Regression Analysis Project
- Constructed the Iowa house price prediction model via Polynomial Regression of 42 variables and transformed the variables by analyzing the scatter plot between the response and different predictors.
- Evaluated and optimized models by R-squared, AIC value, T-test, VIF value, etc. and found 95% Confidence Interval and Prediction Interval for the price of houses.
- Utilized Standardized Residual and Studentized Residual for residual analysis, and calculated Leverage and Cook's distance to analyze outliers and influential data points.