Regression Analysis

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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).

Regression Analysis Special Interest Group

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Discussions about Regression Analysis.

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 fee (...)
Regression Analysis in Sales Forecasting
I need assistance in the use of regression analysis in sales forecasting. What are some common variables used and how ar (...)
Explanation of Regression Formula
The regression formula is: Y = B0 + B1X or: Dependent variable = constant + B1
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, (...)
Number of Variables in Regression Analysis?
In a cross-sectional study for regression analysis, how many observations should at least be used? (...)
🔥 NEW Difference Between a Random Sample and Time Series
Hi all... I would like to know the basic difference between a random sample and time series.. Can someone please explai (...)
Theoretical and Statistical Explanations
I would like to understand how to determine the theoretical and statistical explanations and the residuals in the model (...)

Best Practices about Regression Analysis

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Regression Analysis Diagram

Statistical Forecasting (...)

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

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