01032nam0 2200313 450 00003165420130222100241.020120314d1980----km-y0itaa50------baitaITPube angelicaleManuel Puigtraduzione di Angelo Morino2. edTorinoEinaudi1980253 p.21 cmNuovi coralli2852001Nuovi coralli2852001Pubis angelical25888Puig,Manuel132059Morino,Angelo132915ITUniversità della Basilicata - B.I.A.RICAunimarc000031654Pubis angelical25888UNIBASLETTERESTD0780120120314BAS010959STD0780120120314BAS011016TTM3020130222BAS011002BAS01BAS01BOOKBASA1Polo Storico-UmanisticoGENCollezione generaleFP/4823048230L482302012031402Prestabile Generale05485nam 2200721Ia 450 991102014310332120200520144314.09786612188893978128218889112821888959780470746912047074691297804707469290470746920(CKB)1000000000794253(EBL)454378(OCoLC)609843881(SSID)ssj0000354380(PQKBManifestationID)11251813(PQKBTitleCode)TC0000354380(PQKBWorkID)10302637(PQKB)10117377(MiAaPQ)EBC454378(Perlego)2750590(EXLCZ)99100000000079425320090306d2009 uy 0engur|n|---|||||txtccrAn introduction to optimal designs for social and biomedical research /Martijn P.F. Berger, Weng Kee WongHoboken, NJ Wiley20091 online resource (348 p.)Statistics in Practice ;v.83Description based upon print version of record.9780470694503 0470694505 Includes bibliographical references and index.An Introduction to Optimal Designs for Social and Biomedical Research; Contents; Preface; Acknowledgements; 1 Introduction to designs; 1.1 Introduction; 1.2 Stages of the research process; 1.2.1 Choice of a 'good' design; 1.3 Research design; 1.3.1 Choice of independent variables and levels; 1.3.2 Units of analysis; 1.3.3 Variables; 1.3.4 Replication; 1.4 Types of research designs; 1.5 Requirements for a 'good' design; 1.5.1 Statistical conclusion validity; 1.5.2 Internal validity; 1.5.3 Control of (unwanted) variation; 1.6 Ethical aspects of design choice1.7 Exact versus approximate designs1.8 Examples; 1.8.1 Radiation dosage example; 1.8.2 Designs for the Poggendorff and Ponzo illusion experiments; 1.8.3 Uncertainty about best .tting regression models; 1.8.4 Designs for a priori contrasts among composite faces; 1.8.5 Designs for calibration of item parameters in item response theory models; 1.9 Summary; 2 Designs for simple linear regression; 2.1 Design problem for a linear model; 2.1.1 The design; 2.1.2 The linear regression model; 2.1.3 Estimation of parameters and efficiency; 2.2 Designs for radiation-dosage example2.3 Relative efficiency and sample size2.4 Simultaneous inference; 2.5 Optimality criteria; 2.5.1 D-optimality criterion; 2.5.2 A-optimality criterion; 2.5.3 G-optimality criterion; 2.5.4 E-optimality criterion; 2.5.5 Number of distinct design points; 2.6 Relative efficiency; 2.7 Matrix formulation of designs for linear regression; 2.8 Summary; 3 Designs for multiple linear regression analysis; 3.1 Design problem for multiple linear regression; 3.1.1 The design; 3.1.2 The multiple linear regression model; 3.1.3 Estimation of parameters and ef.ciency; 3.2 Designs for vocabulary-growth study3.3 Relative efficiency and sample size3.4 Simultaneous inference; 3.5 Optimality criteria for a subset of parameters; 3.6 Relative efficiency; 3.7 Designs for polynomial regression model; 3.7.1 Exact D-optimal designs for a quadratic regression model; 3.7.2 Scale dependency of A- and E-optimality criteria; 3.8 The Poggendorff and Ponzo illusion study; 3.9 Uncertainty about best .tting regression models; 3.10 Matrix notation of designs for multiple regression models; 3.10.1 Design for regression models with two independent variables3.10.2 Design for regression models with two non-additive independent variables3.11 Summary; 4 Designs for analysis of variance models; 4.1 A typical design problem for an analysis of variance model; 4.1.1 The design; 4.1.2 The analysis of variance model; 4.1.3 Formulation of an ANOVA model as a regression model; 4.2 Estimation of parameters and efficiency; 4.2.1 Measures of uncertainty; 4.3 Simultaneous inference and optimality criteria; 4.4 Designs for groups under stress study; 4.4.1 A priori planned unequal sample sizes; 4.4.2 Not planned unequal sample sizes4.5 Specific hypotheses and contrastsThe increasing cost of research means that scientists are in more urgent need of optimal design theory to increase the efficiency of parameter estimators and the statistical power of their tests. The objectives of a good design are to provide interpretable and accurate inference at minimal costs. Optimal design theory can help to identify a design with maximum power and maximum information for a statistical model and, at the same time, enable researchers to check on the model assumptions. This Book:Introduces optimal experimental design in an accessible format.ProStatistics in PracticeSocial sciencesResearchBiologyResearchOptimal designs (Statistics)Social sciencesResearch.BiologyResearch.Optimal designs (Statistics)300.72Berger Martijn P. F1837963Wong Weng Kee1837964MiAaPQMiAaPQMiAaPQBOOK9911020143103321An introduction to optimal designs for social and biomedical research4416835UNINA