# What Is Generalized Linear Regression with Gaussian Distribution And How Can An Enterprise Use This Technique To Analyze Data?

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What is Generalized Linear Regression with Gaussian Distribution?

The Generalized Linear Model (GLM) Regression is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. GLM with gaussian Distribution is a model with low complexity where the response variables exhibit gaussian exponential distribution form.

Generalized linear regression is limited to predicting numeric output so the dependent variable has to be numeric in nature.

To have a better understanding of this algorithm, let’s look at one such analysis on loan eligibility to identify whether or not the amount is eligible for loan application based upon various influencing factors. If we consider the use cases below, we can see the value of Generalized Linear Regression with gaussian distribution analysis.

Identifying the profit made by each product based upon various factors like its total revenue, number of units sold, region of sale etc.

Target/dependent variable:

• Total Profit

Predictor/independent variables:

• Total Revenue
• Units Sold
• Region
• Total Cost

The predictive model will help us identify, profit on different products based on the sales, region and other cost factors.

To determine a student’s chance to get admission based on certain educational scores and factors.

Target/dependent variable:

Predictor/independent variables:

• CGPA
• GRE Score
• LOR
• TOEFL Score