Regression Analysis

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Describing and evaluating the relationship between the dependent variable, and one or more independent variables. Explanation of Regression Analysis. Statistical Forecasting.

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What is Regression Analysis? Description

regression analysis (statistical forecasting)Regression Analysis 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.


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 intelligence 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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Usage (application): Statistical Forecasting
 
 

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Compare with: Dynamic Regression  |  Exploratory Factor Analysis  |  Exponential Smoothing  |  ARIMA  |  Analytical CRM  |  Operations Research


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