Richard J. Brook, Gregory C. Arnold
Applied Regression Analysis and Experimental Design (eBook, ePUB)
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Richard J. Brook, Gregory C. Arnold
Applied Regression Analysis and Experimental Design (eBook, ePUB)
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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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- mit Kopierschutz
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- Größe: 7.65MB
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.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 256
- Erscheinungstermin: 13. Dezember 2018
- Englisch
- ISBN-13: 9781351465885
- Artikelnr.: 54797362
- Verlag: Taylor & Francis
- Seitenzahl: 256
- Erscheinungstermin: 13. Dezember 2018
- Englisch
- ISBN-13: 9781351465885
- Artikelnr.: 54797362
Brook, Richard J.; Arnold, Gregory C.
Preface Fitting a Model to Data Introduction How to Fit a Line Residuals
Transformations to Obtain Linearity Fitting a Model Using Vectors and
Matrices Deviations from Means An Example- Value of a Postage Stamp Over
Time Problems Goodness of Fit of the Model Introduction Coefficient
Estimates for Univariate Regression Coefficient Estimates for Mulitvariate
Regression ANOVA Tables The F Test The Coefficient of Determination
Predicted Values of Y and Confidence Intervals Residuals Reduced Models
Pure Error and Lack of Fit Example- Lactation Curve Problems Which Variable
Should Be Included in the Model Introduction Orthogonal Predictor Variables
Linear Transformations of the Predictor Variables Adding Nonorthogonal
Variables Sequentially Correlation Form Variable Selection- All Possible
Regressions Variable Selection- Sequential Methods Qualitative (Dummy)
Variables Aggregation of Data Problems Peculiarities of Observations
Introduction Sensitive or High Leverage Points Outliers Weighted Least
Squares More on Transformations Eigenvalues and Principal Components Ridge
Regression Prior Information Cleaning up Data Problems The Experimental
Design Model Introduction What Makes an Experiment The Linear Model Tests
of Hypothesis Testing of Assumptions Problems Assessing the Treatment
Methods Introduction Specific Hypothesis Contrasts Factorial Analysis
Unpredicted Effects Conclusion Problems Blocking Introduction Structure of
Experimental Units Balanced Incomplete Block Designs Confounding
Miscellaneous Tricks Problems Extensions to the Model Introduction
Hierarchic Designs Repeated Measures Covariance Analysis Unequal
Replication Modeling the Data Problems Appendix A Review of Vectors and
Matrices Some Properties of Vectors Some Properties of Vector Spaces Some
Properties of Matrices Appendix B Expectation, Linear and Quadratic Forms
Expectations Linear Forms Quadratic Forms The F-Statistic Appendix C Data
Sets Ultra-Sound Measurements of Horses Hearts Ph Measurement of Leaf
Protein Lactation Records of Cows Sports Cars House-Price Data Computer
Teaching Data Weedicide Data References Index
Transformations to Obtain Linearity Fitting a Model Using Vectors and
Matrices Deviations from Means An Example- Value of a Postage Stamp Over
Time Problems Goodness of Fit of the Model Introduction Coefficient
Estimates for Univariate Regression Coefficient Estimates for Mulitvariate
Regression ANOVA Tables The F Test The Coefficient of Determination
Predicted Values of Y and Confidence Intervals Residuals Reduced Models
Pure Error and Lack of Fit Example- Lactation Curve Problems Which Variable
Should Be Included in the Model Introduction Orthogonal Predictor Variables
Linear Transformations of the Predictor Variables Adding Nonorthogonal
Variables Sequentially Correlation Form Variable Selection- All Possible
Regressions Variable Selection- Sequential Methods Qualitative (Dummy)
Variables Aggregation of Data Problems Peculiarities of Observations
Introduction Sensitive or High Leverage Points Outliers Weighted Least
Squares More on Transformations Eigenvalues and Principal Components Ridge
Regression Prior Information Cleaning up Data Problems The Experimental
Design Model Introduction What Makes an Experiment The Linear Model Tests
of Hypothesis Testing of Assumptions Problems Assessing the Treatment
Methods Introduction Specific Hypothesis Contrasts Factorial Analysis
Unpredicted Effects Conclusion Problems Blocking Introduction Structure of
Experimental Units Balanced Incomplete Block Designs Confounding
Miscellaneous Tricks Problems Extensions to the Model Introduction
Hierarchic Designs Repeated Measures Covariance Analysis Unequal
Replication Modeling the Data Problems Appendix A Review of Vectors and
Matrices Some Properties of Vectors Some Properties of Vector Spaces Some
Properties of Matrices Appendix B Expectation, Linear and Quadratic Forms
Expectations Linear Forms Quadratic Forms The F-Statistic Appendix C Data
Sets Ultra-Sound Measurements of Horses Hearts Ph Measurement of Leaf
Protein Lactation Records of Cows Sports Cars House-Price Data Computer
Teaching Data Weedicide Data References Index
Preface Fitting a Model to Data Introduction How to Fit a Line Residuals
Transformations to Obtain Linearity Fitting a Model Using Vectors and
Matrices Deviations from Means An Example- Value of a Postage Stamp Over
Time Problems Goodness of Fit of the Model Introduction Coefficient
Estimates for Univariate Regression Coefficient Estimates for Mulitvariate
Regression ANOVA Tables The F Test The Coefficient of Determination
Predicted Values of Y and Confidence Intervals Residuals Reduced Models
Pure Error and Lack of Fit Example- Lactation Curve Problems Which Variable
Should Be Included in the Model Introduction Orthogonal Predictor Variables
Linear Transformations of the Predictor Variables Adding Nonorthogonal
Variables Sequentially Correlation Form Variable Selection- All Possible
Regressions Variable Selection- Sequential Methods Qualitative (Dummy)
Variables Aggregation of Data Problems Peculiarities of Observations
Introduction Sensitive or High Leverage Points Outliers Weighted Least
Squares More on Transformations Eigenvalues and Principal Components Ridge
Regression Prior Information Cleaning up Data Problems The Experimental
Design Model Introduction What Makes an Experiment The Linear Model Tests
of Hypothesis Testing of Assumptions Problems Assessing the Treatment
Methods Introduction Specific Hypothesis Contrasts Factorial Analysis
Unpredicted Effects Conclusion Problems Blocking Introduction Structure of
Experimental Units Balanced Incomplete Block Designs Confounding
Miscellaneous Tricks Problems Extensions to the Model Introduction
Hierarchic Designs Repeated Measures Covariance Analysis Unequal
Replication Modeling the Data Problems Appendix A Review of Vectors and
Matrices Some Properties of Vectors Some Properties of Vector Spaces Some
Properties of Matrices Appendix B Expectation, Linear and Quadratic Forms
Expectations Linear Forms Quadratic Forms The F-Statistic Appendix C Data
Sets Ultra-Sound Measurements of Horses Hearts Ph Measurement of Leaf
Protein Lactation Records of Cows Sports Cars House-Price Data Computer
Teaching Data Weedicide Data References Index
Transformations to Obtain Linearity Fitting a Model Using Vectors and
Matrices Deviations from Means An Example- Value of a Postage Stamp Over
Time Problems Goodness of Fit of the Model Introduction Coefficient
Estimates for Univariate Regression Coefficient Estimates for Mulitvariate
Regression ANOVA Tables The F Test The Coefficient of Determination
Predicted Values of Y and Confidence Intervals Residuals Reduced Models
Pure Error and Lack of Fit Example- Lactation Curve Problems Which Variable
Should Be Included in the Model Introduction Orthogonal Predictor Variables
Linear Transformations of the Predictor Variables Adding Nonorthogonal
Variables Sequentially Correlation Form Variable Selection- All Possible
Regressions Variable Selection- Sequential Methods Qualitative (Dummy)
Variables Aggregation of Data Problems Peculiarities of Observations
Introduction Sensitive or High Leverage Points Outliers Weighted Least
Squares More on Transformations Eigenvalues and Principal Components Ridge
Regression Prior Information Cleaning up Data Problems The Experimental
Design Model Introduction What Makes an Experiment The Linear Model Tests
of Hypothesis Testing of Assumptions Problems Assessing the Treatment
Methods Introduction Specific Hypothesis Contrasts Factorial Analysis
Unpredicted Effects Conclusion Problems Blocking Introduction Structure of
Experimental Units Balanced Incomplete Block Designs Confounding
Miscellaneous Tricks Problems Extensions to the Model Introduction
Hierarchic Designs Repeated Measures Covariance Analysis Unequal
Replication Modeling the Data Problems Appendix A Review of Vectors and
Matrices Some Properties of Vectors Some Properties of Vector Spaces Some
Properties of Matrices Appendix B Expectation, Linear and Quadratic Forms
Expectations Linear Forms Quadratic Forms The F-Statistic Appendix C Data
Sets Ultra-Sound Measurements of Horses Hearts Ph Measurement of Leaf
Protein Lactation Records of Cows Sports Cars House-Price Data Computer
Teaching Data Weedicide Data References Index