We've previously discussed the inner working of Ordinary least squares regression here, it's time to focus on statistical analysis and uncovering patterns using The General Linear Model (GLM).

• Practical application of GLM covering ANOVA and Post hoc analysis
• Importance of explanatory data analysis (EDA) in understanding data, uncovering hidden patterns and drawing correct conclusions for modeling
• Pitfalls leading to wrong conclusions

• Exploratory Data Analysis (EDA)
• Univariate Analysis
• Bivariate analysis - Numeric vs Numeric variables
  • Correlation
  • Trend Lines
  • Residuals Homoscedasticity assumption
  • Residuals Normality assumption
  • Experimenting with violation fixes
• Bivariate analysis - Categorical vs Categorical variables
• Bivariate analysis - Categorical vs numeric variables
  • One-way ANOVA
  • Multiple comparisons tests
• Multivariate analysis
• Modeling
  • Full Data
  • Model Selection
  • Smokers Group
  • Non-Smokers Group
• Modeling Summary
• Automation
  • Starting from scratch - Insurance Dataset
  • House Price Dataset

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