ANALYTICS
Regression analysis is 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 variable(s) we want to use as predictors (explanatory variables). For example, the dependent variable might be overall satisfaction, the explanatory variables price, quality, value for money, delivery time and staff knowledge.
The multiple regression analysis would then identify the relationship between the dependent variable and the explanatory variables – this is presented as a model (formula) that might look like this:
Overall satisfaction = (1.37 x price rating)+ (0.91 x quality rating) + (0.64 x delivery time rating) + 2.42 (a constant)
Invariably not all of the possible explanatory variables are included in the model due to inter-correlation between them: for example, the ratings that people give on price and value for money may be very closely correlated and are therefore not both required in the formula.
The overall predictive powers of the model can be calculated and expressed as the co-efficient of determination R2 (= the explained variation / total variation). The co-efficient of determination will lie between 0 and 1: 1 would mean that it is able to explain 100% of the variation although a figure of less than 50% is more common.
