Regression Analysis, a statistical technique, is used to evaluate the relationship between two or more variables. Regression analysis helps an organisation to understand what their data points represent and use them accordingly with the help of business analytical techniques in order to do better decision-making. In this analysis, you will understand how the typical value of the dependent variable changes when one of the independent variables is varied, while the other independent variables are held fixed. Business analysts and data professionals use this powerful statistical tool for removing the unwanted variables and select the important ones. There are several ways that an organisation use regression analysis and some of them are discussed below.
Organisations, in order to run smoothly as well as efficiently, need better decisions and must understand the effects of the decision taken. Organisations collect data about sales, investments, expenditures and other parameters and analyse it for improvement. The regression analysis helps the organisations to make sense of the data which is then used for gaining insights into an organisation. Business analysts and data professionals use the regression analysis to make strategic business decisions.
2| Optimisation Of Business
The motive of regression analysis is to turn the collected data into actionable insights. The organisations are adopting data-driven decision making which eliminates the old-school techniques like guesswork or assuming a hypothesis which eventually improves the performance of work in an organisation. This analysis provides practical assistance to the management unit of the organisation. With lots of data available, the data can be analysed as well as understood to gain efficient insights and work smartly.
3| Predictive Analysis
Organisations use regression analysis in order to predict future events. In this process, the business analysts predict the man of the dependent variables for given specific values of the dependent variables. The multivariate linear regression is used for various important purposes such as forecasting sale volumes or create growth plans, etc. According to this article, the general procedure for using regression in order to make good predictions are mentioned below:
- Research the subject-area so that you can build on the work of others. This research with the subsequent steps.
- Collect data for the relevant variables.
- Specify and assess your regression model.
- If you have a model that adequately fits the data, use it to make predictions.
4| Risk Analysis
Most of the time while analysis data, the analyst make mistakes and made a confusion between correlation and causation. It is important to note and understand that correlation is not causation. The regression analysis is used by the organisations to assess the risk in the financial domain and other such domains and thus guide to make crucial business decisions. The linear regression analysis developed the capital asset pricing model which helps in taking financial decisions.
5| Understand The Failures
Besides the analysis of data, regression analysis also helps an organisation to understand any failure and learns from it to correct in the future analysis. Thee regression analysis basically provides quantitative support for the decision-making process. Predicting success can be said as one of the main assets of regression analysis such as analysing the data points of previous sales data as well as current sales data in an organisation to understand and predict the future success.
In pharmaceutical companies, regression analysis is used to analyse the quantitative stability data for the retest period or estimation of shelf life. In this approach, the nature of the relationship between an attribute and time determines whether the data should be transformed for linear regression analysis or non-linear regression analysis.
Simple linear regression is also known as Ordinary Least Squares (OLS) provides an overall rationale for the placing of the line of the best fit among the data points which are being studied. This tool is commonly used in forecasting and financial analysis. Another application such as the statistical method is fundamental to the Capital Asset Pricing Model (CAPM) which describes the relationship between the expected return and risk of investing in a security.
In the credit card company, regression analysis helps in understanding various factors like customer’s risk of credit default, expected consumer behaviour, prediction of credit balance, etc. and based on these results the company implements specific EMI options while minimising the default among risky customers.
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