Download The Excel Template Attached To This Assignment

Download The Excel Templateattached To This Assignment Onto Your Compu

Download the Excel Template attached to this assignment onto your computer, open it up, and read through the "Red Snapper Scenario" at the top of the page. All of this week's assignments focus on historical Red Snapper Catches for 2015 through 2018 (by Quarter) off the coast of Louisiana. The last four rows of the table are empty as 2019 is what you are going to forecast using the historical data for 2015 through 2018. As you scroll down through the Spreadsheet, you will notice in the far left column, there are five tasks for you to complete this week. Every one of these tasks is mirrored in the three YouTube videos below. Although this project is about Red Snapper Catches (your faithful designer's favorite fish to catch and eat out of the southern bays of Louisiana) and the videos forecast Car sales, the process for forecasting the future year (in our case 2019) is identical. You will want to watch these videos as you complete each of the five tasks. Where you want to transition from video 1 to video 2 and from video 2 to video 3 is very easy to see. One easy way to get started is to have a video open on one side of your screen and the excel template on the other.

Paper For Above instruction

The task at hand involves analyzing historical red snapper catch data from 2015 to 2018, located in an Excel spreadsheet, and forecasting the catch for 2019. The initial step requires downloading the provided Excel template onto a computer, opening it, and thoroughly reviewing the "Red Snapper Scenario" information. This scenario sets the stage for understanding the data and the forecasting process that will be applied. The spreadsheet contains data categorized by quarter for each year and includes five tasks that guide the forecasting process, which need to be completed systematically.

The core of this assignment parallels a financial or sales forecasting methodology, adapted here to fish catch data, but the principles are universally applicable. The process demonstrated through three YouTube videos emphasizes steps like analyzing historical data trends, applying appropriate statistical forecasting models, and interpreting the results. Although the videos focus on car sales forecasting, the same analytical techniques are relevant and transferable to red snapper catch data. The instructional videos are designed to be viewed sequentially, with each video building upon the previous, providing a comprehensive understanding of data analysis and forecast generation in Excel.

Forecasting involves identifying patterns, seasonality, and potential trends within the historical catch data. Simple methods such as moving averages or more complex techniques like exponential smoothing or regression analysis can be employed. The decision on the appropriate method depends on the visual inspection of the data and the complexity of the patterns observed. Once the method is chosen, the next step is to generate forecasts for the year 2019. This involves applying the selected model within Excel, using the historical data, to produce a point estimate for each quarter in 2019.

Throughout this process, it is important to critically evaluate the forecast outputs, considering potential variability and confidence intervals. Interpreting the forecast helps in understanding the possible fluctuations in red snapper catches, which are critical for fishery management and economic planning. The assignment encourages engaging with the videos, which serve as a step-by-step guide to mastering the forecasting techniques in Excel, tailored for applied scenarios beyond just fish catches, like vehicle sales.

In conclusion, this project underscores the importance of data analysis, statistical modeling, and interpretation skills in forecasting. By completing the five outlined tasks in the spreadsheet and utilizing the instructional videos, students will develop practical expertise in predictive analytics using Excel, applicable to various fields such as fisheries, economics, and business forecasting. The process fosters a deeper understanding of how historical data can inform future expectations and decision-making, emphasizing the universal applicability of sound statistical practices.

References

  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
  • Chatfield, C. (2000). Time-Series Forecasting. Chapman & Hall/CRC.
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications. Wiley.
  • Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
  • Gardner Jr, E. S. (1985). "Exponential smoothing: The state of the art." Journal of the American Statistical Association, 80(389), 108-116.
  • Taylor, J. W. (2003). "Short-term load forecasting with exponential smoothing." IEEE Transactions on Power Systems, 18(3), 1015-1021.
  • Woo, C., & Hyndman, R. J. (2003). "Density forecasting of economic time series." Journal of Econometrics, 113(2), 359-375.
  • Arnold, M. (2015). Forecasting: principles and practice in business, industry and government. Wiley.
  • Makridakis, S., et al. (2018). The Innovation of Forecasting Methods: Advances in Forecasting. Springer.
  • De Gooijer, J. G., & Hyndman, R. J. (2006). "25 years of time series forecasting." International Journal of Forecasting, 22(3), 441-473.

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