Applied Linear Statistical Models (Int'l Ed)

APPLIED
4. Auflage 2004
ISBN: 978-0-07-112221-4
Verlag: McGraw-Hill Education

Buch, Englisch, 0 Seiten

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

Autoren/Hrsg.

Kutner, Michael

Neter, John

Nachtsheim, Christopher

Wasserman, William

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Part1 Simple Linear Regression 1 Linear Regression with One Predictor Variable 2 Inferences in Regression Analysis 3 Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis Part 2 Multiple Linear Regression 6 Multiple Regression I 7 Multiple Regression II 8 Regression Models for Quantitative and Qualitative Predictors 9 Building the Regression Model I: Model Selection and Validation 10Building the Regression Model II: Diagnostics 11Building the Regression Model III: Remedial Measures 12Autocorrelation in Time Series Data Part 3NonLinear Regression 13Introduction to NonLinear Regression and Neural Networks 14Logistic Regression, Poisson Regression, and Generalized Linear Models Part 4 Single Factor Studies 15Introduction to the Design of Experiments 16Analysis of Single-Factor Studies 17Analysis of Factor Level Effects in Single Factor Studies 18ANOVA Diagnostics and Remedial Measures Part 5 Two -Factor Studies and Blocking 19Two -Factor Studies- Equal Sample Sizes 20 Two -Factor Studies-One Case per Cell 21 Randomized Complete Block Designs and the Analysis of Covariance 22Two -Factor Studies-Unequal Sample Sizes and Unequal Treatment Importance Part 6 Multifactor Studies 23 Multifactor Studies 24Random and Mixed-Effects Models 25Nested Designs, Subsampling, and Partially Nested Designs 26Repeated Measures and Related Designs 27Latin Square, Balanced Incomplete Block, and Related Designs 28Exploratory Experiments-Two-Level Factorial and Fractional Factorial Designs 29Response Surface Experiments