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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
• 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 :Î²0 = 0
• Inference for the overall model
- ANOVA of a different kind