Regression analysis is a very important and commonly used tool when determining the relationship that exists between two or more independent variables. This type of assessment is essential for any researcher who wishes to determine the manner and extent to which one variable influences another. For this reason, the main issue of consideration is usually to determine the variables of concern, and from this, it will be easy to determine the relationship. From showing the relationship between obesity and fast-food eating habits, and the correlation between teen violence and gaming, there is just so much that you can do through regression analysis.
Describe 2–3 combinations of independent and dependent variables that you could test using a regression analysis .
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To understand the idea of regression analysis, it would be great to consider a number of examples that incorporate independent and dependent variables. Here are examples of three combinations of such variables:
One may wish to measure the effect that different amounts of nutrient intake have on the growth of a baby. In this case, the number of nutrients consumed will be considered as the independent variable while the dependent variable is the growth of the baby, which is measured in terms of height, weight, or any other growth indicator as outlined by the experiment.
One may also wish to estimate the overall cost of living of a person based on their lifestyle. In this case, such factors as marital status, age, salary, etc. are taken as the independent variables, whereby the individual’s cost of living is dependent on these factors. For this reason, the cost of living is taken as the dependent variable.
Regression analysis can even be applied in a classroom setting when analyzing a case of poor performance by a student in their education. In this case, the independent variables are factors such as poor memory, failure to attend classes regularly, among other factors. On the other hand, the test variable is the student’s test score.
What types of results could the regression analysis yield? How could you use the knowledge gained from the test?
A regression analysis has both independent and dependent variables, and these two variables are used to make an inference of one with respect to the other. A regression analysis is used to calculate a value known as the correlation coefficient. One begins by drawing a scatter diagram, which shows the where most variables lie with respect to one another. From the diagram, it will be easy to calculate the correlation coefficient. This coefficient is a representation of the mean change in the response of one variable while holding the other variables constant. This statistical control of regression is important because it makes it possible to isolate the role of a single variable in comparison to the other variables in the model. A positive correlation coefficient shows that there is a strong relationship that exists between the two variables in question while a negative correlation coefficient indicates that there is little or no relationship between the variables.
Describe a specific organizational application of correlation and regression that you will use in your future career
One of the key organizational application of correlation and regression that I believe I will use in my future career is in predictive analytics. I can use this statistical concept to forecast future risks and opportunities in a business. Through demand analysis, it will be possible to predict the number of product units that a consumer is likely to purchase. Regression analysis is also a crucial tool that can also be used to predict the impact that demand will have on direct revenue. I hope to get into insurance in the future, and then I can make use of regression analysis to make an estimation of the possible number of claims expected in a given period of time and the creditworthiness of policyholders.
Describe a situation in your current or former workplace for which it would be appropriate to use correlation and regression to predict a future outcome that the company may be interested in.
I was initially working in the healthcare information technology sector, and correlation and regression analysis would have come in handy to determine the effect that waits time would have on the patient appointments.
Why do you believe it is important for the company to look at these variables? What does the company risk if it does not do this correlation/regression?
It is very important for the healthcare department to understand whether a long waiting time results in a decrease in patient appointments. While it is not possible to prove statistically that one thing causes another, it is possible to determine if any relationship exists between the variables, and this could help direct the analysis. Failure of the company to do this correlation/regression analysis is a risk to lose customers without clearly understanding what the issue is. If the company does this regression analysis, it will be able to eliminate or include some factors when determining the cause of a reduction in patient appointments.
