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Design Statistical Models on OpenClassrooms
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P1CH3_01 Calculate Correlation.ipynb
P1CH3_02 Anscombes Quartet DatasaurusDozen.ipynb
P1CH4_01 Hypothesis Testing The T-test.ipynb
P1CH4_02 Hypothesis Testing The Kolmogorov Smirnoff test.ipynb
P2CH1 Univariate Regression.ipynb
P2CH2 Mutivariate Regression.ipynb
P2CH3_01 Assumptions of Linearity and Collinearity.ipynb
P2CH3_02 Linear Regression Assumptions on Residuals.ipynb
P3CH1 Logistic Regression.ipynb
P3CH2 Categorical Predictors.ipynb
P3CH3 Polynomial Regression.ipynb
P4CH1 Predicting and Model Selection.ipynb
P4CH2 Evaluating Classification Models.ipynb
README.md

README.md

Design-Statistical-Models

This repository, contains the Jupyter notebooks and datasets companions to the OpenClassrooms course: Design Statistical Models

Part I

We start with the core concepts required to build linear regression models

  • Linearity
  • Correlation
  • Hypothesis testing

Part II

We move on to univariate and multivariate linear regression. We start with hands-on applications to standard datasets and follow up with the underlying theoretical basis.

  • Univariate and multivariate linear regression
  • The 5 assumptions of linear regression
  • The mathematical basis for Linear regression

Part III

We expand the framework of linear regression for classification, handling categorical variables and polynomial regression.

  • Logistic regression
  • Dealing with categorical variables
  • Polynomial regression

Part IV

In the last part we move from statistical modeling to a predictive analytics approach and address overfitting, cross validation and classification metrics.

  • Predicting with linear regression
  • Classification metrics and model selection

The datasets are available in the /data folder.

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