Sibashis Chakraborty
• Male
• Kolkata, West Bengal
• India

# Sibashis Chakraborty's Page

## Latest Activity

"Hemanth,  Can you please elaborate the problem a little more? I am assuming you have some historical data, based on which you are training your model. Here your independent is Sales and dependent is Investment. Now, you want to predict (not…"
Oct 25, 2019
Oct 22, 2019
Sibashis Chakraborty's blog post was featured

### A short introduction to Log Models

Why do we take logs of variable in Regression analysis?We should remember that a regression equation has two partsi) The Dependent variable (Predictand)ii) The Independent variables (Predictors) ; which can be one or more and can be of different types (Categorical or Continuous).The nature of the regression that we should run depends on the type of Dependent variable that we are dealing with in our model. For example, if the dependent variable is Continuous then we might run OLS (though this…See More
Oct 20, 2019
Sibashis Chakraborty updated their profile
Oct 20, 2019

## Profile Information

Short Bio
M.Sc(Economics)from University of Calcutta, ACET qualified, Analytics Professional, currently working as a Senior Analyst in Data Science. Experienced Marketing Mix Modeller
My Web Site Or LinkedIn Profile
Field of Expertise
Data Science, Machine Learning
Professional Status
Technical
Years of Experience:
2
Tiger Analytics
Industry:
Analytics
Senior Analyst
How did you find out about DataScienceCentral?
Article
Interests:
Networking, Finding a new position

## Sibashis Chakraborty's Blog

### A short introduction to Log Models

Posted on October 20, 2019 at 8:57am

Why do we take logs of variable in Regression analysis?

We should remember that a regression equation has two parts

i) The Dependent variable (Predictand)

ii) The Independent variables (Predictors) ; which can be one or more and can be of different types (Categorical or Continuous).

The nature of the regression that we should run depends on the type of Dependent variable that we are dealing with in our model. For example, if the dependent variable is Continuous…

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