Correlation and Regression Aid Business Success Through Predictive Analysis
If you love math, numbers, and logic, then a job in the field of statistics may hold the key to your future career success. The field of statistics and statistical analysis is becoming more and more important as businesses realize that they can statistically analyze their results and that they can use statistics to make critical decisions that will impact their financial success in the long run. The Optimization & A/B Testing Statistics course for example will teach you to speed up your a/b testing which in turn can affect your return on investment. Today we will show you how correlation and regression will help you and your business through predictive analysis.
Statistics and statistical analysis used to be the exclusive domain of statisticians, mathematicians and university professors, but with the launch of various software programs and applications, you no longer need a doctoral degree in mathematics in order to work out correlation coefficients or to perform regression analysis. Programs like Excel have add-ins that allow for statistical data analysis at the touch of a button, without necessarily having to completely understand how the analysis works or how to actually calculate the equations themselves.
A simple explanation of what correlation means
Correlation, it its simplest form, means the determination of whether there is a link between two sets of data or measurements. If there is a link, then the correlation coefficient allows you to express that link in terms of the relationship between the two sets of data and the strength of that relationship. The link merely expresses whether there is a direct or inverse relationship between the data. It does not imply a causal link.
Correlation is determined by the correlation coefficient. The correlation coefficient is a formula that is used to determine and express the relationship between the data. The formula has a value between -1 and 1. The value of -1 represents an inverse or negative correlation between the data. This means that as one value increases, the other value decreases. The value of 1 represents a positive correlation between the data and as one number increases, so does the other number. One or minus one represents the strongest relationship between the two sets of data and as the value of the correlation coefficient approaches zero, the relationship is weaker until the value of zero represents no correlation between the data at all.
Here is a simple example of correlation: As we approach the summer months, there is an increase in the sales of ice cream. When it gets hot, people eat more ice cream. I know it’s logical, but stay with me for a second. As it gets hot, people swim more and the number of drowning deaths increases.
We could express those two statements as two sets of data. One set of data would include the ice cream sales and the other set of data could include the number of deaths caused by drowning in those months.
Here is a fictitious spreadsheet to express the two sets of data mentioned above:
You can see from the above that the correlation coefficient for the above data equals a value of one, which shows a perfect correlation between ice cream sales and death by drowning.
A simple explanation of regression and regression analysis.
Regression is a description of the relationship between two variables where one variable is dependent on another variable. The predictor variable is called X and is plotted on the x axis of the graph which is the horizontal axis. The dependent variable is called Y and plotted on the Y axis of the graph which is the vertical axis.
Unlike correlation, regression analysis infers a causal relationship between the two sets of data. So not only is the data related, but a change in one will cause a change in the other.
If we apply regression to the above data then we would either have to draw the conclusion that when ice cream sales increase it causes an increase in drowning, or alternatively that as drowning deaths increase, it causes an increase in the sales of ice creams.
Logically, neither of these statements is true because we know that the weather is most likely a factor in each and although both increase at the same time, ice cream sales are most likely not related to drowning deaths or vice versa.
In fact, we could most likely use temperature data and ice cream sales and there will most likely be regression analysis that applies to these two variables because as temperatures rise, so more people buy ice cream and as we head towards winter, ice cream sales decrease. In fact a lot of business owners already use this type of inference to ensure they have sufficient stock of products. For a practical workshop in probability and statistics, why not sign up for a course today?
Research implies more than merely finding correlations within data
Although our ice cream and drowning is a fairly good example of regression versus correlation, a lot of the studies and data are not as logical or simple as our example. That is why further research is needed once a scientist has found correlation within a set of data. To isolate and work out what the real relationship is between the data, generally scientists perform further experiments where they change the value of X to see if Y is affected, and they change Y to see if X is affected, to ensure that their regression analysis makes logical sense and that the conclusions that they draw are scientifically sound.
For practical application of correlation and regression to analyze user experience for example, you could sign up for the UX Training today and learn to apply these concepts to improve practical user experience.
Correlation and Regression Conclusion
Although they may not know it, most successful businessmen rely on regression analysis to predict trends to ensure the success of their businesses. Consciously or unconsciously, they rely on regression to ensure that they produce the right products at the right time. They use it to measure the success of their marketing and advertising efforts. They rely on inference to predict future market trends and react to them. That is also why statistical analysis is gaining in popularity as a career.
If you are interested in statistics and how you can help business predict future trends or measure current success, try this course in Introductory statistics from Udemy today.
Last Updated September 2016
Learn how to use machine learning algorithms and statistical modeling for clustering, decision trees, etc by using R | By R-Tutorials TrainingExplore Course
Statistical Modeling students also learn
Empower your team. Lead the industry.
Get a subscription to a library of online courses and digital learning tools for your organization with Udemy Business.