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Regression analysis is a term specifically used in the field of statistical modeling that essentially refers to the estimation of the relationship between distinct variables. The analysis refers to a statistical process that essentially incorporates a number of analyses along with modeling techniques for variables and aims to ascertain the relationship between two variables, dependent and independent. Are you studying regression analysis and on the lookout for Regression Analysis Assignment Help? Entrust livewebtutors.com to attain distinguished help from a wide array of professionals well equipped with the needed knowledge to perform the assignments at large.

Regression analysis is beneficial for determining the ways in which the value of the dependent variables alters during any variation on the independent variable offered, once the value of the independent variable is fixed. The models are made up of dependent variables, independent variables along with unknown parameters. However, the regression analysis usually involves a number of assumptions that need to be considered while using it. Let us explore and analyze the subject in depth.

## Types of regression analysis:

A number of regression analyses can be formed and here are a few as mentioned in the following types:

1. Linear Regression

This regression kind is one of the most popular ones. The linear regression has the most number of academic applications along with a number of businesses. Under the liner regression, the dependent variable is a continuous one and the predictor variable might either be continuous or discrete. It essentially establishes the relationship between the dependent variable along with the predictor one.

1. Simple linear regression

The simple linear regression essentially explores the relationship between a dependent variable and a predictor one. When it comprises of more than just one independent variable, it is better known as multiple linear regression. Opt for Regression Analysis Assignment Help now to put your stress to rest.

1. Logistic Regression

Logistic regression is known as the logit model which analyzes a dataset with the presence of one or more than one independent variable. It is beneficial for predicting binary outcomes such as true/false, or yes/or. The prediction is performed from a set of independent variables. Dummy variables are useful for representing the categorical outcome.

1. Polynomial Regression

It is a non-linear regression under a model where the relationship between dependent and predictor variables is estimated well by the nth degree of the polynomial. This type of model makes use of the least-squares method.

This refers to a statistical technique that comprises of a wide array of applications. The two of the most pivotal applications of the analysis can be referred to as forecasting and optimization. Linear regression can further predict the estimates while evaluating the trends. It is also beneficial for analyzing the sale promotion, the pricing, along the marketing effectiveness of a product.

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