Big D Incorporated has proposed several business initiatives to expand its business. The company should be given insight regarding the recommendations for the matters. While pushing into a new market can be quite scary and tricky, the use of statistical methods to gain better information about the market can be used to provide how well the organization will do. Forecasting through the use of statistical methods will play a significant role in helping leaders and managers (Berman & Wang, 2016). Regression was previously discussed as a statistical forecasting tool. This paper analyzes how Big D Incorporated can make use of positive, negative, and minimal correlation to forecast its ventures.
A positive correlation implies that an increase in the independent variable corresponds with an increase in the dependent variable (Bolstad & Curran, 2016). For instance, if a college decided to offer more scholarship for the students, then the number of students would increase. The number of students (dependent variable) goes hand in hand with the number of scholarship offered (independent variable). A negative correlation shows an inverse relationship where an increase in the independent variable corresponds with a decrease in the dependent variable (Bolstad & Curran, 2016). An example of a negative correlation is an increase in the negative mood of the workers (independent variable) corresponds to a decrease in the productivity of the workers (dependent variable). A minimal correlation occurs where an increase or decrease in independent variables does not show either a positive or negative correlation (Bolstad & Curran, 2016). For instance, the height of an individual has a minimal correlation with the level of their income.
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The table below shows the correlation for the sample data provided.
The first correlation is positive since the three college basketball teams and one NBA team that spark interest will result in a positive increase in the number of indoor basketball leagues. The second correlation is negative since the lack of any indoor sporting facilities will result in a decrease in the demographic of the younger target market. The third correlation was positive since a warm geographic area results in an increase in the number of indoor sporting facilities. The fourth correlation was minimal since the high-income geographic setting would not affect the rural geographic setting.
Deductions from Correlations and Analysis as Either Short or Long-Term
From the analysis of the correlation, a positive correlation would have a long-term objective. For instance, from the first variables, one can conclude that the more the indoor basketball leagues that play and practice, the higher the chance to have college players and NBA players and teams. The second positive correlation is also long-term since a warmer climate is going to lead to more indoor sporting facilities as it would be too warm to play outside.
Implications for Big D Incorporated for the Client in Outdoor Sporting Goods
In case Big D Incorporated had its client based on the demographic, it may not be the best idea to invest in outdoor sporting goods. This is because a warmer climate would lead to a decrease in the number of outdoor sports.
Implications for Indoor Sporting Goods Market
The implications for Big D Incorporated client’s expansion to the indoor sporting goods would be an ideal demographic. This is because the warm climate will result in an increase in indoor sporting facilities.
Use of Correlation Tools to Identify Variables in the Research
The correlation tools can be used to identify how the variables are related by making use of the correlation coefficient. The Pearson’s correlation is used to measure linear correlations between two variables. The relationship between two variables could be reciprocal, causal, or parallel and would be seen in the simultaneous changes in the values after some time (Mertler & Reinhart, 2016). Big D’s client could use this with the supply and demand since the client would be offering indoor sporting goods to a demographic that has indoor sporting facilities and there will be a high supply and demand.
In conclusion, from the analysis of the correlation, it is thus recommended to expand. The indoor facilities imply that sales would be good based on the temperature and weather. The future of the correlations would also be important in expanding into the outdoor sporting goods market.
References
Berman, E., & Wang, X. (2016). Essential statistics for public managers and policy analysts . Cq Press.
Bolstad, W. M., & Curran, J. M. (2016). Introduction to Bayesian statistics . John Wiley & Sons.
Mertler, C. A., & Reinhart, R. V. (2016). Advanced and multivariate statistical methods: Practical application and interpretation . Routledge.
