This econometrics project analyzes a dataset of house prices, focusing on the relationship between price and square footage (sqrft). The analysis begins with descriptive statistics, histograms, and scatter plots to understand data characteristics, including skewness, kurtosis, and potential outliers. Linear regression models are estimated using Stata, with interpretations of coefficients, R-squared values, and model assumptions such as exogeneity and homoskedasticity. Residual plots are used to assess the validity of these assumptions. The project also explores the impact of lot size and house style (colonial vs. non-colonial) on price through multiple linear regression. Hypothesis tests are conducted to evaluate the statistical significance of coefficients and test specific hypotheses about the relationships between variables. The analysis includes both OLS and robust standard errors, and the findings are presented with Stata outputs and detailed interpretations. The project concludes with a comparative analysis of different models and hypothesis testing results.
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