(Read Part 1, Part 2, Part 3, Part 4, Part 5 and Part 6 of this Stepping Stones series.)
“Sometimes the smallest step in the right direction ends up being the biggest step in your life. Tip toe if you must, but take the step.” ― GemmaStone.Org
Step-wise Multiple Regression Modeling and other means such as hardware monitors were used to uncover and obtain solutions for these situations. If Step-wise Multiple Regression were a tool only for computer technology, it would be a highly powerful one. That is not the case. As Regressions can be used to predict the relationships between variables, the techniques have an extremely large range of uses and applications.
Let’s consider a few examples
- The selling price of a house is a good candidate. The price is a function of the number of rooms, size of the house in square feet or meters, the school district, pricing of other comparable houses in the area and other factors.
- In virtually any business, customer and employee profiling is important. Customer profiles based on factors such as demographics, age, sex, income level, education, ethnic group and many more factors can impact marketing, sales and needs. The basis of likelihood of an employee being suited for promotion might be evaluated based on salary, education level, certifications, performance reviews and such.
- Medical research might use Multiple Regression or Step-wise Multiple Regression to determine the likelihood of disease being present in a person or chances of them contracting a malady.
- If you watch the weather, it is most likely that some of the projections are based upon Multiple Regression Models of one form or another. These are based on wind direction and speed, temperature, humidity and a range of other factors that are used for forecasts.
- Science and engineering use Multiple Regression Models for numerous purposes. In chemistry, the use of Multiple Regression Models is used for a multitude of analysis. If some of these seem like space science, yes they do reach space and analysis of interaction among planets, stars and astrological bodies.
- Insurance companies can similarly use Regressions for accident rates and mortality rates to base premiums and reserve amounts.
- If you are involved in agriculture, these techniques can be utilised based upon factors such as amount and type of fertilizer used (or need to be used); type of soil, moisture and rainfall past and projected; previous crops planted; angle of soil for run-off; variety of crop planted and many more.
- Mining or similar activity may use various forms of Regression to determine the probability of oil, gas or other elements being present. Sonic pulses, drilling samples, geological characteristics and other sources of information can be effectively used to model and evaluate the conditions for particular purposes.
- Educational institutions can use these models for evaluating the benefit of particular teaching methods or the likelihood of success of a student. Analysis of test scores, attendance records, involvement in particular activities, parental involvement and other factors can be used as input for analysis.
- Athletic organizations can evaluate current performance and project future performance of athletes. Factors such as motion and stress can be measured using sensitive monitors and then analysed using techniques like Multiple Regression Models and others.
- Construction companies can evaluate stress and load capabilities for differing methods and materials. They can also use Multiple Regression to evaluate factors that influence the stability of structures, like wind impact. Consider “Galloping Gertie,” otherwise known as the Tacoma Narrows Bridge which was built and collapsed in 1940. This was an excellent example of not doing proper analysis. In contrast, structures like the World Trade Centre had flex in its design and could sway back and forth up to approximately 3 feet at the top so it would not have natural damage, unlike Gertie.
Hopefully some insight has been given and you have gleaned some pearls of wisdom as a reward for sticking with this series.
It should be noted that conscientious effort at accurate development and analysis of Regression models is crucial to effective and meaningful results. A large number of individuals and organisations misuse statistics and correlations. Many even do not adhere to the main underlying assumption of randomness.
Often correlations are made where none exist. It is too often that covariant elements (variables that both vary or are dependent upon another variable) are taken as one causing the variance in the other when actually they both vary on some other variable altogether (or it could even be mere coincidence). There are many who are against Step-wise Regression. However, as with pearls, if cultured well, they will bring high quality, precious results. If done poorly, they will be course and the results will be very little, if not actually detrimental.