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Regression analysis is one of the most essential parts of the statistics course. With the help of this analysis, you will be able to find out the relationship between the predictor variables and response variables. It is also very famous as the technique for predicting the time series modeling and the effects and cause of the connection of two variables. If you are looking for a Regression Analysis assignment writing service, approach our team today.

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There are different types of regression analysis; you already know about them. It is a plus point for you, but that student who does not have much information about this for them our Regression Analysis homework help exports have jotted the types of regression analysis. You can give a quick revision here to recall the types -

Linear regression is the most used technique. The dependent variables are continuous, and three independent variables are discrete, making the nature of the regression line in linear regression. In this type of regression, you will find two types, i.e., multiple linear and single linear regression.
Logistic regression- Logistic regression is primarily used to find out the probability of an event. When the dependent variables' nature is binary, then you can use logistic regression. It can also be used for classification problems.
Polynomial regression- when you see the variable is independent and contains powers of more than one, it is considered the polynomial regression equation.
Stepwise regression- When an individual deals with more than one independent variable at that state, stepwise regression can be used. In this process, there is no need for any human involvement in the selection of independent variables. The selection can be made automatically.
Ridge regression - In this type of analysis, the independent variables are correlated. With the help of ridge regression, you can quickly solve multicollinearity problems.
Lasso regression - This type of regression is mostly the same as ridge regression. It can minimize the variability and increase the level of accuracy of the linear regression models.
Elastic net regression - elastic net regression can be very helpful when multiple features are related to each other. It might be a little difficult to understand for the first time, but it becomes the easiest once you understand.

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Assignment-

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• London School of Economics and Political Science
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• University of Leeds
• University of Edinburgh

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