Regression Analysis

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What is Regression Analysis?

Regression Analysis (RA) is a statistical forecasting method, that is concerned with describing and evaluating the relationship between a particular dependent variable and one or more other variables (usually called the independent variables).

Regression Analysis models are used to help us predict the value of one unknown variable, through one or more other variables whose values can be predetermined.

Example of Regression Analysis

Suppose a marketing or sales manager wants to predict the next month's numbers in some segment. Of course there are many factors that potentially could affect those numbers such as a promotion by a competitor or maybe the introduction of a new, improved product. These are called variables that may affect future sales. There could be a person in the organization who argues that the amount of rain in that month will be more important. There can be many such pottential factors.

In such situation, we can use regression analysis to mathematically sort the variables to find out which will actually have an impact on the sales. RA basically gives us the answer to questions such as: What factors are most important? What factors should we ignore? How are certain factors interrelated? And: With what confidence can we trust these factors?

In RA, these factors are called "variables". You have a dependent variable: the main factor which you are predicting. In our case that is monthly sales. And there are independent variables (also: explanatory variables): factors which you believe may have an impact on the dependent variable.

Usage of Regression Analysis. Benefits

A good regression model can predict the outcome of a given key business indicator (dependent variable) based on the interactions of other related business drivers (explanatory variables). For example: it allows you to predict (to a certain extent) a sales volume, using the amount spent on advertising and the number of sales people that you employ.

Of course, a real life situation typically has many more variables and is more complex. Nobody can really see into the future. However modern statistical methods, econometric models and business analytics software can be used to forecast and estimate to some extent what may happen in the future.

Steps in Regression Analysis. Process

The first stage of the process is to identify the variable that we must predict (the dependent variable). Then we carry out multiple regression analysis, focusing on the variables we want to use as predictors (explanatory variables). The multiple regression analysis would then identify the relationship between the dependent variable and the explanatory variables. This is then finally presented as a model (formula).

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Best Practices The best, top-rated topics about Regression Analysis. Here you will find the most valuable ideas and practical suggestions. 🥇 Regression Analysis Enables one to Determine the most Critical Predictor Variable Great description of regresion analysis. So well presented that any person with no statistical background can have a feel of the beauty of this rather complex analysis. Maybe just to add to this expla... 21   🥈 Regression Analysis Example Let’s say the demand relationship for apples is: Q = 120 – 1.8 P Can you explain exactly what this means? Explain how, as a business manager, you would use this information I am having trouble with... 6   4 comments

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