Using Business Drivers and Analysis of Effectiveness: Correlations with Business Performance

When two variables are positively correlated, they move in the same direction, that is when one is variable is increasing the other variable also increases and vice versa (Schober, Boer and Schwarte, 2018) . An example of positive correlation is an improvement in the technological aspects in a factory resulting in higher production capacity. When two variables are negatively correlated, they move in different directions, that is when one variable is increasing, the other one is decreasing ( Nicosia and Latora, 2016) . An example of negative correlation is when an increase in criticism at the work place results in decreased workers’ productivity ( Bakotić, 2016) . Minimal correlation of two variables is denoted by zero and signifies that the two variables have no close association and that an increase or decrease of one variable pose neither positive nor negative correlation to the other ( Gogtay and Thatte, 2017) . An example of minimal correlation is the increased use of the internet being found not to have any correlation to the students’ test scores. 

In the first case, variable A was found to be positively correlated to B because the higher the number of available leagues for players to participate in, the higher the amount of funding that will be available thus more teams. 

In the second case, variable A was found to be negatively correlated to variable B because an increased number of younger players would mean they would require lesser indoor sporting facilities as they would not be using them. 

In the third case, the correlation was minimal because there is no much association between the number of indoor sporting facilities and the climatic conditions of the location. 

In the fourth case the correlation is positive because an increase in number of coaches willing to coach in the rural settings, the more the number of teams that will be formed and hence higher incomes. 

From the correlations, it is evident that the positive have long term objectives and outcomes. From the first correlation, the higher the number of indoor basketball leagues, the more the numbers of players who will join NBA. 

The investment of Big D Incorporated in the outdoor sporting goods would be quite worthwhile. This is because of the extremely warm outdoor weather which increases the need for outdoor sports. Big D Incorporated penetration into the indoor sporting goods market would not be fruitful because of decreased demand of indoor sporting goods. 

The correlation tools can effectively be used in the identification of whether expansion in indoor sporting goods is required for different locations. The Pearson’s correlation coefficients can be used to identify the different impacts that the variables will have on the need for indoor sporting goods. The correlations are reflective of the changes over time. The different variable s will affect the supply and demand as evident in Big D Incorporated case. 

References 

Bakotić, D. (2016). Relationship between job satisfaction and organisational performance.  Economic research-Ekonomska istraživanja ,  29 (1), 118-130. 

Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis.  Journal of the Association of Physicians of India ,  65 (3), 78-81. 

Nicosia, V., & Latora, V. (2015). Measuring and modeling correlations in multiplex networks.  Physical Review E ,  92 (3), 032805. 

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation.  Anesthesia & Analgesia ,  126 (5), 1763-1768.