Assumptions of Linear Regression Explained :- By Saurabh

-



Before Implementing a Linear Regression model,its important to go through the assumptions.As linear Regression is a parametric algorithm, It's important that the data satisfies the following assumptions So lets go through it one by one.

1.Linearity

This assumes that there is a linear relationship between the predictors (e.g independent variable) and the response variable(e.g dependent variable).
You  can use the scatter plot to detect the linearity of the variables.

2.No Outliers

There should be no outliers in the data.You can check for outliers with the help of box plot

3. No Multicollinearity

There should be no multicollinearity between the independent variables.

4. Autocorrelation

There should be no correlation between the residual (error) terms.Absence of this concept is known as autocorelation.

5. Normality

The dependant varible should be normally distributed.If not you convert it through log function.

It’s not uncommon for assumptions to be violated on real-world data, but it’s important to check them so we can either fix them and/or be aware of the flaws in the model for the presentation of the results or the decision making process.
It is dangerous to make decisions on a model that has violated assumptions because those decisions are effectively being formulated on made-up numbers. Not only that, but it also provides a false sense of security due to trying to be empirical in the decision making process. Empiricism requires due diligence, which is why these assumptions exist and are stated up front.

Comments

Post a Comment

Popular posts from this blog

Linear Regression Implementation By : Saurabh

-
Image
We discussed the Linear Regression concept in the previous blog with a Layman example. So, let's discuss the Linear Regression model and its mechanism. Linear Regression model makes use of two sets of variables:- Independent variables (termed as x ) Dependent variable(termed as y) Independent variables are the ones that are used to predict(calculate) the dependent variable. If we take into consideration an example from the previous blog, then the independent variable will be 'Kilometers' and the dependent variable will be "Amount". So in a nutshell:- You are predicting the amount based on the kilometers .i.e 'if I want travel x km then how much will it cost me (Y)?   Linear Regression can be further broken down into two types:-  Simple Linear Regression   Multiple Linear Regression   Simple Linear Regression is the one we just discussed above where we have one independent variable (km) and based on that we have to predict the dependent variable (Amount). Mu...
Read more

Machine Learning Introduction - By Saurabh

-
Image
Here we are going to discuss about the base level concept of the machine learning. There are numerous algorithms in Machine Learning But, Linear regression is the oldest ,simplest and widely used supervised learning algorithm for predictive analysis. What is Linear Regression? It’s a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable. Application in Real Life:- Say you are planning a Trip to Goa with 2 best buddies.You start off in Mumbai and you know its 12 hr drive. While your friends  are in charge of the party operations you're in charge of the logistics involved. You've to plan :the schedule,when to stop and where ,make sure you reach on time ,etc So,Whats the first thing you do?? Bring a sheet of paper and start your plan!!  First item in your checklist is Budget .  Its a 12 hr drive -approximately 600 km. the fun ride ,so it should take a total of 15 hrs on the road. The next question will be "...
Read more

Machine Learning Algorithms Explained -By Saurabh

-
Image
Welcome back on our  journey to learn the basics of Machine learning.Before having a hands on on real time data I thought making you all familiar with various types of machine learning algorithms.Let as now breakdown the classification of Machine Learning.  Let us go through each type one by one. 1. Supervised Learning :  Imagine you are a student in a class.Teacher trains you by giving you  knowledge about a subject. Supervised learning is like learning from a teacher  here the machine is you(student) and the teacher is the training data(the data   we  discussed in the previous blog, one stored in the excel file).Training data   will be used to train the   model. Supervised Learning is further divided into two types:-   Regression Classification Regression includes a scenario where machine is trained to predict some value like price ,height and weight other example will be  predict stock market prices etc.Algorithms which ca...
Read more