A01=Ben Klemens
Apophenia
Arbitrary-precision arithmetic
Assertion (software development)
Author_Ben Klemens
Binary tree
Binomial distribution
Bootstrapping (statistics)
C syntax
C99
Calculation
Category=GPH
Category=UFM
Category=UMT
Category=UNA
Central limit theorem
Chi-squared test
Command-line interface
Compiler
Conditional (computer programming)
Conjugate gradient method
Convenience function
Cook's distance
Count data
Counting
Cramer-Rao bound
Data set
Database
Debugger
Dummy variable (statistics)
Environment variable
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Error term
Estimation
Executable
Expected value
F-test
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Fisher's exact test
For loop
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Include directive
Indent style
Integer
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Least squares
Likelihood function
Likelihood-ratio test
Linker (computing)
Listwise deletion
Local variable
Logistic regression
Logit
Long filename
Makefile
Mathematical optimization
Maximum likelihood estimation
Missing data
Monte Carlo method
Naming convention (programming)
Negative binomial distribution
Newline
Newton's method
Normal distribution
Null pointer
Object file
One-way analysis of variance
Order of operations
Ordinary least squares
Pairwise
Parameter (computer programming)
Pattern matching
Preprocessor
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Pseudocode
Ranking (information retrieval)
Regular expression
Root-finding algorithm
Sentinel value
Significant figures
Source lines of code
SQL
SQLite
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Struct (C programming language)
Student's t-test
Subset
Type I and type II errors
Variable (computer science)
Variable (mathematics)
Variance
Void type
Product details
- ISBN 9780691133140
- Weight: 1021g
- Dimensions: 178 x 254mm
- Publication Date: 26 Oct 2008
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
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Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.
Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.
Ben Klemens is a senior statistician at the National Institute of Mental Health. He is also a guest scholar at the Center on Social and Economic Dynamics at the Brookings Institution.
