003e8.2.3.6 The Bertalanffy Function 321\u003c\/p\u003e \u003cp\u003e8.2.3.7 The Gompertz Function 321\u003c\/p\u003e \u003cp\u003e8.3 Models with Random Regressors 322\u003c\/p\u003e \u003cp\u003e8.3.1 The Simple Linear Case 322\u003c\/p\u003e \u003cp\u003e8.3.2 The Multiple Linear Case and the Quasilinear Case 330\u003c\/p\u003e \u003cp\u003e8.3.2.1 Hypotheses Testing - General 333\u003c\/p\u003e \u003cp\u003e8.3.2.2 Confidence Estimation 333\u003c\/p\u003e \u003cp\u003e8.3.3 The Allometric Model 334\u003c\/p\u003e \u003cp\u003e8.3.4 Experimental Designs 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Analysis of Covariance (ANCOVA) \u003c\/b\u003e\u003cb\u003e339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 339\u003c\/p\u003e \u003cp\u003e9.2 Completely Randomised Design with Covariate 340\u003c\/p\u003e \u003cp\u003e9.2.1 Balanced Completely Randomised Design 340\u003c\/p\u003e \u003cp\u003e9.2.2 Unbalanced Completely Randomised Design 350\u003c\/p\u003e \u003cp\u003e9.3 Randomised Complete Block Design with Covariate 358\u003c\/p\u003e \u003cp\u003e9.4 Concluding Remarks 365\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multiple Decision Problems \u003c\/b\u003e\u003cb\u003e367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 367\u003c\/p\u003e \u003cp\u003e10.2 Selection Procedures 367\u003c\/p\u003e \u003cp\u003e10.2.1 The Indifference Zone Formulation for Selecting Expectations 368\u003c\/p\u003e \u003cp\u003e10.2.1.1 Indifference Zone Selection, 𝜎\u003csup\u003e2\u003c\/sup\u003e Known 368\u003c\/p\u003e \u003cp\u003e10.2.1.2 Indifference Zone Selection, 𝜎\u003csup\u003e2\u003c\/sup\u003e Unknown 371\u003c\/p\u003e \u003cp\u003e10.3 The Subset Selection Procedure for Expectations 371\u003c\/p\u003e \u003cp\u003e10.4 Optimal Combination of the Indifference Zone and the Subset Selection Procedure 372\u003c\/p\u003e \u003cp\u003e10.5 Selection of the Normal Distribution with the Smallest Variance 375\u003c\/p\u003e \u003cp\u003e10.6 Multiple Comparisons 375\u003c\/p\u003e \u003cp\u003e10.6.1 The Solution of MC Problem 10.1 377\u003c\/p\u003e \u003cp\u003e10.6.1.1 The \u003ci\u003eF\u003c\/i\u003e-test for MC Problem 10.1 377\u003c\/p\u003e \u003cp\u003e10.6.1.2 Scheffé’s Method for MC Problem 10.1 378\u003c\/p\u003e \u003cp\u003e10.6.1.3 Bonferroni’s Method for MC Problem 10.1 379\u003c\/p\u003e \u003cp\u003e10.6.1.4 Tukey’s Method for MC Problem 10.1 for \u003ci\u003en\u003csub\u003ei\u003c\/sub\u003e \u003c\/i\u003e= \u003ci\u003en \u003c\/i\u003e382\u003c\/p\u003e \u003cp\u003e10.6.1.5 Generalised Tukey’s Method for MC Problem 10.1 for \u003ci\u003en\u003csub\u003ei\u003c\/sub\u003e \u003c\/i\u003e≠\u003ci\u003en \u003c\/i\u003e383\u003c\/p\u003e \u003cp\u003e10.6.2 The Solution of MC Problem 10.2 – the Multiple t-Test 384\u003c\/p\u003e \u003cp\u003e10.6.3 The Solution of MC Problem 10.3 – Pairwise and Simultaneous Comparisons with a Control 385\u003c\/p\u003e \u003cp\u003e10.6.3.1 Pairwise Comparisons – The Multiple t-Test 385\u003c\/p\u003e \u003cp\u003e10.6.3.2 Simultaneous Comparisons –The Dunnett Method 387\u003c\/p\u003e \u003cp\u003eReferences 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Generalised Linear Models \u003c\/b\u003e\u003cb\u003e393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 393\u003c\/p\u003e \u003cp\u003e11.2 Exponential Families of Distributions 394\u003c\/p\u003e \u003cp\u003e11.3 Generalised Linear Models – An Overview 396\u003c\/p\u003e \u003cp\u003e11.4 Analysis – Fitting a GLM – The Linear Case 398\u003c\/p\u003e \u003cp\u003e11.5 Binary Logistic Regression 399\u003c\/p\u003e \u003cp\u003e11.5.1 Analysis 400\u003c\/p\u003e \u003cp\u003e11.5.2 Overdispersion 408\u003c\/p\u003e \u003cp\u003e11.6 Poisson Regression 411\u003c\/p\u003e \u003cp\u003e11.6.1 Analysis 411\u003c\/p\u003e \u003cp\u003e11.6.2 Overdispersion 417\u003c\/p\u003e \u003cp\u003e11.7 The Gamma Regression 417\u003c\/p\u003e \u003cp\u003e11.8 GLM for Gamma Regression 418\u003c\/p\u003e \u003cp\u003e11.9 GLM for the Multinomial Distribution 425\u003c\/p\u003e \u003cp\u003eReferences 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Spatial Statistics \u003c\/b\u003e\u003cb\u003e429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 429\u003c\/p\u003e \u003cp\u003e12.2 Geostatistics 431\u003c\/p\u003e \u003cp\u003e12.2.1 Semi-variogram Function 432\u003c\/p\u003e \u003cp\u003e12.2.2 Semi-variogram Parameter Estimation 439\u003c\/p\u003e \u003cp\u003e12.2.3 Kriging 440\u003c\/p\u003e \u003cp\u003e12.2.4 \u003ci\u003eTrans\u003c\/i\u003e-Gaussian Kriging 446\u003c\/p\u003e \u003cp\u003e12.3 Special Problems and Outlook 450\u003c\/p\u003e \u003cp\u003e12.3.1 Generalised Linear Models in Geostatistics 450\u003c\/p\u003e \u003cp\u003e12.3.2 Copula Based Geostatistical Prediction 451\u003c\/p\u003e \u003cp\u003eReferences 451\u003c\/p\u003e \u003cp\u003eAppendix A List of Problems 455\u003c\/p\u003e \u003cp\u003eAppendix B Symbolism 483\u003c\/p\u003e \u003cp\u003eAppendix C Abbreviations 485\u003c\/p\u003e \u003cp\u003eAppendix D Probability and Density Functions 487\u003c\/p\u003e \u003cp\u003eIndex 489\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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