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 + b1x 
• Optimization to identify the best fit 
• Inference on the individual parameters

             - The test for the H0 :β0 = 0

• Inference for the overall model

             - ANOVA of a different kind