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What is Linear Regression?

Linear Regression refers to an algorithm for machine learning which is designed based on supervised learning. It is developed for performing regression tasks. The analysis of this machine learning algorithm is performed for predicting the value of a variable that is based on the value of another variable

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The variable that needs to be predicted is known as a dependent variable and the variable with which the value of another variable is predicted is termed an independent variable. The models of linear regression are mainly used to find out the connection between forecasting and variable. Find Linear Regression Analysts and Hire freelancers in the UK, India, USA, Canada, and AUS on the world's top freelancing website.

Linear Regression is a simple and common type of analysis based on prediction. The entire analysis is conducted to find out two major things:

  • Whether predictor variables are good enough to predict a dependent variable or not
  • Which variables are most important to dependent variables and in which way do they impact the dependent variable?

The simplest structure of the linear regression equation is presented with the formula y = c + b*x

Where, y = score of the predicted dependent variable 

c = constant

b = coefficient of regression

x = independent variable score.

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Types of Linear Regression

Following are the important types of Linear Regression that are briefly mentioned below:

  • Simple linear regression: 1 dependent variable (ratio or interval) and 1 independent variable (ratio or interval or dichotomous).
  • Multiple linear regression: 1 dependent variable (ratio or interval) and 2+ independent variables (ratio or interval or dichotomous).
  • Logistic regression: 2+ independent variables (ratio or interval or dichotomous) and 1 dependent variable (dichotomous).
  • Ordinal regression: 1+ independent variable(s) (dichotomous or nominal) and 1 dependent variable (ordinal).
  • Multinomial regression: 1+ independent variable(s) ratio or interval or dichotomous) and 1 dependent variable (nominal),
  • Discriminant analysis: 1+ independent variable(s) (ratio or interval) and 1 dependent variable (nominal).    

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Importance of Linear Regression 

Models of linear regression are simple and offer a clear formula of mathematics that helps generate predictions. It can be applied to multiple areas of academic study and business. Complete research into what people know about a subject is what academic research involves. Hire academic researchers from Paperub. Moreover, it is also used in environmental, social, behavioral, biological sciences, and business. It has become an important and proven way to predict the future reliably and scientifically. Because it is a long-established procedure of statistics and the properties of the models are properly defined and trained more easily.    

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Advantages of Linear Regression

Various advantages can be gained by using linear regression but the important ones are briefly discussed below:

  • Simple implementation: It is considered a simple algorithm that can be implemented very quickly for a satisfactory outcome. Moreover, the models of this machine learning algorithm can be easily and efficiently trained even on systems that have very low power of computation in comparison to other algorithms. It has a significantly lower time involvement compared to other algorithms of machine learning. The mathematical equations included in this algorithm are relatively easy to interpret and understand. So, it can be said that this is very simple and convenient to master.
  • Performance on linearly divisible datasets: This algorithm fits perfectly in linearly divisible datasets and is extremely beneficial for finding out the relation between variables. 
  • Overfitting can be reduced by regularization: Overfitting refers to a situation when a model of machine learning fits closely to a set of data and also apprehends the noisy data. This negatively affects the model performance and decreases the precision of a set of tests. On the other hand, regularization refers to a technique or procedure that can be implemented easily and it can reduce the complexity of a function effectively so that the risk of overfitting gets reduced.

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