There are many occasions in business when changes in one factor appear to be related in some way to movements in one or several other factors. For example, a Marketing Manager may observe that sales increase when there has been a change in advertising expenditure. The Transport Manager may notice that as vans and lorries cover more miles then the need for maintenance becomes more frequent.
Certain questions may arise in the mind of the manager or analyst. These may be summarised as follows: 1)
Are the movements in the same or opposite direction?
Could changes in one phenomenon or variable be causing or be caused by movements in the other variable? This is an important relationship called a Causal Relationship. 3)
Could apparently related movements come about purely by chance? 4)
Could movements in one factor or variable be as a result of combined movements in several other factors or variables? 5)
What is the use of this knowledge anyway?
Very frequently, the manager or analyst is interested in prediction of some kind. For example, the Sales Manager may wish to predict sales levels if advertising were increased by, say, 20%. Here there is clearly some causal model in the mind of the manager.
When the value of one variable is related to the value of another, they are said to be correlated. Thus, correlation is a statistical technique used to describe the strength of the relationship between two variables by measuring the degree of ‘scatter’ of the data values. The less scattered the data values are, the stronger the correlation is said to be. Correlation may be positive, negative or zero.
If the two variables in a bi-variate data set rise or decrease together we talk of positive correlation. For example: 1)
The more a company advertises, the more the sales it expects. This is a testable hypothesis in economics/marketing research. 2)
The higher the intelligence quotient (I.Q) of a student, the higher...
Please join StudyMode to read the full document