Before going into the linear regression model, let me ask you a question? Wondering what would be that? The question is obviously simple, “What is regression?”

Regression is the statistical method that is more popular in finance, investing, and other disciplines that determine the strength and characteristics of data where one is the series of the dependent variables and the other one depends on one independent variable. That’s it. All you have to answer is this when someone asks you what is regression the next time.

Linear regression is a popular method for finding the linear relationship between target variables and one or more dependent variables. There are two types of linear regression - single and multiple. You can use this process depending upon your requirements.

Simple linear regression is useful for finding the relationship between two continuous variables, you can name them as (x) and (y) respectively. Where one is a predictor or an independent variable. Whereas, the other one is a response or dependent variable (to find out the statistical relationship) and not a deterministic relationship.

Wondering, what is a deterministic relationship?

We call a relationship between two variables a deterministic relationship if one of the variables accurately expresses the other one. Whereas, the basic difference between statistics and deterministic variables is that statistics variables are not accurate. Hence, you can use them, but you cannot determine the accurate relationships between one another.

Why is regression analysis so important in data science?

The core idea of using regression analysis is to obtain a line that best fits the data. When it comes to the best fit lines, the main objectives behind this are that your dependent and independent variables should draw a line, where the error should be near to zero or mostly negligible. Error is the possible distance between the points to the regression lines.

Process in Regression analysis are:

• Finding coefficient and normal equation

• Optimization using gradient descent

• Residual Analysis

Metrics for Model evaluation

R Squared Value

Its value ranges between 0 and 1. Where 1 indicates the predictor is suitable for all kinds of variables, whereas, the value with 0 symbolizes, there is no variation present in the predictor.

The various value under R Squared Value are:

• Regression Sum of the square (RSS)

• Sum of square error (SSE)

• Total sums of Square (SSTO)

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