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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.