Have you heard this before, “there is no new thing under the earth”? The future has been predicted to be very different from the world we know today, this is true as folks in the ‘60s would be shocked at the advances in technology especially in areas bordering telecommunications, transportation, and even businesses.
These revolutions might have been predicted years ago by our ancestors, just like in their days they were told of certain possibilities not obtainable in their time. They have made life easy for the common man and even with the advent and proliferation of artificial intelligence, which has the tendency to replace major classroom discussions for youngsters and even working professionals.
Events happening today might have been predicted before, and this sets a pattern that might reoccur in the future. We see patterns in almost everything we do, and this forms the reference or basis for forecasting how the future will become.
In business, this is common and people have used their experiences to affect what outcome they want to achieve. Taking a clue from the data analytics course on the MBA program, this allows us a peep into the future using mathematical models to determine certain phenomena within our communities.
One mathematical model used extensively to predict or forecast the future is Regression. This has found its use within artificial intelligence models and even machine learning as it works efficiently with a pattern recognition model to identify patterns and gems within any statistical analysis or in our business operations to enable managers make marketing decisions or even managerial decisions for their companies.
To make predictions about the future is known as forecasting while for the past, it is backcasting. As useful as this model is, one must get a hang of it by understanding what regression analysis means.
Regression analysis is a methodology that seeks to understand the relationship between an independent variable (this can be more) and a dependent variable. A classic example will be that of sales driven by advert, etc. This relationship allows us to optimize operations as stated above.
The formula stands as this; Y = mX + b
Where Y is the dependent variable, X is the independent variable, M is the slope and b is the intercept.
The formula is also identical to the equation of a straight line, nevertheless it got different types, namely; simple linear regression deals with just two variables, multiple linear regression considers the influence of multiple variables, time series regression deals with data captured over a period of time, polynomial regression comfortably sorts non-linear variables, etc.
Using the Microsoft Excel application offers a quick window into understanding the flow and subsequent predictable outcomes by using the generated equation for the future. This application is just one of the statistical tools used, R and Python languages are popular as they provide a more robust approach into the subject.
Fellow managers, I implore you to try your hands on this subject, plug in your data and allow the models to help you identify those variables that make or mar business operations.
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