Imagine that you are a manager at a delivery service and you are creating a report to project the effects on your company of rising gas prices in the next ten years. Using the attached document as statistical analysis as your basis and outside scholarly resources to support your claims, Include the following considerations:
1. Introduce the project and its significance to the company.
2. Explain the statistical analysis that you completed in Part I. Be sure to explain where the data came from, what analysis was done, and what the results were.
3. Give conclusions that you have drawn from the data. Consider the effects of your gas price predictions on the delivery business. Also consider whether or not you believe your predicted gas prices are accurate. What could occur in the future that would change your linear regression line and therefore your prediction?
Project and its Significance to the Company
Fuel prices have been an area of concern for many industries including delivery industry in which the company operates. The project of studying effects of rising gas prices in next ten years is important for the company in terms of strategic and financial planning. The changes in gas prices affect entire operations of the company and hence it is very important to determine the effects of these price changes on delivery business. This would eventually help the company in identifying appropriate responses. Using the past data, a future trend could be predicted and the same could be applied to the delivery business to determine feasibility in the future. If the analysis results show that gas prices have been increasing and in future too they would increase to a point where the company would not be able to sustain in the industry, it would be wise to consider alternatives. If the estimate shows that the price would be within reasonable limits, the company could go ahead with the plan for the delivery business.
For the purpose of analysis data was obtained from the Bureau of Labor Statistics website for regular unleaded gasoline for a period of 30 years from1982 to 2011. The data is U.S. city average for each month. Regression was conducted on annual mean of twelve months for 30 years. A regression of y on x is a method of predicting ...
The companies effects of rising gas prices are examined. The statistical analysis that are completed are explained.