{"product_id":"statistical-planning-and-inference-concepts-and-applications-hardback-9781119962786","title":"Statistical Planning and Inference; Concepts and Applications (Hardback) 9781119962786","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistical Planning and Inference\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConcepts and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSubir Ghosh (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119962786, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 November 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e240 pages\u003cbr\u003e24.4 x 16.8 x 2 cm, 0.726 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eExplore the foundations of, and cutting-edge developments in, statistics\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eStatistical Planning and Inference: Concepts and Applications\u003c\/i\u003e delivers a robust introduction to statistical planning and inference, including classical and computer age developments in statistical science. The book examines the challenges faced in statistical planning and inference, exploring the optimum methods identifying limitations and commonly encountered pitfalls. \u003c\/p\u003e\n\u003cp\u003eIt addresses linear and non-linear statistical inference and discusses noise-effect reduction, error rates, balanced and unbalanced data, model selection, discrimination and classification, truncated and censored data, and experimental designs. \u003c\/p\u003e\n\u003cp\u003eEach chapter offers readers problems and solutions and illustrative examples to introduce the concepts and methods discussed within. \u003c\/p\u003e\n\u003cp\u003eThe book offers: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAnalysis of both classical theory and modern developments in the field of statistical inference and planning\u003c\/li\u003e\n\u003cli\u003eExpansive discussions of linear and non-linear statistical inference\u003c\/li\u003e\n\u003cli\u003eStatistical problems and solutions to test the reader’s progress through and retention of the material contained within\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eAimed at practitioners and researchers in the field of statistics, \u003ci\u003eStatistical Planning and Inference: Concepts and Applications\u003c\/i\u003e is also a must-read resource for graduate students, professors, and researchers in the life sciences, agriculture, psychology, education and measurement, sociology, computer and engineering sciences, and all other fields that rely on statistical concepts.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Foundation of Experiments 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Uncertainties in Evidences 1\u003c\/p\u003e \u003cp\u003e1.2 Examples 2\u003c\/p\u003e \u003cp\u003e1.2.1 The Louis Pasteur Anthrax Vaccination Experiment 2\u003c\/p\u003e \u003cp\u003e1.2.2 The Lanarkshire Milk Experiment: Milk Tests in Lanarkshire Schools 2\u003c\/p\u003e \u003cp\u003e1.3 Replication, Randomization, Blocking, and Blinding 4\u003c\/p\u003e \u003cp\u003e1.3.1 Replication 4\u003c\/p\u003e \u003cp\u003e1.3.2 Randomization 4\u003c\/p\u003e \u003cp\u003e1.3.3 Blocking 4\u003c\/p\u003e \u003cp\u003e1.3.4 Blinding 4\u003c\/p\u003e \u003cp\u003e1.4 Figuring It Out! 4\u003c\/p\u003e \u003cp\u003eQuestions and Answers 5\u003c\/p\u003e \u003cp\u003eBibliography 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Completely Randomized Design 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 An Example 7\u003c\/p\u003e \u003cp\u003e2.2 Analyses Using R and SAS 9\u003c\/p\u003e \u003cp\u003e2.3 Figuring It Out! 12\u003c\/p\u003e \u003cp\u003eBibliography 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Randomized Complete Block Design 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Fixed Effects Model 18\u003c\/p\u003e \u003cp\u003e3.2 Binomial Model for Signs 20\u003c\/p\u003e \u003cp\u003e3.3 Randomization Model 20\u003c\/p\u003e \u003cp\u003e3.4 Mixed Effects Model 25\u003c\/p\u003e \u003cp\u003e3.5 General Mixed Effects Model 27\u003c\/p\u003e \u003cp\u003e3.6 The REML Variance Components Estimates 28\u003c\/p\u003e \u003cp\u003e3.7 BLUEs and BLUPs 31\u003c\/p\u003e \u003cp\u003e3.7.1 The Conditional Model 32\u003c\/p\u003e \u003cp\u003e3.7.2 The Unconditional Model 32\u003c\/p\u003e \u003cp\u003e3.7.3 Computation—The Conditional Model 33\u003c\/p\u003e \u003cp\u003e3.7.4 Computation—The Unconditional Model 34\u003c\/p\u003e \u003cp\u003e3.8 Figuring It Out! 39\u003c\/p\u003e \u003cp\u003eBibliography 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Randomized Incomplete Block Design 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Model M1: Fixed-Effects Model 41\u003c\/p\u003e \u003cp\u003e4.2 Model M2: Mixed-Effects Model 43\u003c\/p\u003e \u003cp\u003e4.3 Research Questions 44\u003c\/p\u003e \u003cp\u003e4.4 Figuring It Out! 45\u003c\/p\u003e \u003cp\u003e4.5 Definitions 46\u003c\/p\u003e \u003cp\u003eExercises 46\u003c\/p\u003e \u003cp\u003eBibliography 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Error Rates 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Definitions of Error Rates 53\u003c\/p\u003e \u003cp\u003e5.2 Single-Stage Methods 55\u003c\/p\u003e \u003cp\u003e5.3 A Multistage Method 56\u003c\/p\u003e \u003cp\u003e5.3.1 Benjamini and Hochberg Method 57\u003c\/p\u003e \u003cp\u003e5.4 Figuring It Out 58\u003c\/p\u003e \u003cp\u003eQuestions 59\u003c\/p\u003e \u003cp\u003eBibliography 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Nutrition Experiment 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Figuring It Out! 63\u003c\/p\u003e \u003cp\u003eBibliography 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Pearson Dependence 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Bivariate Normal Distribution 77\u003c\/p\u003e \u003cp\u003e7.2 Estimation of Unknown Parameters 79\u003c\/p\u003e \u003cp\u003e7.2.1 The Unconditional Model 79\u003c\/p\u003e \u003cp\u003e7.2.2 The Conditional Model 81\u003c\/p\u003e \u003cp\u003e7.2.3 Test of Significance 83\u003c\/p\u003e \u003cp\u003e7.3 A Bayesian Estimation 84\u003c\/p\u003e \u003cp\u003e7.4 Exercises 86\u003c\/p\u003e \u003cp\u003eBibliography 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Multivariate Dependence 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 The Multivariate Normal Distribution 90\u003c\/p\u003e \u003cp\u003e8.2 Inference 91\u003c\/p\u003e \u003cp\u003e8.3 Partial Dependence 96\u003c\/p\u003e \u003cp\u003e8.4 Exercises 96\u003c\/p\u003e \u003cp\u003eBibliography 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Conditional Mean Dependence 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 LS Estimation 100\u003c\/p\u003e \u003cp\u003e9.2 Ridge Estimation 101\u003c\/p\u003e \u003cp\u003e9.2.1 A Bayesian Estimation 103\u003c\/p\u003e \u003cp\u003e9.3 Dependence of Ridge Estimator on the Tuning Parameter 103\u003c\/p\u003e \u003cp\u003e9.4 LASSO Estimation 104\u003c\/p\u003e \u003cp\u003e9.5 Dependence of LASSO Estimators on the Tuning Parameter 105\u003c\/p\u003e \u003cp\u003eBibliography 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 More Parameters Than Observations 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Learning by Doing—Exercises 122\u003c\/p\u003e \u003cp\u003eExercises 123\u003c\/p\u003e \u003cp\u003eBibliography 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Eigenvalues, Eigenvectors, and Applications 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Eigenvalues and Eigenvectors 127\u003c\/p\u003e \u003cp\u003e11.2 Second-Order Response Surface 129\u003c\/p\u003e \u003cp\u003eExercises 132\u003c\/p\u003e \u003cp\u003eBibliography 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Covariance Estimation 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Model 1 135\u003c\/p\u003e \u003cp\u003e12.1.1 Characterization of the Covariance Matrix and Its Estimators 135\u003c\/p\u003e \u003cp\u003e12.1.2 Likelihood Function 136\u003c\/p\u003e \u003cp\u003e12.1.3 Properties 137\u003c\/p\u003e \u003cp\u003e12.2 Model 2 137\u003c\/p\u003e \u003cp\u003e12.2.1 Characterization of the Covariance Matrix and Its Estimators 138\u003c\/p\u003e \u003cp\u003e12.3 Model 3 138\u003c\/p\u003e \u003cp\u003e12.4 Model 4 139\u003c\/p\u003e \u003cp\u003e12.5 Model 5 140\u003c\/p\u003e \u003cp\u003e12.6 Exercises 141\u003c\/p\u003e \u003cp\u003eBibliography 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Discriminant Analysis 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Learning from the Univariate Data—Two Normal Populations with Equal Variances 145\u003c\/p\u003e \u003cp\u003e13.1.1 Discriminant Analysis for the Univariate Data 147\u003c\/p\u003e \u003cp\u003e13.1.2 Example—Univariate Discriminant Analysis 148\u003c\/p\u003e \u003cp\u003e13.2 Learning from the Univariate Data—Two Normal Populations with Unequal Variances 151\u003c\/p\u003e \u003cp\u003e13.2.1 Classification of 25 Versicolor Iris Flowers 153\u003c\/p\u003e \u003cp\u003e13.2.2 Classification of 25 Setosa Iris Flowers 154\u003c\/p\u003e \u003cp\u003e13.2.3 Test of Homogeneity of Variances 154\u003c\/p\u003e \u003cp\u003e13.3 Learning from the Multivariate Data 155\u003c\/p\u003e \u003cp\u003e13.3.1 Classification of Versicolor and Setosa 156\u003c\/p\u003e \u003cp\u003e13.3.2 Classification of Versicolor and Virginica 158\u003c\/p\u003e \u003cp\u003e13.4 Logistic Regression 159\u003c\/p\u003e \u003cp\u003e13.5 Exercises 160\u003c\/p\u003e \u003cp\u003eBibliography 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Optimizing the Variance–Bias Trade-Off 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Variance–Bias Trade-Off 163\u003c\/p\u003e \u003cp\u003e14.1.1 Example 1 164\u003c\/p\u003e \u003cp\u003e14.1.2 Example 2 165\u003c\/p\u003e \u003cp\u003e14.1.3 Example 3 166\u003c\/p\u003e \u003cp\u003e14.2 Information in Data 167\u003c\/p\u003e \u003cp\u003e14.3 Information and Design in Presence of a Covariate 169\u003c\/p\u003e \u003cp\u003e14.3.1 Information 169\u003c\/p\u003e \u003cp\u003e14.3.2 Optimum Design for a Covariate 170\u003c\/p\u003e \u003cp\u003e14.4 Information and Design in Presence of Multiple Covariates 171\u003c\/p\u003e \u003cp\u003e14.4.1 Information 171\u003c\/p\u003e \u003cp\u003e14.4.2 Exponential Model 175\u003c\/p\u003e \u003cp\u003e14.4.3 Exponential Regression Model with Multiple Covariates 176\u003c\/p\u003e \u003cp\u003e14.4.4 Poisson Log-Linear Model 177\u003c\/p\u003e \u003cp\u003e14.4.5 Non-parametric Regression Model 180\u003c\/p\u003e \u003cp\u003e14.5 Exercises 183\u003c\/p\u003e \u003cp\u003eBibliography 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Specification, Discrimination, Robustness, and Sensitivity 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 The Global and Local Optimal Models 189\u003c\/p\u003e \u003cp\u003e15.2 The T-Optimal Design 190\u003c\/p\u003e \u003cp\u003e15.3 Convex and Concave Functions 192\u003c\/p\u003e \u003cp\u003e15.4 The Kullback–Leibler (KL) Divergence 194\u003c\/p\u003e \u003cp\u003e15.5 The KL Design Optimality 197\u003c\/p\u003e \u003cp\u003e15.6 The Differential Entropy 198\u003c\/p\u003e \u003cp\u003e15.7 Lindley Information Measure 200\u003c\/p\u003e \u003cp\u003e15.8 Joint Entropy, Conditional Entropy, and Mutual Information 202\u003c\/p\u003e \u003cp\u003e15.9 Maximum Entropy Sampling 204\u003c\/p\u003e \u003cp\u003e15.10 Search Linear Models and Search Designs 207\u003c\/p\u003e \u003cp\u003e15.10.1 Factorial Experiments 209\u003c\/p\u003e \u003cp\u003e15.10.2 Search Probability Matrix 210\u003c\/p\u003e \u003cp\u003e15.11 Robustness Against Unavailable Data 210\u003c\/p\u003e \u003cp\u003e15.12 Influential Sets of Observations 212\u003c\/p\u003e \u003cp\u003e15.13 Exercises 213\u003c\/p\u003e \u003cp\u003eBibliography 214\u003c\/p\u003e \u003cp\u003eData Index 217\u003c\/p\u003e \u003cp\u003eSubject Index 219\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52431017541912,"sku":"9781119962786","price":66.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119962786.jpg?v=1784768589","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistical-planning-and-inference-concepts-and-applications-hardback-9781119962786","provider":"Freshly Printed Books","version":"1.0","type":"link"}