Advanced Data Analysis from an Elementary Point of View pdf epub mobi txt 電子書 下載2025
- Statistics
- 美國
- 統計進階
- 統計學
- 教材
- Statistics&ML
- 數據分析
- 統計學
- 高等教育
- 數據科學
- 概率論
- 綫性代數
- 機器學習
- R語言
- Python
- 數學
具體描述
This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.
By making this draft generally available, I am not promising to provide any assistance or even clarification whatsoever. Comments are, however, welcome.
The book is under contract to Cambridge University Press; it should be turned over to the press at the end of 2013 or beginning of 2014. A copy of the next-to-final version will remain freely accessible here permanently.
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/
著者簡介
Associate Professor
Statistics Department
Baker Hall 229C
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213-3890 USA
圖書目錄
I. Regression and Its Generalizations
Regression Basics
The Truth about Linear Regression
Model Evaluation
Smoothing in Regression
Simulation
The Bootstrap
Weighting and Variance
Splines
Additive Models
Testing Regression Specifications
More about Hypothesis Testing
Logistic Regression
Generalized Linear Models and Generalized Additive Models
II. Multivariate Data, Distribution Estimates, and Latent Structure
Multivariate Distributions
Density Estimation
Relative Distributions and Smooth Tests
Principal Components Analysis
Factor Analysis
Mixture Models
Graphical Models
III. Causal Inference
Graphical Causal Models
Identifying Causal Effects
Estimating Causal Effects
Discovering Causal Structure
IV. Dependent Data
Time Series
Time Series with Latent Variables
Longitudinal, Spatial and Network Data
Appendices
A. Writing R Functions
B. Big O and Little o Notation
C. chi-squared and the Likelihood Ratio Test
D. Proof of the Gauss-Markov Theorem
E. Constrained and Penalized Optimization
F. Rudimentary Graph Theory
G. Pseudo-code for the SGS Algorithm
· · · · · · (收起)
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fantastic, this is the one, although lack of traditional stochastic point of view
评分fantastic, this is the one, although lack of traditional stochastic point of view
评分卡耐基梅隆係統計學都這路數
评分卡耐基梅隆係統計學都這路數
评分卡耐基梅隆係統計學都這路數
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