A predictive modelling named Regression Analysis is used to determine the relationship between independent variable (predictor) and a dependent variable (target). Regression Analysis techniques are used in several fields like modelling, weather forecasting, time series etc. With this, you can also find casual effect relationship between these two variables. For instance, the number of road accident done by rash drivers is best done with the regression. There are different types of regression techniques which you must know about. Read this blog and know about types of regression techniques. Other than that, you can hire regression analysis assignment help.

Types of Regression Analysis

Prediction can be done from various types of regression techniques. Three metrics are there which drive these techniques, that are the type of dependent variables, number of independent variables, and shape of regression line.

 

Let’s explore these regression techniques one by one:

Linear Regression

This technique is widely used if you are going to opt for a predictive model. In this regression technique independent variable(s) is considered as discrete or continuous where the dependent variable is always continuous. In this way, the regression line is linear.

Through Linear regression, a linear relationship is formed between Independent variables or X and a dependent variable, which is symbolised as X. It can be shown with a straight line.

Equation of Linear equation is Written like this: Y= a+b*X+e

Where a -> intercept, b-> slope, e-> error term.

Most Important Points of Linear Regression:

  • The linear relationship must be formed between the dependent and independent variables.
  • Multiple regression has to face severe suffers like heteroscedasticity, autocorrelation, multicollinearity.
  • This regression technique sensitive to Outliers. It can affect forecast values due to regression line.
  • If you have multiple independent variables, then you can go with backward elimination, step-wise approach, forward selection for choosing the necessary independent variable. If you need assignment and essay writing help, you can hire the best academic company.

Logistic Regression

If you want to find the success of event whether it is successful or not, can be determined with logistic regression. Logistic regression is taken as binary- Yes/No, 0/1, True/False etc. Value ranges from 0 to 1. See a representation of the equation:

Here we are working on binomial distribution which is a dependent variable. Select link function which benefits the most binomial distribution.

Important Points of Logistic regression are as follows:

  • For classification problems, logistics regression is widely used
  • In this regression techniques, there is no need to find the relationship between the independent and dependent variables. You can apply non-linear log transformation.
  • For avoiding underfitting and over fitting, you can choose all the necessary variables. The step wise method is the best approach for these practices as you can easily estimate values on logistic regression.
  • Ordinal logistic regression is used when you have an ordinal value of dependent variables.
  • Multinational Logistic regression is used when there is a multi-class dependent variable.
  • In this regression technique, the independent variable is not interlinked and correlated with each other. In short, you can say there is no multidisciplinary. For more details, you can hire creative thinking help.

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