Bus308 Statistics for managers

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orgunal1973

Mathematics

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Language Discussion 1: Language Numbers and measurements are the language of business. Organizations look at results in many ways: expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of? Are they descriptive or inferential data, and what is the difference between these? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples, or conduct outside research on an interest of yours, or use personal measures.) Guided Response: Review several of your classmates’ posts. Respond to at least two of your classmates by providing recommendations for the measures being discussed Probability Read the article, "Better Living Through...Statistics?!" and give an example of how you might use increasing information to make actual business decisions. Respond to at least two of your classmates’ posts. Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on the situations that are being illustrated. Better Living Through...Statis tics?! Comment Now Follow Comments You’ve probably heard of Nate Silver. He’s the “King of Quants,” and his book The Signal and the Noise is an excellent discussion of some of the problems we have with prediction. You’ve probably never heard of the Reverend Thomas Bayes, who is responsible for a theorem (called “Bayes’ Theorem”) that helps us understand how we can update our estimates of the probabilities of different events given new pieces of information. It’s still pretty counter-intuitive. Fortunately, the people at Nowsourcing, Inc, who have provided content for this space before, were kind enough to produce the infographic below that introduces Bayes’ Theorem with a contrived example involving baseball: what’s a good estimate of the probability that the Yankees will win game #101 if they have won 72 of their first 100 games and Sportscaster Bob–who is correct 55% of the time when he predicts a Yankees victory–has predicted that they will win? Since the Yankees have won 72 of 100 games, a good estimate of the probability that they will win their 101st game would be 72%. Now, we introduce some information: since Bob is right just over half the time when he predicts a Yankees victory, it will nudge our estimate of the probability of a Yankees victory up just a little bit (if Sportscaster Bob were right less than half the time, it would nudge our estimate of the probability downward). Our estimate of the probability changes as we add more information. Is it a night game? Who are the Yankees playing? Who is pitching? Did it rain last night? Is a key player injured? And so on: the more accurate information we add, the better our estimates will be. The applications are numerous and important: while Bayesian reasoning can help us understand baseball (except for the Yankees’ hypothetical 72-28 record in this example), it also helps us understand far more important things like medical diagnostics. And elections. And all sorts of other interesting things.
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Anonymous
Excellent resource! Really helped me get the gist of things.

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