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Section 1: Getting started with testing statistical hypotheses with Python 3
This section explains what we need to know about the class and the prerequisites
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Lecture 1: What to know about the class 03 minLecture1.1
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Lecture 2: Installing Python Anaconda distribution on your PCLecture1.2
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Lecture 3: Testing hypotheses in Data Science with Python 3 class structure 07 minLecture1.3
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Section 2: Parametric tests of hypotheses using Python
Testing hypotheses in Python based on parametric assumptions
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Lecture 4: Testing if the data is normally distributed in Python 3 14 minLecture2.1
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Lecture 5: Test of hypothesis about a correlation coefficient in Python 13 minLecture2.2
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Lecture 6: One sample t-test using Python 11 minLecture2.3
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Lecture 7: One sample Z test about the population the mean in Python 10 minLecture2.4
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Lecture 8: One sample Z test about a population proportion p 10 minLecture2.5
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Lecture 9: One sample test about the population variance using Python 08 minLecture2.6
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Lecture 10: Two samples test about the population mean using t-test in Python 13 minLecture2.7
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Lecture 11: Two-samples test about the population mean using the Z test in Python 13 minLecture2.8
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Lecture 12: Two sample test about the population proportion using Python 08 minLecture2.9
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Lecture 13: Computing the left and right tailed P-values of the Student t-test in Python 06 minLecture2.10
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Lecture 14: Conducting a paired t-test in Python for related samples 09 minLecture2.11
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Lecture 15: Test of equality of variance using Barlett’s test in Python 07 minLecture2.12
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Lecture 16: Chi-squared test of independence using Python 06 minLecture2.13
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Lecture 17: Chi square Goodness of fit test using Python 09 minLecture2.14
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Lecture 18: Performing a One way Analysis of Variance (ANOVA) in Python 18 minLecture2.15
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Section 3: Hands on Project about testing Hypotheses in Python 3
Project files for the course Testing hypotheses using Python
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Lecture 19: About the Project for testing hypotheses in Python 3 03 minLecture3.1
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SECTION 4: Nonparametric tests of hypotheses using Python
This section is about nonparametric tests of hypotheses using Python.
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Lecture 20: Mann-Whitney-Wilcoxon test about two populations mean in Python 07 minLecture4.1
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Lecture 21: Computing the Fisher’s exact test in Python 13 minLecture4.2
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Lecture 22: Test of equality of two populations variances using Levene’s test in Python 08 minLecture4.3
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Lecture 23: Computing the Cochran Q test in Python 08 minLecture4.4
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Lecture 24: Wilcoxon nonparametric test about the population mean in Python – Part 1 04 minLecture4.5
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Lecture 25: Wilcoxon nonparametric test about the population mean in Python – Part 2 07 minLecture4.6
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Lecture 26: Computing Kolmogorov-Smirnov test in Python 06 minLecture4.7
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Lecture 27: Computing the Friedman test in Python (Part1) 09 minLecture4.8
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Lecture 28: Computing the Friedman test in Python (Part2) 04 minLecture4.9
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Lecture 29: Using Post hoc nonparametric tests in Python Scikit library 11 minLecture4.10
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Section 5: Conclusion for Testing Statistical Hypotheses in Data science with Python
Concluding remarks for the course
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Conclusion for Testing hypotheses using Python 02 minLecture5.1
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