Trending Technology Machine Learning, Artificial Intelligent, Block Chain, IoT, DevOps, Data Science

Recent Post

Codecademy Code Foundations

Search This Blog

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 H0 :β0 = 0
  • Inference for the overall model
             - ANOVA of a different kind


No comments:

Post a Comment

Popular Posts