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dicussionreplies.docx1. carson hale
I wish I had met Leonard Mlodinow back in the 1990’s. In a four-year period during my undergraduate career the Buffalo Bills cost me dollars I did not have to spare. As a History/English major I had very little knowledge of how the law of large numbers actually worked. I had heard about a thing called the law of averages. In my mind I was convinced no team could lose four consecutive Super Bowls. Each year I convinced myself this was the time so, I doubled down each year on the previous years bet…and lost. In my non-scientific thought process, I believed the odds grew in my favor each unsuccessful trip the Bills made to the big game. Had I had a little bit stronger mathematical background I may have saved myself a few dollars on the illicit dorm sports betting circuit.
A few areas that led me down the path of financial destitution did not become apparent until later in life. The first major error was in the sample size. At the time of the first appearance there had only been 25 Super Bowls played. With 16 teams attempting to fill two spots each year the odds are slim to make the big game. Adding into that calculation is the assumption that the teams all have a fair chance to win. That leads to my ultimate failure in my betting theories. According to the law of large numbers when you are flipping a coin, the coin must be fair (Lind, D. A., Marchal & Wathen, 2017). In the Super Bowl there is no law saying the teams must be evenly matched. For the Bills to have a chance for the law of averages to pan out the teams needed to be evenly matched. Unfortunately for the Bills and I that was not the case.
References
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics (17th ed.). Retrieved from http://connect.mheducation.com/class/
2. Ibrahima Ba
ThursdayDec 19 at 7:25pm
Quantitative data is defined as the value of data in the form of counts where each data set has a unique numerical value associated with it. The data is used in that matter for any quantifiable information that can be used for calculation and statistical analysis. According to Lind, Marchal, and Wathen (2017), the difference between qualitative and quantitative variables is that qualitative variables are not numerical, nominal and ordinal data fall under this category, while quantitative variables are numerical. Quantitative research gathers a range of numeric data. Some of the numeric data can be intrinsically quantitative, while in other cases the numeric structure is imposed. The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data. Quantitative includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. To present quantitative data analysis, few graphical methods can be employed including histogram, frequency polygon, cumulative frequency distribution, and cumulative relative frequency distribution. It is very important nowadays to have the ability to analyze both quantitative and qualitative data to get a competitive edge in a wide variety of careers.
References
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics (17th ed.). Retrieved from http://connect.mheducation.com/class/
3. Carson Hale
ThursdayDec 19 at 6:21pm
I found a really interesting study performing a quantitative risk analysis for parts failure in extreme conditions of the Arctic. The goal of the study was to look into the fail rate of components in offshore Arctic regions. Many of the components used in offshore operations fail prematurely due to harsh environmental conditions. Components in the Arctic often are put under a greater degree of stress leading to failure rates much higher that warmer climates (Yele & Barabadi, 2017). A quantitative risk analysis is typically compiled by taking reliability and maintainability and past experience to determine level of risk. Since there is little data on reliability and past performance, the study used high stress tests such as the accelerated life test (ALT) to create quantitative data for analysis.
The ALT simulates stressors to provide life like results for risk analysis. The study gathered data from two different simulations the Arrhenius model and the Erying model (Yele & Barabadi, 2017). The Arrhenius model is used to determine how temperature effects time to fail and the Erying is used to demonstrate temperatures as the primary stressor. The risk analysis derived from the data under ALT allowed verification of design margins and safety levels.
FridayDec 20 at 5:15pm
When I worked at DHL Supply Chain as a Production Coordinator, I used to conduct along with the Operation Management team, Material Requirement Planning (MRP) whenever a new product is being implemented. In fact, MRP is a production planning, scheduling, and inventory control system that is used to manage the manufacturing process. When conducting an MRP, you can notice the disruption of orders, unexpected losses, shortages, and shipment delays. The advantage of an MRP is also to make sure there is a smooth flow to the mainline of production so that products are assembled on time and orders released to customers ( Krajewski, Ritzman & Malhotra 2013). In this manner, a quantitative analysis of the Supply Chain helps the company to improve quality of service, to reduce excess stocks and write-offs, to boost productivity, to lower purchase prices and operating costs
At the end of the report, we present flow charts of every product and its production flow. In fact, the flow chart is in this particular case the best visual method for dividing a large and sometimes complicated procedure into several clear segments. I present the flow charts to our Production Manager on a face to face meeting and discuss the relationships between different areas of work and where more attention is needed. For example, a visual of the Bill of materials, Inventory records and workforce will be presented. Following this meeting, job orders are given to designated departments and new goals are set. With detailed documents and flow charts presented to the manager in a PowerPoint presentation, the company has all the information needed to either change its ways or maintain it.
Krajewski, L.J, Ritzman, L., P., & Malhotra, M.K.(2013). Operations management process and supply chains
(3rd Custom Edition). Upper Saddle River, NJ: Prentice-Hall.
