Data modeling and analysis frequently use regression analysis. This statistical approach is used in the fields of finance, investment, and other areas to assess the nature and strength of the connection between two given variables. This blog will walk you through the concept of regression analysis and its applications in real life, including calculations and examples.
What Is Regression Analysis?
Regression analysis is the most popular statistical method for determining or estimating the relationship between a dependent variable and one or a group of independent variables. Most survey analysts use regression analysis to comprehend how the given variables are related, which can then be used to forecast the precise result.
For example, a paint company wanted to understand the challenges leading to brand marketing. The survey was the most effective way to contact both current and potential clients. A thorough questionnaire was created with the aid of a survey tool for a massive consumer survey. Several questions related to the brand, impact, vision, and culture were effectively asked in the survey. After receiving responses to the survey, regression analysis was used to narrow down the top ten factors responsible for challenges in brand marketing. All the attributes derived (mentioned in the image below) highlighted the challenges in brand marketing.
Usability of regression analysis
Numerous practical uses can be made of regression analysis. It is crucial for any machine learning issue involving continuous numbers. Its uses include but are not restricted to the following:
- Forecasting financial data (like house price estimates or stock prices)
- Predicting sales and promotions
- testing vehicles
- Forecasting and analyzing weather
- Forecasting time series
Regression analysis can provide particular information on a relationship between two or more variables and indicate if it is significant. It can estimate the magnitude of an independent factor’s effect on a dependent variable. Regression analysis should be able to tell you what impact changing the value of one variable (like price) will have on the dependent variable (like sales).
Regression analysis is a statistical tool that businesses may use to test the impact of variables on several scales. They can evaluate the ideal combination of factors to incorporate when creating predictive models, considerably improving forecasting accuracy.
Finally, regression analysis is the best method for utilizing data modeling to solve regression problems in machine learning. Businesses may forecast each data point’s chances of error by putting the data points on a chart and then drawing the best fit line through them; the farther away from the line the data points are, the greater their error of prediction (this best fit line is also known as a regression line).
Examples
Let’s say a frozen food company wants to relocate its manufacturing facility. Before moving forward, the business wants to examine its revenue generation model and all the potential influences. As a result, the business performs an online survey using a certain questionnaire. The responses from the survey will help analyze the revenue generation model. Accordingly, the company can decide whether to relocate or not and how to proceed with the relocation.
Consider another example. Suppose a beauty salon manager believes extending the closing time will increase its customers and sales. On the contrary, according to regression analysis, the extra revenue brought on by higher sales won’t be enough to cover the increased operating costs brought on by longer working hours.
Regression analysis makes it simpler for the business to comprehend the relationships between variables, such as electricity and revenue (here, revenue is the dependent variable), and to interpret survey results. Additionally, knowing how diverse independent variables like price, labor force, and logistics relate to revenue can assist a business in evaluating the influence of various variables on its sales and profitability.
How To Calculate Regression Analysis?
Formula For Regression Analysis
Moreover, regression analysis is usually conducted in spreadsheet programs like MS Excel and Google Sheets as it involves complex calculations.
Calculation For Regression Analysis — Example
Let’s say a bank decides to link the interest rate on savings accounts to the repo rate. The bank’s auditor now wants to perform an unbiased study of the decisions made by the bank regarding interest rate changes and whether the Repo rate has changed due to the new changes.
The bank’s auditor uses the regression method to analyze whether the bank’s rate changed when the Repo rate did.
With n = 6, we have all the values in the aforementioned table. So, first, determine the regression’s intercept and slope first. The intercept is calculated as follows:
a = (24.17 x 237.69) – (37.75*152.06) / 6 x 237.69 – (37.75)2 = 4.28
The slope is calculated as follows:
b = (6 x 152.06) – (37.75 x 24.17) / 6 x 237.69 – (37.75)2 = (-0.04)
Now, put the values in the formula to obtain the final figure.
Hence, the regression line Y = 4.28 – 0.04 x X
As a result, the slope value indicates a relationship between the repo rate and the bank’s saving account rate. Therefore, it looks like the bank is indeed adhering to the policy of linking its saving rate to the repo rate.
Financial Takeaways
Regression analysis is significant since it revolves around data, specifically the numbers and statistics that actually characterize a business. Businesses can essentially crunch the numbers with regression analysis to make better decisions for their organization now and in the future.
FAQs
Is regression analysis qualitative or quantitative?
Regression analysis is a quantitative research method.
Is regression analysis machine learning?
Yes, regression analysis is among the most fundamental tools of machine learning used for prediction.
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