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1.

電子ブック
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1. A Course on Small Area Estimation and Mixed Models : Methods, Theory and Applications in R. 1st ed. 2021
by Domingo Morales, María Dolores Esteban, Agustín Pérez, Tomáš Hobza
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: Statistics for Social and Behavioral Sciences ;
オンライン: https://doi.org/10.1007/978-3-030-63757-6
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目次情報: 続きを見る
1 Small Area Estimation
2 Design-based Direct Estimation
3 Design-based Indirect Estimation
4 Prediction Theory
5 Linear Models
6 Linear Mixed Models
7 Nested Error Regression Models
8 EBLUPs under Nested Error Regression Models
9 Mean Squared Error of EBLUPs
10 EBPs under Nested Error Regression Models
11 EBLUPs under Two-fold Nested Error Regression Models
12 EBPs under Two-fold Nested Error Regression Models
13 Random Regression Coefficient Models
14 EBPs under Unit-level Logit Mixed Models
15 EBPs under Unit-level Two-fold Logit Mixed Models
16 Fay-Herriot Models
17 Area-level Temporal Linear Mixed Models
18 Area-level Spatio-temporal Linear Mixed Models
19 Area-level Bivariate Linear Mixed Models
20 Area-level Poisson Mixed Models
21 Area-level Temporal Poisson Mixed Models
A Some Useful Formulas
Index
1 Small Area Estimation
2 Design-based Direct Estimation
3 Design-based Indirect Estimation
2.

電子ブック
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2. Introduction to Statistics and Data Analysis : With Exercises, Solutions and Applications in R. 2nd ed. 2022
by Christian Heumann, Michael Schomaker, Shalabh
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
オンライン: https://doi.org/10.1007/978-3-031-11833-3
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Part I Descriptive Statistics: Introduction and Framework
Frequency Measures and Graphical Representation of Data
Measures of Central Tendency and Dispersion
Association of Two Variables
Part I Probability Calculus: Combinatorics
Elements of Probability Theory
Random Variables
Probability Distributions
Part III Inductive Statistics: Inference
Hypothesis Testing
Linear Regression
Logistic Regression
Part IV Additional Topics Simple Random Sampling and Bootstrapping
Causality
Part V Appendices: Introduction to R
Solutions to Exercises
Technical Appendix
Visual Summaries
Part I Descriptive Statistics: Introduction and Framework
Frequency Measures and Graphical Representation of Data
Measures of Central Tendency and Dispersion
3.

電子ブック
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3. An Introduction to Statistics with Python : With Applications in the Life Sciences. 2nd ed. 2022
by Thomas Haslwanter
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2022
シリーズ名: Statistics and Computing ;
オンライン: https://doi.org/10.1007/978-3-030-97371-1
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目次情報: 続きを見る
I Python and Statistics
1 Introduction
2 Python
3 Data Input
4 Data Display
II Distributions and Hypothesis Tests
5 Basic Statistical Concepts
6 Distributions of One Variable
7 Hypothesis Tests
8 Tests of Means of Numerical Data
9 Tests on Categorical Data
10 Analysis of Survival Times
III Statistical Modelling
11 Finding Patterns in Signals
12 Linear Regression Models
13 Generalized Linear Models
14 Bayesian Statistics
Appendices
A Useful Programming Tools
B Solutions
C Equations for Confidence Intervals
D Web Ressources
Glossary
Bibliography
Index
I Python and Statistics
1 Introduction
2 Python