This article describes the analytical technique of generalized linear regression with gaussian distribution.


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 Generalized Linear Regression with Gaussian Distribution 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.

Target/dependent variable:

  • Total Profit

Predictor/independent variables:

  • Total Revenue
  • Units Sold
  • Region
  • Total Cost

Business Benefit:

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.

Target/dependent variable:

  • Chance of Admit

Predictor/independent variables:

  • CGPA
  • GRE Score
  • LOR
  • TOEFL Score

Business Benefit:

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.

About Smarten

The Smarten approach to augmented analytics and modern business intelligence focuses on the business user and provides tools for Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include Assisted Predictive ModelingSmart Data Visualization, Self-Serve Data Preparation, Clickless Analytics with natural language processing (NLP) for search analytics, Auto Insights, Key Influencer Analytics, and SnapShot monitoring and alerts. These tools are designed for business users with average skills and require no specialized knowledge of statistical analysis or support from IT or data scientists. Businesses can advance Citizen Data Scientist initiatives with in-person and online workshops and self-paced eLearning courses designed to introduce users and businesses to the concept, illustrate the benefits and provide introductory training on analytical concepts and the Citizen Data Scientist role.

The Smarten approach to data discovery is designed as an augmented analytics solution to serve business users. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.