In this example, a company can likely come up with a stronger maintenance budget using the outlook from the multiple regression analysis. Adding more variables complicates the model, though it's often one of the biggest advantages of a regression study. With the multiple regression analysis model, the company can get a more precise maintenance cost outlook by adding several other factors, like the age of cars in the fleet, the make and model of vehicles, the locations of each rental office and the number of car accidents recorded. With the simple regression analysis model, the company can get a rough estimate of maintenance costs by basing the study on the average number of miles each car drives in a 12-month period. Let's say a national car rental company wants to calculate the yearly estimated cost of maintenance for its vehicle fleet. Here's a scenario to help show you how to apply the two types of regression analysis to a business situation: Related: Q&A: What Is Forecasting? Definition, Methods and Examples An example of regression analysis Using regression analysis helps teams figure out which factors matter most, which ones are less of a priority and how they all connect. Predicting future sales and growth : What will profits look like over the next six months?Įxplaining a certain event: Why did customer service calls increase last month?ĭeciding what to do: Should the team start this sales promotion or another one? Regression analysis uses a set of data to make predictions and is a great tool to use for a variety of business reasons, like: Professionals in many industries use regression analysis to understand and interpret links between factors in order to make data-driven decisions. Related: How To Do Regression Analysis in Excel in 9 Steps (Plus Tips) Why do professionals use regression analysis? Running a multiple regression analysis study is more complex, but it offers more realistic and specific results than simple regression analysis. For example, you might evaluate the relationship between how much money a person makes and their experience, education and geographic location. In comparison, you can use multiple regression analysis to estimate the relationship between a dependent variable and two or more independent variables. Related: 13 Regression Types and When To Use Them in Data Analysis Multiple regression analysis Some individuals may also refer to this method as a single regression analysis. For example, you could assess the connection between how much money a person makes and their education level or the number of crop yields compared to the seasonal rainfall. Simple regression analysis can estimate the relationship between a dependent variable and a single independent variable. There are two types of regression analysis that you can use: Simple regression analysis For example, it can help you better understand the relationship between variables that affect your sales or budgeting goals. Regression analysis evaluates how strongly related the two elements are to help you make stronger business plans, decisions and forecasts. Regression analysis is the mathematically measured correlation of a link between two variables : the independent variable X and the dependent variable Y. While a simple regression analysis evaluates the relationship between two variables, multiple regression analysis assesses the correlation between a dependent variable and more than one independent variable.īusinesses can use regression analysis to predict future sales, evaluate growth opportunities, explain past occurrences and make strategic decisions. Regression analysis evaluates the strength of the correlation between independent and dependent variables. In this article, we discuss what regression analysis is, explore why businesses use it and explain how to conduct a regression analysis to optimize professional decision-making processes. Understanding regression analysis can help you make more effective business decisions for the company you work for or your team. For instance, you might want to use regression analysis to determine if raising the price of a product influences how many people buy it or if the weather affects your target audience's purchasing decisions. Businesses can use statistical tools, such as regression analysis, to help them evaluate the relationship between two variables.
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