Regression Analysis 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 you could predict sales volume based on the the
amount spent on advertising and the number of sales people you employ. Of
course, a real model would need more variables and is much more complex.
Although nobody can really look into the
future, modern statistical methods, econometric models and
business intelligence software go a long way in helping businesses
forecast and estimate what is going to happen in the future.
Regression analysis is a Statistical Forecasting model that
is concerned with describing and
evaluating the relationship between a given variable (usually called the
dependent variable) and one or more other variables (usually known as the
Regression analysis models are used to help us
predict the value of one variable from one or more other variables whose
values can be predetermined.
The first stage of the process is to
identify the variable we want to predict (the dependent variable) and to
then 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).
Dynamic Regression |
| ARIMA |
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