Introduction
What is regression analysis? Regression analysis is a statistical process that estimates the relationship between the dependent and the independent variables. The common one is linear regression because it usually fits appropriately with the specification according to the mathematics criteria.
Regression analysis is mainly used for two purposes that are prediction and forecasting and the second one is the substantial overlap along with machine learning. In some situations, it is seen that it is also used in the relationship between the independent and the dependent variables. It is very important to carefully justify the existing relationship or the relationship between the two variables.
The relationship between the variable holds an important role especially when the estimation is to be carried out for a casual relationship.
Regression analysis is considered a very reliable method to know that which of the variables have the impact on the particular topic. The process helps to know the factors that have to be given importance and those factors that can be ignored.
The main important types of regression analysis
The three common regression analyses are;
Linear regression– This is the process of finding the straight line with a set of points on the graph.
The Logistic regression– This is a regression where the regression uses the logistic function in its basic form.
The Multiple Regression-This is used to analyse the relationship between the single dependent variable and various independent variables.
To know exactly why it is important to perform the regression the following points have to be clearly understood.
- One of the main factors to understand first is the dependent variable.
- The second factor is the independent variables that have percussion on the dependent variable.
Working of the regression analysis.
To conduct regression analysis it is very important to define the dependent variable that contemplates various independent variables. There is a requirement to establish you’ll then need to establish an encyclopedic to work with the dataset. This should include all the questions connected with the independent variables.
More about the regression analysis
Regression analysis is very useful for any organisation which helps them to understand the representation of the data and can be used accordingly with many analytical techniques which further help in making appropriate decisions.
The analysis will help to understand the value of the change in the dependent variable when there is a variation in one of the independent variables. The data professionals usually use this tool to eradicate the unwanted variables and at the same time, the important variables are selected.
Some of the methods where an organisation uses the regression analysis is seen below;
The main purpose of the regression analysis is the description, estimation, prediction and control. In the description, it helps to know the relationship between the dependent and the independent variables. Estimation helps to estimate the value of the dependent variable by using the values of the independent variables while prediction helps to know the outcome of the changes in the dependent variables and helps in controlling the effect of the independent variable.
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Is one of the important factors for any organisation to run smoothly and in an efficient way. What the organization exactly does is that they collect data from all the sections analyse the same and see all the ways to improve the sectors which need further improvement. This also helps the performance of the organisation drastically. With so much data, any organisation can take advantage and work effort efficiently and smartly.
Regression analysis helps in the research so that a proper work atmosphere can be developed in an organisation and helps in the collection of data for different variables.
In many organisations, it is very important to avoid any critical mistakes which further can lead to many damages.
The analysis helps in analysing the risk factors thus helping the organisations to access the risk in financial domains and helps to take accurate decisions. Regression analysis also helps the organisation to know the drawbacks and also helps in learning for the future. Regression analysis helps with the past and the future data which is useful to predict upcoming ventures.
Uses of regression analysis in some organisations
- It is well used in pharmaceutical companies to analyse the data for the estimated period.
- Uses in the financial organisation especially in the credit card company where it helps in understanding the facts like the customer’s credit default and many other various factors which helps in avoiding any kind of loss to the organisation.
We can define linear regression analysis as the relationship between the single dependents and independent variables. In the linear regression analysis, the symbol ε signifies that the independent variable cannot predict any change in the dependent variable.
The factors that mainly affect the linear regression are the size of the sample, missing data and the nature of the sample.
Sample size specifies connections with the strong relationship variables only hence the sample size has to be selected based on the number of the independent variables.
The missing values will have an effect on the sample size. Therefore it becomes very important to accurately deal with all the mission values before processing the regression analysis.
It is also important to take the fact that the subsamples have to be predefined. If it is not done so then the analysis will be done for the whole sample.
Conclusion
Regression is the process that helps the organisation to predict and estimate and helps to make better decisions. It helps in the relationship between the specified variables. While performing the regression analysis it is very important to know the type and the number of dependent and the independent variables and the size and the nature of the sample.
