**Know the Principles of Regression Analysis with Basic of Regression Homework Help**

The regression analysis is a technique to estimate the relationship between the variables, i.e. the dependent and independent variables. There can be one or more independent variables present which can also be known as predictors. It shows the change that takes place in the dependent variable when there is a no change or a simple shift in a single variable. Even if other independent variables remain fixed, the dependent variable still shows a change.

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**Importance of Regression**

There are many ways in which regression analysis is necessary. You will find more details about it in **the basics of regression assignment help**. However, let’s check some of its uses.

- In the field of machine learning, you can use it to predict and forecast the results.
- It also shows the significant relationships between the variables.
- It can illustrate how a particular independent variable relates to the dependent variable.
- It indicates how the independent variables have an impact on the dependent variable.
- It can also mean the price change and promotional activities which are helpful for the market researchers and data scientists.

**Types**

There are different types of regression analysis. However, the factors that result in the estimation are as follows:

- Shape of regression
- Type of dependent variable
- Number of independent variables

For more information, visit myhomeworkhelp.com to receive **the basics of regression homework help**. The types are:

**Linear**

It is a traditional modeling method. Here, the dependent variable is continuous while the independent variables can be either continuous or discrete. The regression line is linear.

**Ridge**

You can use this when the data is multi collinear. It solves the problem by using a shrinkage parameter.

**Polynomial**

It follows an equation where the power of independent variable is more than one. Here, the regression line is a curve.

**Step wise**

You can use this type of technique when there are many independent variables. You can also handle higher dimensional data sets. Here, you have to maximize the power with less number of predictors.

**Logistic**

You can use this technique to determine the probability of success and failure for an event.

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