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.