Books  Data Analysis & Modelling  Data Analysis & Statistics 

Applied Linear Statistical Models

Textbook

Series: McGraw-Hill Key Textbooks Series

By: John Neter, William Wasserman, Michael H Kutner and Christopher J Nachtsheim

1408 pages, Figs, tabs

Irwin

Paperback | Dec 2005 | Edition: 5 | #151957 | ISBN: 0071122214
Availability: Usually dispatched within 1 week Details
NHBS Price: £45.99 $58/€54 approx

About this book

There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities,and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.


Contents

Contents: Linear Regression with One Independent Variable
Inferences in Regression Analysis
Diagnostic and Remedial Measures
Simultaneous Inferences and Other Topics in Regression Analysis
Matrix Approach to Simple Linear Regression Analysis
Multiple Regression I
Multiple Regression II
Building the Regression Model I: Selection of Predictor Variables
Building the Regression Model II: Diagnostics
Building the Regression Model III: Remedial Measures and Validation
Qualitative Predictor Variables
Autocorrelation in Time Series Data
Introduction to Nonlinear Regression
Logistic Regression, Poisson Regression, and Generalized Linear Models
Normal Correlation Models
Analysis of Variance
Analysis of Factor-Level Effects
ANOVA Diagnostics and Remedial Measures
Two-Factor Analysis of VarianceßEqual Sample Sizes
Analysis of Factor Effects in Two-Factor StudiesßEqual Sample Sizes
Two-Factor StudiesßOne Case per Treatment
Two Factor StudiesßUnequal Sample Sizes and Unequal Treatment Importance
Multi-Factor Studies
Random and Mixed-Effect Models
Analysis of Covariance
Design of Experiments, Randomization, and Sample Size Planning
Randomized Block Designs
Nested Designs, Subsampling, and Partially Nested Designs
Repeated Measure Designs
Latin Square and Related Designs
Explanatory Experiments--Two-level Factorial and Fractional Factorial Designs
Response Surface Methodology

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