Introduction to Probability, Statistics & R Foundations for Data-Based Sciences
Part II delves into probability concepts, including rules and conditional probability, and introduces widely used discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random v...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2024, 2024
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| Edition: | 1st ed. 2024 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Part I Introduction to basic Statistics and R
- 1 Introduction to basic statistics
- 2 Getting started with R
- Part II Introduction to Probability
- 3 Introduction to probability
- 4 Conditional probability and independence
- 5 Random variables and their probability distributions
- 6 Standard discrete distributions
- 7 Standard continuous distributions
- 8 Joint distributions and the CLT
- Part III Introduction to Statistical Inference
- 9 Introduction to statistical inference
- 10 Methods of point estimation
- 11 Interval estimation
- 12 Hypothesis testing
- Part IV Advanced Distribution Theory and Probability
- 13 Generating functions
- 14 Transformation and transformed distributions
- 15 Multivariate distributions
- 16 Convergence of estimators
- Part V Introduction to statistical modelling
- 17 Simple linear regression model
- 18 Multiple linear regression model
- 19 Analysis of variance
- Appendix: Table of common distributions