# OpenClassrooms-Student-Center/Design-Statistical-Models

Design Statistical Models on OpenClassrooms
Jupyter Notebook Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information. data .gitignore 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

# 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.

You can’t perform that action at this time.