There are several students who do not know about what is regression in statistics as it is used to find out the relationship between dependent variables and independent variables. Using these variables, the analyst can forecast about various things, such as sales production and other factors that are beneficial for small as well as for the large scale businesses.
Therefore, this blog will help you to understand the concept of what is regression in statistics; besides this, it will provide the information on types of regression, important of it, and finally, how one can use regression analysis in forecasting. So, before proceeding to its beneficial uses and types, let’s get details on the meaning of regression.
What is regression in statistics?
Regression is one of the branches of the statistics subject that is essential for predicting the analytical data of finance, investments, and other discipline. It is also used to calculate the character and strength of the connection between the dependent variables with a single or more series of predicting variables. The main objective of the regression is to fit the given data in a meaningful way that they must exist in minimum outliers.
Regression is the supervised machine learning and statistical method and an integral section of predictive models. In other words, regression means a curve or a line that passes through the required data points of X-Y plot in a unique way that the distance between the vertical line and all the data points is considered to be minimum. The distance between these x and y points and the lines specifies whether the sample has a strong connection, and then it is called a correction.
What is the importance of regression analysis?
As we are well-versed with the term what is regression in statistics which is all about information: information means figures and numbers which can define one’s business. There are several advantages of these analyses, such as they can allow you to make better decisions that are beneficial for your businesses. Various methods are studied out to forecast the relationship between the data points that are essential for:
- Prediction of the sales in the long term.
- Understand demand and supply.
- Inventory groups and levels understanding.
- Understand and review the process of different variables effects all these things.
There are several companies that are using regression analysis to get to know about:
- Forecast what sales can be beneficial for the next six months.
- Is there any need to expand the businesses or produce and market the new products.
- Why client services call a decline in the past years or in the last month.
- Which marketing promotion should use over another.
The advantage of using the regression analysis is that one can use this to know about all types of trends that are generating in data. The new methods are valuable for understanding what can help you to create a difference in the businesses. As you have the idea about what is regression in statistics and what its importance is, now let’s move to its types.
Types of regression analysis
Basically, there are two kinds of regression that are simple linear regression and multiple linear regression, and for analyzing more complex data, the non-linear regression method is used. Simple linear regression is used to predict or explain the result of the dependent variable using the independent variable, whereas multiple regression analysis is used to explain more than two variables result.
As we have already mentioned, a regression can help professionals to invest and finance in their businesses by predicting their sales value. Regression can predict the sales of the companies on the basis of previous sales, weather, GDP growth, and other kinds of conditions. The general formula of these two kinds of regression is:
- Simple linear regression: Y = a + bX + u
- Multiple linear regression: Y = a + b1X1 + b2X2 + b3X3 + … + btXt + u
- Y = the variable which is trying to forecast (dependent variable).
- X = the variable which is using to forecast Y (independent variable).
- a = the intercept.
- b = the slope.
- u = the regression residual.
Regression focuses on a set of random variables and tries to explain and analyze the mathematical connection between those variables. This connection is in the straight line (linear regression), which is best to estimate a single data point. But, for multiple regression, the different variables are used with subscripts.
A real-world example of what is regression in statistics
Regression is mostly used for determining the several parameters, like interest rate, sectors influence of an asset, cost of a commodity, or specific industries. The CAPM is used to highlight the expected stock returns and to produce capital’s costs. The return of stocks can be regressed to create a beta for a specific stock against the broader index’s returns, like the S&P 500.
For the risk of a stock, beta is used to represent the relation to the index or market, and it reflects the slope in the CAPM samples. The stock’s return might be the dependent variable Y; besides this, the independent variable X can be used to explain the market risk premium. There are several additional variables, like the valuation ratios, the market capitalization of the stocks, and the return would be sum up to the CAPM samples that can estimate the better results for the returns. These additional parameters are called as the Fama-French factors that are named after the developer of the multiple linear regression sample for better explanation asset returns.
This blog has provided all the information about what is regression in statistics. Regression analysis is the mathematical method that is used to sort out the impact of the variables. There is a huge importance of the regression analysis for large as well small businesses that helps to recognize the parameters that matter most to enhance the sales and which factor is to be ignored. Regression analysis offers a statistical method that is used to examine the connection between two or more variables.
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Some more questions about regression in statistics
Regression is one of the branches of the statistics subject that is essential for predicting the analytical data of finance, investments, and other discipline. It is also used to calculate the character and strength of the connection between the dependent variables with a single or more series of predicting variables.
Prediction of the sales in the long term.
Understand demand and supply.
Inventory groups and levels understanding.
Understand and review the process of different variables effects all these things.
Simple linear regression
Multiple linear regression