LEADER 05341nam 2200673 450 001 9910463951403321 005 20200520144314.0 010 $a1-119-12273-2 010 $a1-118-45624-6 035 $a(CKB)2670000000412010 035 $a(EBL)918623 035 $a(OCoLC)850178494 035 $a(SSID)ssj0000983611 035 $a(PQKBManifestationID)11549543 035 $a(PQKBTitleCode)TC0000983611 035 $a(PQKBWorkID)11010417 035 $a(PQKB)10820688 035 $a(MiAaPQ)EBC918623 035 $a(JP-MeL)3000065405 035 $a(Au-PeEL)EBL918623 035 $a(CaPaEBR)ebr11034369 035 $a(CaONFJC)MIL769933 035 $a(EXLCZ)992670000000412010 100 $a20150411h20122012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegression analysis by example /$fSamprit Chatterjee, Ali S. Hadi 205 $aFifth edition. 210 1$aHoboken, New Jersey :$cWiley,$d2012. 210 4$dİ2012 215 $a1 online resource (734 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aDescription based upon print version of record. 311 $a0-470-90584-0 320 $aIncludes bibliographical references and index. 327 $aCover; Half Title page; Title page; Copyright page; Dedication; Preface; Chapter 1: Introduction; 1.1 What Is Regression Analysis?; 1.2 Publicly Available Data Sets; 1.3 Selected Applications of Regression Analysis; 1.4 Steps in Regression Analysis; 1.5 Scope And Organization of the Book; Exercises; Chapter 2: Simple Linear Regression; 2.1 Introduction; 2.2 Covariance and Correlation Coefficient; 2.3 Example: Computer Repair Data; 2.4 The Simple Linear Regression Model; 2.5 Parameter Estimation; 2.6 Tests of Hypotheses; 2.7 Confidence Intervals; 2.8 Predictions 327 $a2.9 Measuring the Quality of Fit2.10 Regression Line Through the Origin; 2.11 Trivial Regression Models; 2.12 Bibliographic Notes; Exercises; Chapter 3: Multiple Linear Regression; 3.1 Introduction; 3.2 Description of the Data and Model; 3.3 Example: Supervisor Performance Data; 3.4 Parameter Estimation; 3.5 Interpretations of Regression Coefficients; 3.6 Centering and Scaling; 3.7 Properties of the Least Squares Estimators; 3.8 Multiple Correlation Coefficient; 3.9 Inference for Individual Regression Coefficients; 3.10 Tests of Hypotheses in a Linear Model; 3.11 Predictions; 3.12 Summary 327 $aExercisesAppendix: Multiple Regression in Matrix Notation; Chapter 4: Regression Diagnostics: Detection of Model Violations; 4.1 Introduction; 4.2 The Standard Regression Assumptions; 4.3 Various Types of Residuals; 4.4 Graphical Methods; 4.5 Graphs Before Fitting a Model; 4.6 Graphs After Fitting a Model; 4.7 Checking Linearity and Normality Assumptions; 4.8 Leverage, Influence, and Outliers; 4.9 Measures of Influence; 4.10 The Potential-Residual Plot; 4.11 What to Do with the Outliers?; 4.12 Role of Variables in a Regression Equation; 4.13 Effects of an Additional Predictor 327 $a4.14 Robust RegressionExercises; Chapter 5: Qualitative Variables as Predictors; 5.1 Introduction; 5.2 Salary Survey Data; 5.3 Interaction Variables; 5.4 Systems of Regression Equations: Comparing Two Groups; 5.5 Other Applications of Indicator Variables; 5.6 Seasonality; 5.7 Stability of Regression Parameters Over Time; Exercises; Chapter 6: Transformation of Variables; 6.1 Introduction; 6.2 Transformations to Achieve Linearity; 6.3 Bacteria Deaths Due to X-Ray Radiation; 6.4 Transformations to Stabilize Variance; 6.5 Detection of Heteroscedastic Errors; 6.6 Removal of Heteroscedasticity 327 $a6.7 Weighted Least Squares6.8 Logarithmic Transformation of Data; 6.9 Power Transformation; 6.10 Summary; Exercises; Chapter 7: Weighted Least Squares; 7.1 Introduction; 7.2 Heteroscedastic Models; 7.3 Two-Stage Estimation; 7.4 Education Expenditure Data; 7.5 Fitting a Dose-Response Relationship Curve; Exercises; Chapter 8: the Problem of Correlated Errors; 8.1 Introduction: Autocorrelation; 8.2 Consumer Expenditure and Money Stock; 8.3 Durbin-Watson Statistic; 8.4 Removal of Autocorrelation by Transformation; 8.5 Iterative Estimation with Autocorrelated Errors 327 $a8.6 Autocorrelation and Missing Variables 330 $a Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded 410 0$aWiley series in probability and statistics. 606 $aRegression analysis 608 $aElectronic books. 615 0$aRegression analysis. 676 $a519.5/36 700 $aChatterjee$b Samprit$014454 702 $aHadi$b Ali S. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463951403321 996 $aRegression Analysis by Example$9119538 997 $aUNINA LEADER 05285nam 2201669z- 450 001 9910639988103321 005 20231214133555.0 010 $a3-0365-5950-7 035 $a(CKB)5470000001633473 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/95824 035 $a(EXLCZ)995470000001633473 100 $a20202301d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aValorization of Food Processing By-Products 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (300 p.) 311 $a3-0365-5949-3 330 $aThe papers published in this Special Issue report on recent studies investigating the exploitation of by-products produced by the food industry. The topics investigated include the extraction setups used for valuable food waste by-products and their applications as adjuncts to food preparation; the appropriate selection of solvents and extraction processes; and the interactions between extracted fractions and supplementary foods. 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