¶ … personally understood as well as how/whether the topic could/should be applicable to public sector decisions. The author will also explain whether the data will be useful in a future job. While many of the topics in this course are attributed to and associated with the private sector, the usage of these tools and topics in the public sector is pervasive as well even if the manifestation is a bit different in many cases. The spreadsheet modeling supplement from the class text is the first thing that will be discussed. The introduction alludes to the somewhat common presumptions about how Excel modeling and data modeling in general is something that is more in line with what private businesses need and that cannot be further from the truth. The supplement starts off with a good bullet point summary of what is involved in spreadsheet modeling. The author of this response does understand the different facets and outputs of modeling including spreadsheets, goal seeks, data tables and charts. Charts and such are a good way to represent data in a more understandable and perceivable way. While data tables will suffice for more experienced data analysts, people from other specialties or lay people in general will understand a pie chart or line graph much more readily and easily than data that is remotely raw in nature. The author of this response absolutely believes that this tactic and practice will widely be used during the author's career and this will be of great benefit to both the author and those that would be receiving and reviewing the data that is being covered and communicated.

Another facet and lesson of the class that the author is certain will and should be used extensively is making decisions when there is a modicum of uncertainty and fear of the unknown or the not fully known as described in the PowerPoint for the sixth chapter. Indeed, there are opportunity costs and uncertainties when it comes to decisions where complete data is unobtainable or at least not on hand when the decision...

The words about decision trees helps standardize and give a visual element to the decision process and this can be a great help to those that are unable to do so in their heads and/or that prefer to give a visual presence to what is being done and why and what options exist. It also would make it easier to backtrack and review prior decisions if one path taken ends up being a dead end or not as rosy as was presumed. The author will also attempt to use the Bayes model so that there is a standardization to the decision tree and how it is constructed. Other people that understand the Bayes model will instantly recognize what model is at work and will not have to get accustomed to it. Even people new to the model can quickly be brought up to speed with the fact that items to the left of a particular item are antecedents and precursors of what happens to the right of those prior items. In other words, if items A, B and C are listed left to right, then B is dependent on A and C is dependent on both A and B. Very simple and to the point. Items that do not have causality can be separated or otherwise notated as being what they are in relation to other items.
The PowerPoint about time series analysis and forecasting is very much a blend of trying to figure out the unknown (because it has not happened yet and it is not completely clear what precisely will happen) and depicting the results and analysis in a way that can be easily assessed and parsed, not to mention changed if the presumptions or known facts change. The PowerPoint discusses how there are many different ways to mold and shape the data. The author of this report understands this and realizes that this flexibility in Excel and similar programs is necessary because each situation is different. Some points of analysis are about things that are very steady and predictable while other things are much more prone to get shocks to the system that cause huge spikes up, down and so on. Indeed, predicting snow fall totals for a winter season in…