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Applied Linear Statistical Models (Int'l Ed)

4 ed. Illustrations, unspecified. Sprache: Englisch.
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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. … weiterlesen
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Applied Linear Statistical Models (Int'l Ed) als Buch

Produktdetails

Titel: Applied Linear Statistical Models (Int'l Ed)
Autor/en: Michael H. Kutner, John Neter, Christopher J. Nachtsheim, William Wasserman

ISBN: 0071122214
EAN: 9780071122214
4 ed.
Illustrations, unspecified.
Sprache: Englisch.
McGraw-Hill Education - Europe

16. Oktober 2004 - gebunden

Beschreibung

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.

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

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