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.
How Can Random Forest Classification Be Helpful for Business Analysis?
If we consider the use cases below, we can see the value of Generalized Linear Regression with gaussian distribution analysis.
Business Use Case 1
Business Problem: Product’s Profit Prediction
Identifying the profit made by each product based upon various factors like its total revenue, number of units sold, region of sale etc.
- Total Profit
- Total Revenue
- Units Sold
- Total Cost
The predictive model will help us identify, profit on different products based on the sales, region and other cost factors.
Business Use Case 2
Business Problem: Student’s Chance Of Admission Prediction
To determine a student’s chance to get admission based on certain educational scores and factors.
- Chance of Admit
- GRE Score
- TOEFL Score
Using generalized linear regression, we can determine, to what extent a person qualifies to get an admission based on various educational factors. This eases the entire process of admission and allows the most eligible students to be selected.
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