The Advantages of Regression Analysis

The Advantages of Regression Analysis & Forecasting

The Advantages of Regression Analysis : Regression analysis refers to a method of mathematically sorting out which variables may have an impact. The importance of regression analysis for a small business is that it helps determine which factors matter most, which it can ignore, and how those factors interact with each other. The importance of regression analysis lies in the fact that it provides a powerful statistical method that allows a business to examine the relationship between two or more variables of interest.

The Advantages of Regression Analysis

The benefits of regression analysis are manifold: The regression method of forecasting is used for, as the name implies, forecasting and finding the causal relationship between variables. An important related, almost identical, concept involves the advantages of linear regression, which is the procedure for modeling the value of one variable on the value(s) of one or more other variables.

Understanding the importance of regression analysis, the advantages of linear regression, as well as the benefits of regression analysis and the regression method of forecasting can help a small business, and indeed any business, gain a far greater understanding of the variables (or factors) that can impact its success in the coming weeks, months and years into the future.

Why Regression Analysis Is Important

The importance of regression analysis is that it is all about data: data means numbers and figures that actually define your business. The advantages of regression analysis is that it can allow you to essentially crunch the numbers to help you make better decisions for your business currently and into the future. The regression method of forecasting means studying the relationships between data points, which can help you to:

  • Predict sales in the near and long term.
  • Understand inventory levels.
  • Understand supply and demand.
  • Review and understand how different variables impact all of these things.

Companies might use regression analysis to understand, for example:

  • Why customer service calls dropped in the past year or even the past month.
  • Predict what sales will look like in the next six month.
  • Whether to choose one marketing promotion over another.
  • Whether to expand the business or create and market a new product.

The benefit of regression analysis is that it can be used to understand all kinds of patterns that occur in data. These new insights may often be very valuable in understanding what can make a difference in your business.

How Is Regression Analysis Used in Forecasting

The regression method of forecasting involves examining the relationship between two different variables, known as the dependent and independent variables. Suppose that you want to forecast future sales for your firm and you’ve noticed that sales rise or fall, depending on whether the gross domestic product goes up or down. (The gross domestic product, or GDP, is the sum of all goods and services produced within a nation’s borders. In the U.S., it is calculated quarterly by the Commerce Department.)

Your sales, then, would be the dependent variable, because they “depend” on the GDP, which is the independent variable. (An independent variable is the variable against which you are measuring something by comparison – your sales in this case.) You would need to figure out how closely these two variables – sales and GDP – are related. If the GDP goes up 2 percent, how much do your sales rise?

Regression Analysis Example

Though this sounds complicated, it’s actually fairly simple. You could simply look back at the activity of the GDP in the last quarter or in the last three-month period, and compare it to your sales figure. In reality, the government reported that the GDP grew 2.6 percent in the fourth quarter of 2018. If your sales rose 5.2 percent during that same period, you’d have a pretty good idea that your sales generally rise at twice the rate of GDP growth because:

5.2 percent (your sales) / 2.6 percent = 2

The “2” means that your sales are rising at twice the rate of the GDP. You might want to go back a couple of more quarters to be sure this trend continues, say for an entire year. Suppose you sell car parts, wheat, or forklifts. It would be the same regardless of the products or services you sell. Since you know that your sales are increasing at twice the rate of GDP growth, then if the GDP increases 4 percent the next quarter, your sales will likely rise 8 percent. If the GDP goes up 3 percent, your sales would likely rise 6 percent, and so on.

In this way, regression analysis can be a valuable tool for forecasting sales and help you determine whether you need to increase supplies, labor, production hours, and any number of other factors.

Using Regression Analysis to Formulate Strategies

It’s important to understand that a regression analysis is, essentially, a statistical problem. Businesses have adopted many concepts from statistics because they can prove valuable in helping a company determine any number of important things and then make informed, well-studied decisions based on various aspects of data. And data, according to Merriam-Webster, is merely factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.

Regression analysis uses data, specifically two or more variables, to provide some idea of where future data points will be. The benefit of regression analysis is that this type of statistical calculation gives businesses a way to see into the future. The regression method of forecasting allows businesses to use specific strategies so that those predictions, such as future sales, future needs for labor or supplies, or even future challenges, will yield meaningful information.

The Five Applications of Regression Analysis

The regression analysis method of forecasting generally involves five basic applications. There are more, but businesses that believe in the advantages of regression analysis generally use the following:

Predictive analytics: This application, which involves forecasting future opportunities and risks, is the most widely used application of regression analysis in business. For example, predictive analytics might involve demand analysis, which seeks to predict the number of items that consumers will purchase in the future. Using statistical formulas, predictive analytics might predict the number of shoppers who will pass in front of a given billboard and use then use that information to place billboards where they will be the most visible to potential shoppers. And, insurance companies use predictive analysis to estimate the credit standing of policyholders and a possible number of claims in a given time period.

Operation efficiency: Companies use this application to optimize the business process. For example, a factory manager might use regression analysis to see what the impact of oven temperature will be on loaves of bread baked in those ovens, such as how long their shelf life might be. Or, a call center can use regression analysis to see the relationships between wait times of callers and the number of complaints they register. This kind of data-driven decision-making can eliminate guesswork and make the process of creating optimum efficiency less about gut instinct and more about using well-crafted predictions based on real data.

Supporting decisions: Many companies and their top managers today are using regression analysis (and other kinds of data analytics) to make an informed business decision and eliminate guesswork and gut intuition. Regression helps businesses adopt a scientific angle in their management strategies. There is actually, often, too much data literally bombarding both small and large businesses. Regression analysis helps managers sift through the data and pick the right variables to make the most informed decisions

Correcting errors: Even the most informed and careful managers do make mistakes in judgment. Regression analysis helps managers, and businesses in general, recognize and correct errors. Suppose, for example, a retail store manager feels that extending shopping hours will increase sales. Regression analysis may show that the modest rise in sales might not be enough to offset the increased cost for labor and operating expenses (such as using more electricity, for example). Using regression analysis could help a manager determine that an increase in hours would not lead to an increase in profits. This could help the manager avoid making a costly mistake

New Insights: Looking at the data can provide new and fresh insights. Many businesses gather lots of data about their customers. But that data is meaningless without proper regression analysis, which can help find the relationship between different variables to uncover patterns. For example, looking at the data through regression analysis might indicate a spike in sales during certain days of the week and a drop in sales on others. Managers could then make adjustments to compensate, such as making sure to maintain stock on those days, bringing in extra help, or even ensuring that the best sales or service people are working on those days.

What Is the Significance of Regression Analysis in Business?

Regression analysis, then, is clearly a significant factor in business because it is a statistical method that allows firms, and their managers, to make better-informed decisions based on hard numbers. As Amy Gallo notes in the Harvard Business Review:

“In order to conduct a regression analysis, you gather the data on the variables in question….You take all of your monthly sales numbers for, say, the past three years and any data on the independent variables you’re interested in. So, in this case, let’s say you find out the average monthly rainfall for the past three years. . . Glancing at this data, you probably notice that sales are higher on days when it rains a lot. That’s interesting to know – but by how much? If it rains 3 inches, do you know how much you’ll sell? What about if it rains 4 inches?”

Regression analysis is significant, then, because it forces you, or any business, to take a look at the actual data, rather than simply guessing. In Gallo’s example, a business would plot the points showing monthly rainfall for the past three years. That would be the independent variable. Then, you would look at the monthly sales figures for the business for the past three years, which is the depending variable: In essence, you’re saying rising or falling sales depend on the amount of rainfall in a given month.

Rain vs. Sales

Suppose your business is selling umbrellas, winter jackets, or spray-on waterproof coating. You might find that sales rise a bit when there are 2 inches of rain in a month. But you might also see that sales rise 25 percent or more during months of heavy rainfall, where there are more than 4 inches of rain. You could, then, be sure to stock up on umbrellas, winter jackets or spray-on waterproof coating during those heavy-rain months. You might also extend business hours during those months and possibly bring in more help.

The example shows the benefits of linear regression; that is, you are using a single line that you draw through the plot points. The line might go up or down, depending on the rain total for each month, but you are essentially comparing two variables: monthly rainfall versus monthly sales. This type of linear regression gives you a clear, visual look at when a company’s sales crest and fall.

This example may seem obvious: More rain equals more sales of umbrellas or other rain-related products. But it shows how any business, can use regression analysis to make data-driven predictions about the future. Put another way, regression analysis can help your business avoid potentially costly gut-level decisions – and instead – base your decisions about the future on hard data, giving you a clearer, more accurate path into the future.

Read it also:  Subjective Probability

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