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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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