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1 Regression Forecasting for Numerical Data

Use the Quality of Life data (Case06_QoL_Symptom_ChronicIllness) to fit several different Multiple Linear Regression models predicting clinically relevant outcomes, e.g., Chronic Disease Score.

  • Summarize and visualize the data using summary, str, pairs.panels, ggplot.
  • Report paired correlations for numeric data and try to visualize these (e.g., heatmap, pairs plot, etc.)
  • Examine potential dependencies of the predictors and the dependent response variables
  • Fit a Multiple Linear Regression model, report the results, and explain the summary, residuals, effect-size coefficients, and the coefficient of determination, \(R^2\)
  • Draw model diagnostic plots, at least QQ plot, residuals plot and leverage plot (half norm plot)
  • Predict outcomes for new data
  • Try to improve the model performance using step function based on AIC and BIC.
  • Fit a regression tree model and compare with OLS model.
  • Try to use M5P to improve the model.

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