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Regression is a statistical method of determining the relationship between two or more variables. Here one variable is dependent and other variables are independent. A change in dependent variable is linked with and depends on a change in one or more independent variables. Investors and financial managers are benefited by this as it helps them to value assets and understand the relationship between variables. It can help in prediction of sales for a company based on weather, GDP growth or other factors. Regression can be analysed by Regression Assignment Help.
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Regression Forms for Assignment help
There are many forms of regression. Each form has its own importance and is performed where they are best suited. It is explained by Regression Assignment Help.
1.Linear Regression: – It is one of the most basic types of regression. It is one of the sought topics which people pick while studying predictive modelling. In this method, the dependent variable is continuous while the independent variable or variables can be continuous or discrete. The nature of this kind of regression is linear and hence it is termed as linear regression.
2.Logistic Regression: – It is used to find the probability of event (Success and Failure). This type of regression method is used when the dependent variable is binary (0/1, Yes/No, True/False) in nature. It requires large sample sizes and the independent variable should not be correlated with each other. Linear relationship between dependent and independent variables is not required.
3.Polynomial Regression: – A regression equation is said to be polynomial regression equation if the power of independent variable is more than 1. The best fit line is not a straight line. In this technique, the best fit line that fits into the data points is a curve.
4.Ridge Regression: – It is a regression method used when there is multi-collinearity (independent variables are highly correlated with each other). This technique uses regularization.
5.Step wise Regression: – It is the form of regression which is used when there are multiple independent variables. In this method, the selection of independent variables is done with the help of an automatic process. Human intervention is not involved in this process. Standard step wise regression performs addition and removal of predictors as required for each step.
6.Lasso Regression: – This technique is similar to Ridge Regression. The abbreviated form of Lasso is Least Absolute Shrinkage and Selection Operator. It reduces the variability and improves the accuracy of linear regression models. There is one difference between lasso regression and ridge regression, lasso uses absolute values in the penalty function whereas ridge uses squares
7.Elastic Net Regression: – It is a hybrid of Lasso and Ridge regression method. It is used when multiple features are correlated. In this method, there are no limitations on the number of selected variables.
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