Of what value will correlation and regression analysis be to my MBA programme?
And of what purpose will it be for my learning.
These are the questions that re-echoed within me at the introduction of this topic to the class.
But I had to get my mind off from the negative but get focused to understand why I need it to make good business decisions as an executive.
What is correlation?
It can be said that a correlation is referred to as that variable which indicates that as one variable changes in value, and this leads to changes in the specific direction of the other variable. In other to Understand that relationship, it is useful to use the value of one variable to predict the value of the other variable. For example, height and weight are correlated—as height increases, weight also tends to increase. Consequently, if we observe an individual who is unusually tall, we can predict that his weight is also above average.
In statistics, correlation is a quantitative assessment that measures both the direction and the strength of this tendency to vary together.
A correlation is about how two things change with each other.
It is a case of when two things are changing together in the same way. One goes up, then the other also goes up. This is a positive correlation.
Positive correlation between height increases and weight increases. The more height increases, the more weight increases, (trend to the top right).
When we speed with our car and it goes faster, we will probably get to our destination faster and your total travel time will be less. It is a case of two things changing in the opposite direction (more speed, but less time). This is a negative correlation.
A negative correlation between car speed and travel time. The faster the car, the less travel time (trend to the bottom right).
The third possible way we can use for two things can “change”. Or rather, not change. For example, if one were to gain weight and looked at how his test scores changed, there probably would not be any general pattern of change in the test scores. This means there is no correlation.
Knowing about how two things change together is the first step to prediction.
Being able to describe what is going on in our previous examples is a good way to begin. But what is the point? The reason is to apply this knowledge in a meaningful way to help predict what will happen next.
After collecting all this information, we can ask more questions about why this happens to better understand this relationship. Here, we may start to ask what increases our height as well as weight increases.
In analysing correlation, it can be applied to our job or business as well. If we notice sales or other important metrics are going up or down with other measure of our business (in other words, things are positively correlated or negatively correlated), it may be worth exploring and learning more about that relationship to improve our business.
Correlations can have different levels of strength.
As mentioned earlier, there are some general correlations as either.
- positive,
- negative, or
- non-existent
Although the descriptions are nice, all positive and negative correlations are not all the same.
These descriptions can also be translated to numbers. A correlation value can take on any decimal value between negative one, −1−1, and positive one, +1+1.Decimal values between −1−1 and 00 are negative correlations, like −0.37−0.37. Decimal values between 00 and +1+1 are positive correlations, like +0.63+0.63.
In a perfect zero correlation, it means there is no correlation.
For each type of correlation, there is a range of strong correlations and weak correlations. Correlation values closer to zero are weaker correlations, while values closer to positive or negative one is stronger correlation.
With a strong correlation, it shows more obvious trends in the data, while weak ones look untidy. For example, the stronger high, positive correlation below looks more like a line compared to the weaker and lower, positive correlation.
#MMBA-4
