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Decision Modelling and Analysis_Final Paper

Decision Modelling and Analysis_Final Paper

Q During the course, you have applied a variety of methods to analyze data sets and uncover important information used in decision making. Having a good understanding of these topics is important to be able to apply them in real-life applications. Below are some of the key elements that were discussed throughout this course. Analyze each of the elements below. In your analyzation, consider and discuss the application of each of these course elements in analyzing and making decisions about data. Incorporate real-life applications and scenarios. The course elements include: • Probability • Distribution • Uncertainty • Sampling • Statistical Inference • Regression Analysis • Time Series • Forecasting Methods • Optimization • Decision Tree Modeling The paper must (a) apply and reference new learning to each of the ten course elements, (b) build upon class activities or incidents that facilitated learning and understanding, and (c) present specific current and/or future applications and relevance to the workplace for each of the ten course elements. The emphasis of the paper should be on modeling applications, outcomes, and new learning.

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Probability is a study of chances, where one can compute the chance of occurring of an event from the given information. Probability, which is an essential tool for the data analysis, can be used to find out the mathematical value for a likelihood of occurrence of an event. For an example, one can use the probability to find out, how snowfall would affect the flight schedule of an airport. Based on the flight schedule data on the days with the snowfall and without the snowfall one can find out the probability that a flight would be delayed if there would be a snowfall (Fernandez-Granda, 2017).