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Introduction of Regression in Data Science

Introduction 

Regression Analysis is the study of relationships between variables
  • Going beyond categorical variables
  • Model based relationship (linear and non-linear)
  • Useful towards interpretation and prediction

Examples :-
  • How do wages of employees get affected with experience, education, Promotions, etc.
  • How does the current price of stock depends on its past values
  • How does sales revenue get affected as a function advertising expenses, competitors advertisements etc.
  • Relationships between speed and fuel efficiency of a car.
  • How does the price of a house get affected by number of bedrooms, square footage, etc.

Categorizations nomenclature and concepts
  • Linear versus non-linear
  • Simple versus multiple
  • Cross sectional versus time series
  • Response (or Dependent) variables
  • Explanatory (or Independent) variables
  • Scatter plots and outliers
  • Unequal variance
  • Corelations (a quantitative indicator of linear relationship) and R square

Fitting Lines
  • Role of descriptive and inferential statistics 
  • y = mx + c or y = bo + b1
  • Optimization to identify the best fit 
  • Inference on the individual parameters
             - The test for the H00 = 0
  • Inference for the overall model
             - ANOVA of a different kind


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