# Linear Regression

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

# Uses of Regression :

- Determining the strength of predictors
- Forecasting an effect
- Trend forecasting

# Difference between Linear Regression and Logistic Regression :

# Selection Criteria:

- Classification and Regression Capabilities.
- Data Quality
- Computational Complexity
- Classification and Regression Capabilities
- Comprehensible and transparent

# Where is it used:

- Evaluating trends and sales estimates
- Analyzing the Impact of Price Changes
- 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.