/Statistical_testing_python

This repository is created for storing the components of Statistical Tests of One Pop, Two Pops and Three or more pops using Python.

Primary LanguageJupyter Notebook

Statistical Testing :: Using Pen, Paper, Python and Excel

This repository is created for storing the components of Statistical Tests carried out on One, Two and Three or more populations using Python. Here, I have also worked on some of the real-life datasets to perform Statistical or Hypothesis Testing.
Also, shared the hand-written notes & python implementations which I created to attain better knowledge around these tests.

MMGs_Video

Below are the tasks carried out in this project:

  1. Python implementation of statistical tests

  2. Solved One Population problems

    1. T Test or STUDENT-T or STUDENT Test
    2. Z Test
    3. Population Proportion
    4. Chi-Square Test

  3. Solved Two Populations problems

    1. Large Independent Samples
      1. Pooled Large Independent Samples
      2. Not-Pooled Large Independent Samples
    2. Small Independent Samples
      1. Pooled Small Independent Samples
      2. Not Pooled Small Independent Samples
    3. Population Proportions
      1. Large Independent Proportions -- Z Test
    4. Dependent Samples
      1. Small Dependent Samples -- T Test
    5. F-Distribution (2 variances or standard deviations)

  4. ANOVA

    1. Solved One-factor problems

      1. Post-Hoc Analysis
      2. Normality Test
      3. Homogenity Test

    2. Solved Two-factors W/O Repetition problems

      1. Running 1-Way ANOVA
      2. Running 2-Way ANOVA
      3. Post-Hoc Analysis
      4. Normality Test
      5. Homogenity Test

    3. Solved Two-factors With Repetition problems

      1. Running 1-Way ANOVA
        1. 1-Way ANOVA Post-Hoc
      2. Running 2-Way ANOVA
        1. Post-Hoc Analysis
        2. Normality Test
        3. Homogenity Test

  5. Bootstrapping and its usecases

  6. How to use Multi-variate ANOVA, ANCOVA & MANCOVA & interpret their results?

  7. Let's use Excel for ANOVA

  8. Understand various Distribution Functions graphically

🤿 Fun-Fact :: Why I wrote some of these statistical tests from scratch? 🤷‍♂️

  • It was not only my eagerness to gain a full understanding but python statistical packages (like statsmodels and others) were following slightly different mathematical formulations for these tests.
  • So, I was getting a noticeable difference while comparing my on-paper calculated p-values with python-generated p-values. That motivated me to look into the statsmodels implementations and find such differences. 😇

📙 Textbook referred :: Biostatistics: A Foundation for Analysis in the Health Sciences, 10th Edition

✅ Datasets used in Textbook :: Download 👈