Assignment: The Company You Work For Processes And Uses CRUD

Assignment: The Company You Work For Processes And Uses Crude Oil In O

The company you work for processes and uses crude oil. In order to predict the price of production in the future, your manager wants you to analyze the historical price of crude oil. In looking at potential patterns, your company can use this information to help plan for the future. Use the provided crude oil prices data for this analysis, entering it into an Excel spreadsheet and creating various trend line visualizations to identify patterns and forecast future prices.

Paper For Above instruction

The analysis of historical crude oil prices is essential for companies involved in processing and using crude oil, as it enables better forecasting and strategic planning. By examining the price patterns over time, businesses can optimize their operations, hedge against price fluctuations, and make informed decisions regarding sourcing and inventory management. This paper discusses a comprehensive approach to analyzing the provided dataset through Excel, employing trend analysis with polynomial and exponential models to identify price trends and predict future movements.

Initially, the raw data comprising monthly crude oil prices from January 2010 to September 2015 will be organized into an Excel worksheet titled “CrudeOilPriceData.” This dataset will include columns for the month, year, and price, ensuring clarity and ease of analysis. Proper formatting, including a consistent font size of 10 or 12 points and neatly labeled columns, enhances data readability and professionalism. Margins will be set at 0.5 inches on the sides and 1 inch at the top and bottom, complying with assignment specifications.

In order to visualize the data effectively, two additional worksheets labeled “PolyTrend” and “ExpTrend” will be created. Each worksheet will feature an XY scatter chart that plots the historical crude oil prices over time. In the “PolyTrend” worksheet, polynomial trend lines of different orders (such as first, second, and third order) will be added to the scatter plot to explore various polynomial relationships between time and price. These trend lines help identify whether the data follows a linear, quadratic, or cubic pattern, thereby providing insights into the nature of price fluctuations over the given time period. The charts will be properly titled, with axes labeled clearly: the x-axis representing time (months and years) and the y-axis showing crude oil prices in dollars.

Similarly, on the “ExpTrend” worksheet, an exponential trend line will be fitted to the data. Exponential models are particularly useful when dealing with prices exhibiting rapid growth or decay, as they often capture the compounding effects seen in commodity prices such as crude oil. The trend lines will be formatted to match professional standards, with appropriate colors and line styles for clarity. Legends will be included to specify the trend type, and the overall appearance will aim for clarity and aesthetic appeal.

Interpretation of these models involves examining the fit of each trend line—using R-squared values and residual plots—to determine which model best describes the historical price movements. For example, polynomial models may capture cyclical or non-linear trends, while the exponential trend line might indicate periods of accelerating growth or decline. A comprehensive analysis will discuss how these trends can assist in forecasting future crude oil prices, helping the company make strategic decisions regarding production, inventory, and risk management.

In conclusion, visualizing and modeling the historical crude oil prices with polynomial and exponential trend lines provides valuable insights into underlying patterns and future price behavior. Employing Excel's charting and trend analysis tools enables a detailed and professional examination of the data, supporting more accurate forecasting and strategic planning for the company's operations. The combination of these modeling techniques ensures a robust understanding of past trends, which can be instrumental in predicting future fluctuations and making informed business decisions.

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