Statistics in Python¶

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http://pandas.pydata.org/

https://www.statsmodels.org/

https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html

https://pingouin-stats.org/

Freely Available Raw Data¶

If you need some data to try out your statistics skills, maybe one of the following helps ...

Andrew J. Tatem, Carlos A. Guerra, Peter M. Atkinson, Simon I. Hay
Athletics: Momentous sprint at the 2156 Olympics?
Nature Volume 431, Issue 525, 2004.
supplementary information

Bicycle data from Seattle:
http://www.seattlebikeblog.com/2014/06/09/a-statistical-analysis-of-biking-on-the-fremont-bridge-part-1-overview/
https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/
https://jakevdp.github.io/blog/2015/07/23/learning-seattles-work-habits-from-bicycle-counts/

Tutorials etc.¶

http://statpages.org/

Getting started with Pandas:
http://efavdb.com/pandas-tips-and-tricks/

Analysing Weed Pricing across US:
https://github.com/amitkaps/weed

Probability, Paradox, and the Reasonable Person Principle:
http://nbviewer.ipython.org/url/norvig.com/ipython/Probability.ipynb

Machine Learning for Hackers:
http://slendermeans.org/category/will-it-python.html

CS109 Data Science (video lecture):
http://cs109.org/

spurious correlations:
http://www.tylervigen.com/

NIST/SEMATECH e-Handbook of Statistical Methods:
http://www.itl.nist.gov/div898/handbook/

Implementing a Principal Component Analysis (PCA) in Python step by step:
http://sebastianraschka.com/Articles/2014_pca_step_by_step.html

What Educated Citizens Should Know About Statistics and Probability (Jessica Utts, The American Statistician Volume 57, Issue 2, 2003):
http://www.tandfonline.com/doi/abs/10.1198/0003130031630

Things in Pandas I Wish I'd Known Earlier:
http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb

PCA:
http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb

Probabilistic Programming & Bayesian Methods for Hackers:
http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

A Concrete Introduction to Probability (using Python):
http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb

Probability, Paradox, and the Reasonable Person Principle:
http://nbviewer.jupyter.org/url/norvig.com/ipython/ProbabilityParadox.ipynb

Modern Pandas Series: Modern Pandas, Method Chaining, Indexes, Performance, Tidy Data, Visualization, Larger Data (forthcoming)

Distance Metrics for Fun and Profit, Finding Similar Music using Matrix Factorization

Frequentism and Bayesianism: A Practical Introduction, When Results Differ, Confidence, Credibility, and why Frequentism and Science do not Mix, How to be a Bayesian in Python, Model Selection

Easier data analysis in Python with pandas (video series):
http://www.dataschool.io/easier-data-analysis-with-pandas/)

Think Stats, Think Bayes (free PDF books): http://greenteapress.com/thinkstats2/index.html, http://greenteapress.com/wp/think-bayes/

Computational and Inferential Thinking: The Foundations of Data Science
http://www.inferentialthinking.com/

Points of Significance
http://www.nature.com/collections/qghhqm/pointsofsignificance

https://github.com/KIPAC/StatisticalMethods

Introduction to Bayesian Inference: https://www.datascience.com/blog/introduction-to-bayesian-inference-learn-data-science-tutorials

EN 605.448 Data Science, at the Johns Hopkins University, Whiting School of Engineering:
http://nbviewer.jupyter.org/github/actsasgeek/en605448/blob/master/notebooks/010.1%20-%20Introduction.ipynb
https://github.com/actsasgeek/en605448

PROBABILITY THEORY: THE LOGIC OF SCIENCE by E. T. Jaynes (1994) http://omega.albany.edu:8008/JaynesBook.html

Case Studies¶

TODO: Try to analyze (and probably reproduce) some existing studies.

Comparison of violins:

http://www.pnas.org/content/109/3/760
http://www.pnas.org/content/early/2014/04/03/1323367111
http://phenomena.nationalgeographic.com/2014/04/07/stradivarius-violins-arent-better-than-new-ones-round-two/
http://josephcurtinstudios.com/article/the-indianapolis-experiment/
http://www.violinist.com/blog/laurie/20121/13039/
http://www.npr.org/blogs/deceptivecadence/2012/01/02/144482863/double-blind-violin-test-can-you-pick-the-strad
http://www.artsjournal.com/slippeddisc/2012/01/exclusive-how-i-blind-tested-old-violins-against-new.html
http://www.thestrad.com/latest/editorschoice/from-the-archive-classic-and-modern-violins-compared
http://www.thestrad.com/video/new-vs-old-follow-up-to-the-indianapolis-blind-testing-experiment
http://diepresse.com/home/meinung/marginalien/1587780/Bei-Stradivari-hat-vor-allem-der-Mythos-Klang
http://abcnews.go.com/Technology/wireStory/blind-test-soloists-violins-23227307
http://en.wikipedia.org/wiki/Player_preferences_among_new_and_old_violins

Other Stuff¶

TODO: explain "power" and "effect"

https://github.com/grrrr/krippendorff-alpha

Matlab® routines for analyzing psychophysical data: http://www.palamedestoolbox.org/index.html

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