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Doing frequentist statistics with Scipy

Description

PyData DC 2016

Github: https://github.com/gapatino/Doing-frequentist-statistics-with-Scipy Slides: http://www.slideshare.net/PyData/doing-frequentist-statistics-with-scipy

Frequentist statistical tests are still very common, and in some fields they continue to represent the technical standard. In this session we will cover the execution and interpretation of the most common tests using the SciPy.stats package, and plotting the results with Matplotlib and Seaborn. The focus will be on traditional approaches to the tests, not on Bayesian and bootstrapping approaches

The session will cover: - Normality testing - Student's t-test and ANOVA - Wilcoxon rank sum and Kruskal-Wallis - Correlation - Univariate linear and logistic regression - Chi-square - p-value interpretation - Effect size calculation

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