Regression is the study of dependence — A Predictive modelling technique

• It attempts to find the relationship between a DEPENDENT variable “Y” and an INDEPENDENT variable “X”.
• (Note: Y should be a continuous variable while X can be categorical or continuous)
• There are two types of regression —_ Simple Linear Regression and Multiple Linear Regression._
• Simple linear regression will have one independent variable(predictor).
• Multiple linear regression will have more than one independent variable (predictors).
• In a nutshell — Linear Regression maps a continuous X to a continuous Y.

1. APPLICATIONS OF REGRESSION ANALYSIS:

1. To determine strength of independent variables (predictors)

— Example: Relationship between Age & Income

2. To forecast effects

— Example: Effect on sale income for 1000$ spent on marketing

3. To forecast trends

— Example: Predicting price of bitcoin in the next 6 months

2. KEY POINTS FOR LINEAR REGRESSION:

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3. SELECTION CRITERIA FOR LINEAR REGRESSION:

1. Classification & Regression Capabilities:

• Regression models predict continuous variables (Eg: Predict the temperature of a city)
• Once it is known that the aim is to classify data — we choose Logistic Regression.
• Linear Regression is not suitable for classification because “**the idea of fitting a straight line in case of a polynomial is a challenging task. **”

2. Data Quality:

• Each missing value removes one data point that could optimize the regression.
• In simple linear regression, the outliers can significantly disturb the outcome. (i.e. removing outliers enhances the model greatly)

3. Computational Complexity:

• It is not expensive computation-wise as compared to decision tree (or) clustering.

4. Comprehensible & Transparent:

• Easy to comprehend and understand

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