# What Is A Regression Analysis? Important Benefits of Using It

You probably have heard about the term regression analysis during your higher studies. Do you ever think about what regression analysis is? If not, don’t do anywhere and give this article a fine read. I will discuss the regression analysis, its types, and the advantages of using it.

So, what is regression analysis? Regression analysis is a type of data analysis used in dissertation writing. It is a reliable method of identifying which variables affect your topic of interest. The process allows you to identify which variables affect the topic most and which don’t. The researcher can ignore the factors that don’t affect the study issue. It also tells the researcher about the relationship between the factors and how they influence each other. In regression analysis, there are two types of variables. A brief description of the variables is as follows;
• Dependent variables: Dependent variable is the main variable. It is the variable that you are trying to understand through this analysis.
• Independent variables: This variable is independent of any impact. In fact, it has an impact on your dependent variable.
Let’s take an example. A décor company decorated a Mehdi event in a wedding. The company wants customer satisfaction. In this example, customer satisfaction is a dependent variable. On the other hand, décor stuff is an independent variable.

## Types of Regression Analysis

There are many types of regression analysis, but two of them are famous. A brief description of those two types is as follows;

### Linear regression

The first common type of regression analysis is simple regression. In simple regression, the number of dependent and independent variables depends on whether it’s linear or multiple. The researchers use this regression mainly for predictive analysis. The simple regression model has two further types. The linear regression is the simple slope of a line. The types of simple regression are;

#### Simple linear regression

The number of dependents and independent variables is one in simple linear regression. It has only one x and y variable.

#### Multiple linear regression

In multiple linear regression, the number of independent variables is more than two. It will have on y and two or more x variables.

### Multiple regression

The researchers use this type of regression analysis for curvilinear data. The polynomial regression is considered the special case of the multiple regression model. The least-squares method is used to solve polynomial regression. In this regression, the researcher models the value of the dependent variable y based on the value of x. The number of dependent variables will be two or more than two.

There are many advantages of regression analysis. Some of the most prominent advantages are as follows;

### Making Predictions and Forecasts

Forecasting the future is the most common application of regression analysis. The model studies dependent variables in terms of independent variables and makes predictions.
Let’s take an example. The juice sales of Richard Juice Corner were very low in the winter. Now they run a regression analysis. They kept juice sales as a dependent variable and temperature as an independent variable. After the analysis, they found out that juice sales will be greater in summer. This is the way businesses use regression analysis to predict their sales.

### Improve Efficiency

The next advantage of using regression analysis is that it improves the efficiency of your business. The businesses will know more about their insights than ever. Therefore, that knowledge will help business owners to make effective policies.

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Let’s explain it with an example. After the regression model, the manager of the Richard Juice Corner now knows more about the economic order quantity. He knows that sales will be high in summer. Hence, he will take action according to the weather rather blindly. He may schedule work hours for employees. As in winter, the sales are low. He might reduce the working hours of employees.