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BL
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| Record Number
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844976
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| Main Entry
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Brook, Richard J.
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| Title & Author
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Applied Regression Analysis and Experimental Design
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| Publication Statement
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Boca Raton :: Routledge,, 2018.
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| Series Statement
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Statistics: a Series of Textbooks and Monographs ;; v. 62
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| Page. NO
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1 online resource (252 pages)
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| ISBN
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1351465899
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: 9781351465892
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| Contents
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Cover; Half Title; Title Page; Copyright Page; Preface; Table of Contents; 1: Fitting a Model to Data; 1.1 Introduction; 1.2 How to Fit a Line; 1.3 Residuals; 1.4 Transformations to Obtain Linearity; 1.5 Fitting a Model Using Vectors and Matrices; 1.6 Deviations from Means; 1.7 An Example -- Value of a Postage Stamp over Time; Problems; 2: Goodness of Fit of the Model; 2.1 Introduction; 2.2 Coefficient Estimates for Univariate Regression; 2.3 Coefficient Estimates for Multivariate Regression; 2.4 ANOVA Tables; 2.5 The F-Test; 2.6 The Coefficient of Determination
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2.7 Predicted Values of Y and Confidence Intervals2.8 Residuals; 2.9 Reduced Models; 2.10 Pure Error and Lack of Fit; 2.11 Example -- Lactation Curve; Problems; 3: Which Variables Should Be Included in the Model; 3.1 Introduction; 3.2 Orthogonal Predictor Variables; 3.3 Linear Transformations of the Predictor Variables; 3.4 Adding Nonorthogonal Variables Sequentially; 3.5 Correlation Form; 3.6 Variable Selection -- All Possible Regressions; 3.7 Variable Selection -- Sequential Methods; 3.8 Qualitative (Dummy) Variables; 3.9 Aggregation of Data; Problems; 4: Peculiarities of Observations
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4.1 Introduction4.2 Sensitive, or High Leverage, Points; 4.3 Outliers; 4.4 Weighted Least Squares; 4.5 More on Transformations; 4.6 Eigenvalues and Principal Components; 4.7 Ridge Regression; 4.8 Prior Information; 4.9 Cleaning up Data; Problems; 5: The Experimental Design Model; 5.1 Introduction; 5.2 What Makes an Experiment; 5.3 The Linear Model; 5.4 Tests of Hypothesis; 5.5 Testing the Assumptions; Problems; 6: Assessing the Treatment Means; 6.1 Introduction; 6.2 Specific Hypothesis; 6.3 Contrasts; 6.4 Factorial Analysis; 6.5 Unpredicted Effects; 6.6 Conclusion; Problems; 7: Blocking
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7.1 Introduction7.2 Structure of Experimental Units; 7.3 Balanced Incomplete Block Designs; 7.4 Confounding; 7.5 Miscellaneous Tricks; Problems; 8: Extensions to the Model; 8.1 Introduction; 8.2 Hierarchic Designs; 8.3 Repeated Measures; 8.4 Covariance Analysis; 8.5 Unequal Replication; 8.6 Modelling the Data; Problems; Appendix A: Review of Vectors and Matrices; A.1 Some Properties of Vectors; A.2 Some Properties of Vector Spaces; A.3 Some Properties of Matrices; Appendix B: Expectation, Linear and Quadratic Forms; B.1 Expectation; B.2 Linear Forms; B.3 Quadratic Forms; B.4 The F-Statistic
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Appendix C: Data SetsC. 1 Ultra-Sound Measurements of Horses' Hearts; C.2 Ph Measurement of Leaf Protein; C.3 Lactation Records of Cows; C.4 Sports Cars; C.5 House Price Data; C.6 Computer Teaching Data; C.7 Weedicide Data; References; Index
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| Abstract
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For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis of experiments and provides students with the practical understanding needed to apply theory in real data analysis problems. Stressing principles while keeping computational and theoretical details at a manageable level, Applied Regression Analysis and Experimental Design features an emphasis on vector geometry and least squares to unify and provide an intuitive basis for most topics covered ... abundant examples and exercises using real-life data sets clearly illustrating practical of data analysis ... essential exposure to MINITAB and GENSTAT computer packages, including computer printouts ... and important background material such as vector and matrix properties and the distributional properties of quadratic forms. Designed to make theory work for students, this clearly written, easy-to-understand work serves as the ideal texts for courses Regression, Experimental Design, and Linear Models in a broad range of disciplines. Moreover, applied statisticians will find the book a useful reference for the general application of the linear model.
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| Subject
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Analyse de régression.
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Experimental design.
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| Subject
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Plan d'expérience.
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| Subject
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Regression analysis.
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Experimental design.
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| Subject
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Regression analysis.
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| Dewey Classification
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519.5/36
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| LC Classification
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QA278.2.B76 1985
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