Sample Data
Explanation and Justification
The correlation in the first example is positive because the two variables are moving on the same track. This implies that an increase in the number of indoor leagues will signal an increase in interest from the three college teams and one NBA team. In the second example, the correlation is negative because the two variables are moving in the opposite direction – an increase in A equals a decrease in B. The relation in the third example is positive the reason why there are many indoor facilities is because this is an extremely warm geographic area making it difficult to introduce outdoor facilities. In the last example, the correlation is minimal – there is no direct relationship between high income and rural geographic setting.
Positive, Negative, or Minimal
If the correlation between two variables is said to be positive, it means that they are moving on the same track but negative when they appear to be moving in the opposite direction. Similarly, it becomes minimal if there is no direct relationship between two variables (Schober et al., 2018). A good example of positive correlation is where taller people are believed to be heavier – height and weight (Mohammad Rahimi et al., 2020). An example of a negative correlation is where sales are high during rainy days. Lastly, an example of a minimal correlation is the relationship between height and exam scores.
Short or Long-Term Objectives and Outcomes
As indicated earlier, the correlation in the first example was positive which makes it a short-term objective or outcome. This is because the public can get disinterested at any time – they may be interested because there is an upcoming NBA game. The second example is a long-term objective and outcome because the high demographic of younger target market will persist for a long time allowing the organization to introduce several indoor facilities. The third example also qualifies as a long-term objective and outcome – the climate does not change much in general, but the weather of an area tends to vary. The last example can be both long-term and short-term objectives and outcomes. For instance, the short-term objectives and outcome can change and influence one of the variables. With long-term objectives, it might take long but with adequate investments in the regions where there are many missed opportunities, the residents will start earning more.
Implications for Big D Incorporated
It is clear from the above correlation that the region is a great target for indoor sporting activities. Therefore, the main implication from this is that Big D inc. should limit its investment in the outdoor sporting goods. Instead, the company should try and inform its target clients, specifically from the outdoor facilities, the importance of indoor sporting goods. The high number of positive coefficients shows clearly that Big D inc. will succeed in convincing the clients in the outdoor sporting goods to shift to indoor services.
Penetrationinto the Indoor Sporting Goods Market
Penetration into the indoor good markets is a great move because Big D Inc. will gain access to unlimited profit potential. More specifically, the company will incur high profit margin by supplying indoor sporting goods since there are many people in the region that does sporting – they are the target market for Big D Inc. In addition to this, many recruiters from college and other major leagues such as NBA are targeting players at these facilities. There focus, therefore, should be to try and convince the potential clients to buy indoor sporting goods.
Correlation Tools
Correlation tools can be used to identify the variable in the research towards expansion in the indoor sporting good market. First, Big D Inc. should use correlation tools to analyze the impact of their decision to expand in the indoor sporting goods. For example, correlations can help the company in the process of gathering information about employees, resource availability and sales. These data, in turn, will help the company in examining the efficiency of its operations. According to Senthilnathan (2019), correlation tools are beneficial to any organization. Negative correlations can be utilized to find out where there is a population that requires specific services and goods.
The analytical tools can also be used to collect accurate data on variables in the potential market and then analyze the existing relationships or lack of it. These can also be used to gather necessary data about the potential competitors in order to understand their respective strategies. For instance, Big D Inc. could use these data to determine the correlation between indoor swimming and cold weather and months. The idea is to ensure the company arrives at a conclusion that indeed there is a need to venture into the indoor services. Once they conclude that there a positive correlation between indoor facilities and colder climate, they will proceed with venturing in the market.
References
Mohammad Rahimi, G. R., Bijeh, N., &Rashidlamir, A. (2020). Effects of exercise training on serum preptin, undercarboxylated osteocalcin and high molecular weight adiponectin in adults with metabolic syndrome. Experimental physiology, 105(3), 449-459. Web.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. Web.
Senthilnathan, S. (2019). Usefulness of correlation analysis. SSRN Electronic Journal, 5(2), 23-45. Web.