Regression analysis is one of the most valuable parts of statistical studies for business. Regression analysis is used to determine the relationship between independent variables and a dependent variable. Most importantly, regression analysis is used to determine the strength of the relationship between two and more variables, which is widely used in financial and business forecasting.
Regression analysis is also used to model the future association within two variables. There are several types of regression analysis, such as multiple linear, linear, and non-linear. The most commonly used are multiple linear and linear analysis.
A non-linear analysis is used to deal with complicated sets of data. In complicated data sets, people have to deal with dependent and independent variables that show a non-linear relationship. Read more about regression analysis here.
Purpose of regression analysis
There are several purposes of regression analysis. Some of the most significant ideas are listed below:
Analyze several relationships
Regression analysis is used to determine the relationships between complicated data sets.
- Model multiple independent variables
- Categorical variables and continuous variables
- Model curvatures through polynomial
Interpretation of regression output
You must have a good model before using regression analysis. After having a good model, you need to look at the p values and regression coefficients. Moreover, p-values help you determine the relationships in larger values, such as population analysis.
Have reliable regression analysis
You need to follow the next steps to have a reliable regression result.
Develop a correct model
First, you need to choose the correct model for regression analysis. Having a properly trained model lets you have biased results.
Checking of residual plots
Ensure the adequate fitting of the model with the available data.
Find a correlation
The last step is to check if the correlation between variables exists or not. There are several possibilities: the correlation between a couple of variables exists; there is no correlation. Or some variables influence another one, while others do not influence it, or they are dependent on the same variables, while we thought that they are independent. The occurrence of high intercorrelations among two or more independent variables in a multiple regression model is known as multicollinearity. Multicollinearity is okay to some extent, but excessive multicollinearity can be problematic.
The right time to use regression analysis
The least-square regressions are appropriate when we have one independent variable.
However, there are certain types of regression analysis, such as multicollinear regressions, which are difficult to manage without machine learning models.
The most common use of regression analysis for business is some forecasting of values, like determining the cost of the house based on its age, size and location, and the cost per room for rent, based on the average data for this particular location.
Some of the types of regression analysis are described below:
This regression analysis is mainly used for predictive analysis.
Linear regression focuses on the probability conditions that are according to the response of the value of predictors. Overfitting is always at risk in linear regression.
When the dependent variable is dichotomous, then we have to use logistic regression.
Polynomial regression is best used for curvilinear data. It is perfectly used for methods of least squares.
If you wish to go deeper into regression, watch this video: https://youtu.be/ZkjP5RJLQF4