Linear Regression

Regression analysis is a form of predictive modelling technique that investigates the relationship between a dependent and independent variable

Uses of Regression :

1. Determining the strength of predictors
2. Forecasting an effect
3. Trend forecasting

Difference between Linear Regression and Logistic Regression :

Selection Criteria:

1. Classification and Regression Capabilities.
2. Data Quality
3. Computational Complexity
4. Classification and Regression Capabilities
5. Comprehensible and transparent

Where is it used:

1. Evaluating trends and sales estimates
2. Analyzing the Impact of Price Changes
3. Assessment of risk in financial services and insurance domain

Understanding it:

y = mx + c

where y is the dependent variable and x is the dependent variable

So, this method tries to plot a linear graph that is most fit for the model.

R-Square value:

It is a statistical measure of how close the data are to the fitted regression line.

Also called Coefficient of determination, or the coefficient of multiple determination

As the value gets close to 1, the better the model becomes.