Every engineer and scientist should have in their toolbox a course module on Statistics and Probability theory. There is extensive literature with well-written information and theory. However, a condensed library that combines the concepts of statistics and probability with a Python-focused implementation is not clearly available. We provide all the Python applications required for Statistics and Probability in this repository.
- Chapter 1 - The Sample and Its Properties (descriptive statistics)
- Chapter 2 - Probability, Conditional Probability, and Bayes’ Rule
- Chapter 3 - Sensitivity, Specificity, and Relatives
- Chapter 4 - Random Variables (and common distributions)
- Chapter 5 - Normal Distribution
- Chapter 6 - Point and Interval Estimators
- Chapter 7 - Bayesian Approach to Inference
- Chapter 8 - Testing Statistical Hypotheses
- Chapter 9 - Two Samples
- Chapter 10 - ANOVA and Elements of Experimental Design
- Chapter 11 - Distribution-Free Tests
- Chapter 12 - Goodness-of-Fit Tests
- Chapter 13 - Models for Tables
- Chapter 14 - Correlation
- Brani Vidakovic - Statistics for Bioengineering Sciences
- Benjamin Yakir - Introduction to Statistical Thinking
- Christian Heumann & Michael Schomaker Shalabh - Introduction to Statistics and Data Analysis
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