Background

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).

We'll be exploring:

• 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

Following a step-by-step approach to understand individual concepts then wrapping it up with an attempt to automate parts of EDA process!

Table of contents

• 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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