Regression analysis is a useful tool in determining the relationship between variables. This is especially so for businesses due to the necessity to predict favorable outcomes whenever possible. It is a statistical technique which uses numerical data to identify trends and relationships. When combined with the decision-makers intuition and experience, the outcomes of statistical analyses lead to better accuracy in selecting an approach or a course of action. If regression analysis is carried out using the right data, it may offer significant competitive or strategic advantages to the organizations concerned.
There are several dependent and independent variables that come to play when evaluating the relationship between fast food eating habits and obesity, or lack thereof. For instance, a fast food restaurant aiming at evaluating the effect that their meals have on the weight of clients could pick the number of calories per time as the dependent variable and the body mass index (BMI) of the clients as the independent variable (Beck, 2017). Another example would be frequency of meals as the dependent variable and BMI as the independent variable. The regression analysis may reveal a positive correlation between consumers of high calorie meals and their higher BMIs, or higher BMIs among the most frequent clients. However, the margin of error is a significant factor in determining how far results go is representing the real state of affairs. The margin of error is likely to decrease with increase in the number of factors that have been considered in the analysis (Beck, 2017). In the current scenario, for instance, obesity could be caused by various factors, some of which may not even be related to fast food eating like lack of exercise and sedentary living. In fact, the fast food sold by the restaurant may be so healthy that it does not cause obesity at all. The error term determines whether the outcomes of the regression analysis are credible as a basis of decision-making.
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One of the most likely uses of correlation and regression is in evaluating the relationship between sales and market forces. Every entrepreneur must employ whatever mechanisms necessary to ensure that their enterprise remains a going concern amid competition. Competition may be very stiff in industries which are already established with older players are financially stronger than new startups. The ability to interpret the trends of market forces and the effect on sales could therefore provide an opportunity to identify niches within the industry through which the startup could thrive (Darlington & Hayes, 2016). The application of regression analysis would be even more effective when working for a larger organization since such organizations can often provide the relevant data collected over the long periods of operation. Larger pools and varieties of data are likely to increase the usability of correlation and regression outcomes in decision-making. If an organization has existed as a going concern for a century, for instance, current regression models can be compared with past analyses for better perspective.
The recent pandemic has generally led to increased use of the internet and of call and messaging services due to the need to communicate despite the necessary restrictions. As an employee of a telecommunications company which also provides internet services, there are numerous variables that need to be analyzed, particularly pertaining to the trends in the use of data and voice calls. Moving away from generalizations, specific trends should be analyzed to ensure that the organization offers the appropriate plans to maintain the existing clients, maximize on profits and attract new clients. For instance, analyzing the relationship between call frequency or call duration and the number of Covid cases reported daily may provide a trend that allows the company to formulate an appropriate plan that takes advantage of the scenario. One of the observations may indicate a spike in call durations or frequency during the times when the announcements are made, such as during news hours (Darlington & Hayes, 2016). In that case, the company may offer lower rates during those hours to take advantage of economies of scale and as a strategy of acquiring new subscribers.
Looking at these variables is paramount if the telecommunications company is to maintain a competitive edge in the market and retain its market share. Notably, the telecommunications industry is oligopolistic, characterized by a few big players engaging in cut throat competition. Any wrong move could easily lead to disastrous results (Darlington & Hayes, 2016). Conversely, a data driven idea could give an organization a significant advantage over other players. Regression analysis could be used to facilitate greater efficiency in the organizations operations, as exemplified by the ability of a telecommunication company to take full advantage of peak hours (Beck, 2017). Decisions that are backed by statistical data also tend to have greater effectiveness in achieving the desired goal, that those backed by qualitative data and information. However, the greatest benefit is drawn from combining both qualitative and quantitative analysis to make a strategic decision.
In conclusion, regression analysis provides an opportunity for decision makers in a variety of contexts to base their decisions on the outcomes of quantitative analytical techniques. Quantitative data may be very helpful especially when used in support of intuitive ideas. The outcomes of regression analyses are however only as relevant as the imputed data. The decision makers must decide which specific variables should be analyzed in order to produce the required specific result. Analyzing large amounts of data simultaneously requires complex algorithms, and the analysts run the risk of a higher error especially if some of the variables are not relevant.
References
Beck, V. L. (2017). Linear regression: Models, analysis, and applications.
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation.
