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A Modern Introduction to Probability and Statistics
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Why probability and statistics?
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Chapter 2
Outcomes, events, and probability
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Chapter 3
Conditional probability and independence
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Chapter 4
Discrete random variables
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Chapter 5
Continuous random variables
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Chapter 6
Simulation
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Chapter 7
Expectation and variance
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Chapter 8
Computations with random variables
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Chapter 9
Joint distributions and independence
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Chapter 10
Covariance and correlation
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Chapter 11
More computations with more random variables
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Chapter 12
The Poisson process
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Chapter 13
The law of large numbers
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Chapter 14
The central limit theorem
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Chapter 15
Exploratory data analysis: graphical summaries
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Chapter 16
Exploratory data analysis: numerical summaries
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Chapter 17
Basic statistical models
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Chapter 18
The bootstrap
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Chapter 19
Unbiased estimators
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Chapter 20
Efficiency and mean squared error
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Chapter 21
Maximum likelihood
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Chapter 22
The method of least squares
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Chapter 23
Confidence intervals for the mean
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Chapter 24
More on confidence intervals
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Chapter 25
Testing hypotheses: essentials
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Chapter 26
Testing hypotheses: elaboration
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Chapter 27
The t-test
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Chapter 28
Comparing two samples
Overall attention for this book and its chapters
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Mentioned by
blogs
1
blog
twitter
17
X users
syllabi
2
institutions with syllabi
facebook
1
Facebook page
wikipedia
48
Wikipedia pages
Citations
dimensions_citation
474
Dimensions
Readers on
mendeley
292
Mendeley
citeulike
1
CiteULike
Book overview
1. Why probability and statistics?
2. Outcomes, events, and probability
3. Conditional probability and independence
4. Discrete random variables
5. Continuous random variables
6. Simulation
7. Expectation and variance
8. Computations with random variables
9. Joint distributions and independence
10. Covariance and correlation
11. More computations with more random variables
12. The Poisson process
13. The law of large numbers
14. The central limit theorem
15. Exploratory data analysis: graphical summaries
16. Exploratory data analysis: numerical summaries
17. Basic statistical models
18. The bootstrap
19. Unbiased estimators
20. Efficiency and mean squared error
21. Maximum likelihood
22. The method of least squares
23. Confidence intervals for the mean
24. More on confidence intervals
25. Testing hypotheses: essentials
26. Testing hypotheses: elaboration
27. The t-test
28. Comparing two samples
Summary
Blogs
X
Syllabi
Facebook
Wikipedia
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
5
syllabi from
2
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
The University of Texas at Austin
4
Unknown
Unknown
1
Anthropology