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You know the importance of data-driven decisions in business. Data gathered from authentic and relevant sources backs up the strategies you make at your workplace.
But how do you dissect the data which you have collected?
You don’t have to take the help of your math book, so that’s a plus. But you need to know how you can interpret and understand the data to use them properly.
This need for data analysis brings us to Regression Analysis, a statistical approach to make sense of the data at your disposal.
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Regression analysis is a statistical model that examines the relationship between dependent and independent variables. In other words, it helps understand the impact the independent variable has on the dependent variable.
Regression analysis provides insight into what the data represents. This helps organizations learn how to use the data to make better decisions for their business strategies.
Let’s say, you own a clothing company. You want to predict how much sales you will make in the coming winter. There are many factors that can influence the sales, from your latest commercial to competitor’s promotion, snowfall to festivals.
Regression analysis can help you sort through all these factors to identify the factors that have a definite impact on your sales. It can help you avoid the variables that won’t help your business focus and highlight those which are important.
It answers questions that you may have like: What factors influence my sales? Which ones can I ignore? What is the relation between these factors? How do the factors interact?
In the context of business analytics, regression analysis can help you focus on important and impactful variables and remove unwanted variables. This way you can keep your strategies on track and aligned with your business interest.
You will be coming across two terms – Dependent variable and Independent variable – a lot of times when discussing Regression Analysis.
Dependent variable is also called the response or outcome variable because it is the variable you want to understand and predict.
Independent variable also referred to as, input or predictor variable, influences the dependent variable.
Following the above example, quarterly sales is the Dependent variable and the factors influencing the sales are the Independent variable.
In the presence of one dependent variable and one independent variable, it is called Simple Regression.
When there are two or more independent variables and only one dependent variable, it is called Multiple Regression.
You may have a data analysts’ team ready to do this for the company. However, it is helpful to understand the working of Regression Analysis.
Let’s continue with the previous example. You want to predict the sales for the coming winter. So, you decide to examine if there is any relationship between sales and the snowfall from the month of November till February.
You go back three years and collect the monthly sales number for the four months in the past three years. You figure out the average snowfall for the past three years. Now, you plot the data sets on a graph like this:
In the graph, the Y-axis is the dependent variable, i.e., your sales, and the X-axis is the independent variable, total snowfall. In the graph, each dot represents the data for each month – sales you made and the amount of snowfall.
Examining the graph, you notice that the sales are higher for the days when it snowed the most. Now, you need to find out how much did you sell during those days.
Draw a straight line in the graph that runs through the middle of all data points. This line will show you how much you sell when it snows a particular amount. The line explains the relationship between your dependent variable (sale) and independent variable (snowfall).
Now you put your data in the formula of Regression Analysis, so it looks like this:
Y = a +bx
Y = 150 + 3x
So, the formula tells you that even when there is no X, i.e., snowfall, you made a sale of 150.
So, assuming the independent variable, the amount of snowfall stays the same you can expect the same sale. And, with an increase in the amount of snowfall, you will make 3 times more sales.
This means, every time x goes up, y increases three times.
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Use of Regression Analysis in Business
Often organizations use regression analysis to predict the likelihood or outcome of future events. Opportunities and risks are two important aspects that every organization would like to be prepared for. Regression analysis can provide insight into both factors.
Insurance companies use regression analysis to define the credit standing of the policyholders and predict the potential amount of claims in a period of time.
Everyone is gathering data from all possible sources on all business operations- sales, finance, expenditure, and more. Companies depend more on data to make informed decisions instead of believing their hunch, intuition, or guesswork.
Regression analysis backs up the business decision with a scientific angle. This helps executives to make sense of the raw data and leads to a smarter business decision.
Data is the best way to understand the business process. But data, in itself, can’t tell you much, it needs to be evaluated and dissected. Regression analysis can translate the raw data into meaningful insight to help companies optimize their business process.
A Call Center may gather data to analyze the impact of wait times on the number of complaints. They can use regression analysis to understand the relationship between the variables – how the increase in wait times leads to an increase in complaints.
Companies can use regression analysis to understand and identify which variable is affecting their business and how (positively or negatively). Companies can use the insight to focus on preventing the negative impact and create action plans for an increase in operational efficiency.
You are bound to take risks and make errors in your business. But it is never if there is a chance to prevent it from happening. Regression analysis provides that chance.
While it is predicting the impact of variables on your topic of interest it is also providing you with insight into the factors that you should be avoiding. Regression analysis can provide evidence to support your decision and at the same time prevent mistakes.
A café owner may believe that extending the active hours will impact positively on the sales. However, regression analysis can help show that more active hours will not lead to more sales. It will rather increase operating expenses – the electricity bill, rent, employee salary, and so on.
Regression analysis prevents you from making any decision based on intuition. It gives you the necessary data and evidence you need to back up your decisions and proceed with business strategies.
Regression analysis helps you
Regression analysis is a statistical model that dissects the relationship between dependent and independent variables. In other words, it helps understand the impact the independent variable has on the dependent variable.
Businesses use regression analysis to understand the factors that impact the topic of interest. The following ways are how businesses use regression analysis:
Regression analysis helps identify the relationship between variables. For example, an Italian restaurant may want to understand the impact of the price of food on sales. Regression analysis can help the restaurant owner understand whether the increase in price increases sales or decreases sales.
Regression analysis helps understand the effect an independent variable has on the dependent variable. It evaluates the data and translates it into useful insight to help organizations make informed decisions and prevent mistakes.