Understanding Linear Regression
In my recent post on my blog, I tried to present my understanding of linear regression with charts and tables. Here's what I covered in the post.
- What is Linear Regression?
- Understanding regression visually
- How is regression line calculated?
- Assumptions around linear regression and why each one of them is important
- Things to do to make the results better
- Sample code in Python and R
What's the first thing that we can observe by just looking at this table. Is there some kind of pattern. As it turns out human mind is really good at finding hidden patterns, in some cases, better than a computer. In fact when it comes to finding patterns in images or videos, computers have still not been able to beat humans.
For those of you who are still not able to grasp it fully (even non mathematics background people would grasp is partially), let's plot the above into a graph. Does this makes it clearer?
As most of you might have thought of - for each value of X, Y equals X. In short, Y = X.
This in short is the most basic example of Linear Regression. Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables. When there are more than one independent variables, its called multivariate regression analysis.
More can be read in detail on my post. Here is the link https://analyticsbot.ml/2016/12/linear-regression-machine-learning-101/