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1. Resampling Methods : A Practical Guide to Data Analysis. Third Edition
by Phillip I. Good
出版情報: Boston, MA : Birkhäuser Boston, 2006
オンライン: http://dx.doi.org/10.1007/0-8176-4444-X
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2. Analysing Seasonal Health Data
by Adrian G. Barnett, Annette J. Dobson
出版情報: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2010
シリーズ名: Statistics for Biology and Health ;
オンライン: http://dx.doi.org/10.1007/978-3-642-10748-1
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3. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition
by Haruo Yanai, Kei Takeuchi, Yoshio Takane
出版情報: New York, NY : Springer Science+Business Media, LLC, 2011
シリーズ名: Statistics for Social and Behavioral Sciences ;
オンライン: http://dx.doi.org/10.1007/978-1-4419-9887-3
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4. Analyse statistique des risques agro-environnementaux : Études de cas
by David Makowski, Hervé Monod
出版情報: Paris : Springer Paris, 2011
シリーズ名: Collection Statistique et probabilités appliquées ;
オンライン: http://dx.doi.org/10.1007/978-2-8178-0251-0
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5. Missing Data : Analysis and Design
by John W. Graham
出版情報: New York, NY : Springer New York, 2012
シリーズ名: Statistics for Social and Behavioral Sciences ;
オンライン: http://dx.doi.org/10.1007/978-1-4614-4018-5
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6. A Concise Guide to Statistics
by Hans-Michael Kaltenbach
出版情報: Berlin, Heidelberg : Hans-Michael Kaltenbach, 2012
シリーズ名: SpringerBriefs in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-3-642-23502-3
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7. Statistical Challenges in Modern Astronomy V
edited by Eric D. Feigelson, G. Jogesh Babu
出版情報: New York, NY : Springer New York : Imprint: Springer, 2012
シリーズ名: Lecture Notes in Statistics ; 902
オンライン: http://dx.doi.org/10.1007/978-1-4614-3520-4
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目次情報: 続きを見る
Statistics in Cosmology
Bayesian Analysis Across Astronomy
Data Mining and Astroinformatics
Image and Time Series Analysis
The Future of Astrostatistics
Contributed Papers
Statistics in Cosmology
Bayesian Analysis Across Astronomy
Data Mining and Astroinformatics
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8. Six Sigma with R : Statistical Engineering for Process Improvement
by Emilio L. Cano, Javier M. Moguerza, Andrés Redchuk
出版情報: New York, NY : Springer New York : Imprint: Springer, 2012
シリーズ名: Use R! ; 36
オンライン: http://dx.doi.org/10.1007/978-1-4614-3652-2
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目次情報: 続きを見る
Part I Basics
Six Sigma in a Nutshell
R from the Beginning
Part II R Tools for the Define phase
Process Mapping with R
Loss Function Analysis with R
Part III R Tools for the Measure phase
Measurement System Analysis with R
Pareto Analysis with R
Process Capability Analysis with R
Part IV R Tools for the Analyze phase
Charts with R
Statistics and Probability with R
Statistical Inference with R
Part V R Tools for the Improve phase
Design of Experiments with R
Part VI R Tools for the Control phase
Process Control with R
Part VII Further and Beyond
Other Tools and Methodologies
Part I Basics
Six Sigma in a Nutshell
R from the Beginning
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9. Modeling Infectious Disease Parameters Based on Serological and Social Contact Data : A Modern Statistical Perspective
by Niel Hens, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, Philippe Beutels
出版情報: New York, NY : Springer New York : Imprint: Springer, 2012
シリーズ名: Statistics for Biology and Health ; 63
オンライン: http://dx.doi.org/10.1007/978-1-4614-4072-7
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目次情報: 続きを見る
Mathematical models for infectious diesease
The static model
The dynamic model
The stochastic model
Implementation of models in MATLAB
Data sources for modelling infectious diseases
Estimation from serological data
Parametric models for teh prevalence and the force of infection
Non-parametric approaches to model the prevalence and force of infection
Semi-parametric approaches to model the prevalence and force of infection
A Bayesian approach
Modelling the prevalence and the force of infection direction from antibody levels
Modelling multivariate serological data
Estimation from other data sources
Estimating mixing patterns and Ro in a heterogenous population
Modelling in a homogeneous population
Modelling in a heterogeneous population
Modelling AIDS outbreak data
Modelling hepatitis C among injection drug users
Modelling dengue
Modelling bovine herpes virus in cattle
Mathematical models for infectious diesease
The static model
The dynamic model
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10. SAS for Epidemiologists : Applications and Methods
by Charles DiMaggio
出版情報: New York, NY : Springer New York : Imprint: Springer, 2013
オンライン: http://dx.doi.org/10.1007/978-1-4614-4854-9
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目次情報: 続きを見る
Introduction
The SAS Environment
Working with SAS Data
Preliminary Procedures
Manipulating Data
Descriptive Statistics
Histograms and Plots
Categorical Data Analysis I and II
Cleaning and Assessing Continuous Data using MEANS
ANOVA
Correlation
Linear Regression
Regression Diagnostics
Solutions
Introduction
The SAS Environment
Working with SAS Data