Statistics for Data Science with Python.

An IBM Skills Network course to become familiar with Python statistical tools.

Jupyter Notebook of the final project. Shared with MyBinder.
Stephane Dedieu November 2022

Insights in Boston real estate - Statistical analysis.

Home prices v. various features.
Descriptive Statistics - Statistical Analysis - Tests.
Levene Test (Variances), T-test, ANOVA, Pearson Correlation, Regression analysis.

The following describes the dataset variables:

$$ \begin{array}{|c|c|} \hline \small\textbf{Code} & \small\textbf{Features} \\ \hline \small\text{CRIM} & \small\text{per capita crime rate by town} \\ \hline \small\text{ZN} & \small\text{proportion of residential land zoned for lots over 25,000 sq.ft} \\ \hline \small\text{INDUS} & \small\text{proportion of non-retail business acres per town.} \\ \hline \small\text{CHAS} & \small\text{Charles River dummy variable (1 if tract bounds river; 0 otherwise)} \\ \hline \small\text{NOX} & \small\text{nitric oxides concentration (parts per 10 million)} \\ \hline \small\text{RM} & \small\text{average number of rooms per dwelling} \\ \hline \small\text{AGE} &\small\text{proportion of owner-occupied units built prior to 1940} \\ \hline \small\text{DIS} & \small\text{weighted distances to five Boston employment centres} \\ \hline \small\text{RAD} & \small\text{index of accessibility to radial highways} \\ \hline \small\text{TAX} & \small\text{full-value property-tax rate per \$10,000} \\ \hline \small\text{PTRATIO} & \small\text{pupil-teacher ratio by town} \\ \hline \small\text{LSTAT} & \small\text{\% lower status of the population} \\ \hline \small\text{MEDV} & \small\text{Median value of owner-occupied homes in \$1000's} \\ \hline \end{array} $$

Note: Boston home price dataset is pretty old. 1970s.

Statistics for Data Science with Python - Notebook
"Table of Content" links will not work ! Execution with MyBinder (below) fixes the issue.

Notebook execution with MyBinder