Padgett Practical Statistical Methods

A SAS Programming Approach
1. Auflage 2012
ISBN: 978-1-4398-1254-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 304 Seiten

Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems.

After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife.

With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

Zielgruppe

Researchers and applied statisticians in the pharmaceutical industry, biological sciences, physical sciences, social sciences, market research, and psychology; graduate students in a statistical methods course.

Autoren/Hrsg.

Padgett, Lakshmi

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Introduction
Types of Data
Descriptive Statistics/Data Summaries
Graphical and Tabular Representation
Population and Sample
Estimation and Testing Hypothesis
Normal Distribution
Nonparametric Methods
Some Useful Concepts

Qualitative Data
One Sample
Two Independent Samples
Paired Two Samples
k Independent Samples
Cochran’s Test
Ordinal Data

Continuous Normal Data
One Sample
Two Samples
k Independent Samples
Multivariate Methods
Multifactor ANOVA
Variance Components
Split Plot Designs
Latin Square Design
Two Treatment Crossover Design

Nonparametric Methods
One Sample
Two Samples
k Samples
Transformations
Friedman Test
Association Measures
Censored Data

Regression
Simple Regression
Polynomial Regression
Multiple Regressions
Diagnostics
Weighted Regression
Logistic Regression
Poisson Regression
Robust Regression
Nonlinear Regression
Piecewise Regression
Accelerated Failure Time (AFT) Model
Cox Regression
Parallelism of Regression Equations
Variance-Stabilizing Transformations
Ridge Regression
Local Regression (LOESS)
Response Surface Methodology: Quadratic Model
Mixture Designs and Their Analysis
Analysis of Longitudinal Data: Mixed Models

Miscellaneous Topics
Missing Data
Diagnostic Errors and Human Behavior
Density Estimation
Robust Estimators
Jackknife Estimators
Bootstrap Method
Propensity Scores
Interim Analysis and Stopping Rules
Microarrays and Multiple Testing
Stability of Products
Group Testing
Correspondence Analysis
Classification Regression Trees (CARTs)
Multidimensional Scaling
Path Analysis
Choice-Based Conjoint Analysis
Meta-Analysis

References and Selected Bibliography
Index

Über Autor(innen)

Lakshmi V. Padgett is a senior manager at Centocor. She has more than 15 years of industrial experience and has published several papers in various leading journals. She also co-authored Block Designs: Analysis, Combinatorics and Applications. She earned a Ph.D. from Temple University.