Let me get right into the subject. The picture you see above is the mathematical representation of Multiple Linear Regression. All the necessary explanation is given in the image.
As the name suggests MLR (Multiple Linear Regression) is linear combination of multiple features/variables that define the average behavior of the dependent variable.
Consider x1,x2,…xp as the independent variables and Y is the dependent variable. All the beta values correspond to the coefficients for respective independent variables. Beta0 on the other hand is the intercept values which is similar to Simple Linear Regression.
What’s the error term ?
It is an error that is there in the nature. Remember in my previous article I specified one can never predict the exact future value ? that is due to the fact that this error is present. Error consists of all the data that is not recorded/ used in our model. Such as emotions, feelings etc that can not be easily quantifiable or simply the lack of data/human errors in recording the data.
But don’t worry about it. Once we use this regression method we only get the average behavior of the Y variable. This average behavior when compared to the actual data, might be greater than, less than or equal to the original predictions of Y. Since we deal with only the average of Y the error terms cancel out each other and we have an estimated regression function in the end with no error term.
How to decrease error ? Simple, invest more money and find more data.
Let us consider a firm’s profit as the dependent variable (Y) and it’s spending in RnD (x1), Advertising (x2) and Marketing (x3) be our independent variables.Let’s say after doing all the math we come up with the below regression equation or function [the other name for the mathematical representation of any regression]. Please bear in mind that the below function is hypothetical.
Profit = 10000 + 5(RnD) - 2(Advertising) + 1(Marketing)
How are these estimates calculated ?
There is a mathematical method called OLS (Ordinary Least Squares) method. Using certain matrix transformations you can find the estimated coefficient values. Explaining OLS is not in the scope of this article. You can easily find tutorials online regarding the same, kindly go through them if you really want to know how it works. However, modern programming languages will help you in computing those estimates for you.
Let us deep dive into python and build a MLR model and try to predict the points scored by basketball players.
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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