Transcript Statistics - Kellogg School of Management

Managerial Statistics
Why are we all here?
In a classroom, near the beginning of an
executive M.B.A. program in management,
getting ready to start a course on …
The job of a manager is to make …
decisions.
What’s so hard about that?
Fundamental Fact of Life
causal relationships
the things we
really care about
are typically
NOT
the things we
directly control
So, why don’t we just give up?
What makes life worth living?
FAITH!
This Course
• … is focused on a single statistical tool for
studying relationships:
– Regression Analysis
• That said, we won’t use this tool until we
reach the second section of the course.
• First, we need to be comfortable with the two
“languages” of statistics
– The language of estimation (“trust”)
– The language of hypothesis testing (“evidence”)
Our Four Sessions Together
1. The ideas underlying statistics, and the two
languages of statistics
2. The “science” of regression analysis (how to
use the tool)
3. The “art” of regression analysis (regression
modeling – how to use the tool wisely and
well)
4. More modeling
What is “Statistics”?
Statistics is focused on making inferences about a group of
individuals (the population of interest) using only data
collected from a subgroup (the sample).
Why might we do this?
Perhaps …
• the population is large, and looking at all individuals would
be too costly or too time-consuming
• taking individual measurements is destructive
• some members of the population aren’t available for
direct observation
Managers aren’t Paid to be Historians
Their concern is how their decisions will play out in the future.
Still, if the near-term future can be expected to be similar to the
recent past, then the past can be viewed as a sample from a
larger population consisting of both the recent past and the
soon-to-come future.
The sample gives us insight into the population as a whole, and
therefore into whatever the future holds in store.
Indeed, even if you stand in the middle of turbulent times, data
from past similarly turbulent times may help you find the best
path forward.
How is Statistics Done?
Any statistical study consists of three
specifications:
• How will the data be collected?
• How much data will be collected in this way?
• What will be computed from the data?
Running example: Estimating the average age across a
population, in preparation for a sales pitch.
1. How Will the Data be Collected?
Primary Goals:
No bias
High precision
Low cost
• Simple random sampling with replacement
– Typically implemented via systematic sampling
• Simple random sampling without replacement
– Typically done if a population list is available
• Stratified sampling
– Done if the population consists of subgroups with relative
within-group homogeneity
• Cluster sampling
– Done if the population consists of (typically geographic)
subgroups with substantial within-group heterogeneity
2. How is the Sample Size Chosen?
• In order to yield the desired (target) precision
(to be made clearer in a while)
• simple random sampling with replacement
• sample size of 5
3. What Will be Done with the Data?
Some possible estimates of the population mean
from the five observations:
median (third largest)
average of extremes ( [largest + smallest] / 2)
sample mean (x = (x1+x2+x3+x4+x5)/5)
smallest (probably not a very good idea)
We’ve Finally Chosen an Estimation
Procedure!
• simple random sampling with replacement
• sample size of 5
• our estimate of the population mean will be
the sample mean, x = (x1+x2+x3+x4+x5)/5
This will certainly give us an estimate.
But how much can we trust that estimate???
The Fundamental Idea underlying All
of Statistics
At the moment I decide how I’m going to make
an estimate, if I look into the future, the (not yet
determined) end result of my chosen estimation
procedure looks like a random variable.
Using the tools of probability, I can analyze this
random variable to see how precise my ultimate
(after the procedure is carried out) estimate is
likely to be.
Some Notation
population
sample
size, N
size, n
mean, 
sample mean, x
standard deviation, ,
where 2=∑(xi-)2 / N
sample standard deviation, s,
where s2=∑(xi- x)2 / (n-1)
For Our Estimation Procedure, with
X Representing the End Result
• E[X] = 
our procedure is right, on average
• StDev(X) = /n
if this is small, our procedure typically
gives an estimate close to 
• X is approximately normally distributed
(from the Central Limit Theorem)
Pulling This All Together, Here’s the
“Language” of Estimation
“I conducted a study to estimate {something} about
{some population}. My estimate is {some value}.
The way I went about making this estimate, I had {a
large chance} of ending up with an estimate within
{some small amount} of the truth.”
For example, “I conducted a study to estimate the
mean amount spent on furniture over the past year
by current subscribers to our magazine. My
estimate is $530. The way I went about making this
estimate, I had a 95% chance of ending up with an
estimate within $36 of the truth.”
Pictorially
For Simple Random Sampling with
Replacement
“I conducted a study to estimate , the mean value
of something that varies from one individual to the
next across the given population.
“My estimate is x. The way I went about making this
estimate, I had a 95% chance of ending up with an
estimate within 1.96·/n of the truth.
“(And the other 5% of the time, I’d typically be off
by only slightly more than this.)”
There’s Only One Problem …
We don’t know  ! So we cheat a bit, and use s
(an estimate of  based on the sample data)
instead.
And so …
Our estimate of  is x, and the margin of error
(at the 95%-confidence level) is 1.96·s/n .
And That’s It!
• We can afford to standardize our language of
"trust" around the notion of 95% confidence,
because translations to other levels of confidence
are simple. The following statements are totally
synonymous:
• I'm 90%-confident that my estimate is wrong by
no more than $29.61. (~1.64)·s/√n
• I'm 95%-confident that my estimate is wrong by
no more than $35.28. (~1.96)·s/√n
• I'm 99%-confident that my estimate is wrong by
no more than $46.36. (~2.58)·s/√n
Next
• Why should a manager want to know the
margin of error in an estimate?
• Some necessary technical details
• Polling (estimating the proportion of the
population with some qualitative property)
• The language of hypothesis testing (evaluating
evidence: to what extent does data support or
contradict a statement?)
The Language of Estimation (for Simple
Random Sampling with Replacement)
the standard error of the mean
(one standard-deviation’s-worth of
exposure to error when estimating
the population mean)
the margin of error (implied, unless
otherwise explicitly stated: at the
95%-confidence level) when the
sample mean is used as an
estimate of the population mean
a 95%-confidence interval for the
population mean μ
s
n
s
1.96 
n
s
x  1.96 
n
Advertising Sales
A magazine publishing house wishes to estimate (for purposes of
advertising sales) the average annual expenditure on furniture among
its subscribers.
A sample of 100 subscribers is chosen at random from the 100,000person subscription list, and each sampled subscriber is questioned
about their furniture purchases over the last year. The sample mean
response is $530, with a sample standard deviation of $180.
s
x  1.96
n
$180
$530 1.96
100
$530  $36
To whom, and where, is the $36 margin of error of relevance?
Put Yourself in the Shoes of the Marketing
Manager at a Furniture Company
Part of your job is to track the performance of current
ad placements. Each month …
• You apportion sales across all the placements.
• You divide sales by placement costs.
• You rank the placements by “bang per buck.”
The lowest ranked placement is at the top of your
replacement list, and its ratio determines the hurdle a
new opportunity must clear to replace it.
Keep Yourself in the Shoes of the Marketing
Manager at the Furniture Company
Another part of your job is to learn the relationship
between properties of specific ad placements, and the
performance of those placements.
• You do this using regression analysis, with the
characteristics of, and return on, previous placements as
your sample data.
Given the characteristics of a new opportunity (e.g.,
number of subscribers to a magazine, and how much the
average subscriber spends on furniture in a year), you can
predict the likely return on your advertising dollar if you
take advantage of this opportunity.
One Day, the Advertising Sales
Representative for a Magazine Drops By
S/he wants you to buy space in this magazine.
You ask (among other things), “What’s the average
amount your subscribers spend on furniture per year?”
S/he says, “ $530 ± $36 ”
You put $530 (and other relevant information) into your
regression model … and it predicts a return greater
than your current hurdle rate!
Do you jump onboard?
What If the $530 is an Over-Estimate
or an Under-Estimate?
The predicted bang-per-buck could actually be worse
than your hurdle rate!
There are many ways to do a risk analysis, and you’ll
discuss them throughout the program. They all require
that you know something about the uncertainty in
numbers you’re using.
At the very least, you can put $494 and $566 into your
prediction model, and see what you would predict in
those cases.
[More generally, (margin-of-error/1.96) is one standard-deviation’s-worth of
“noise” in the estimate. This can be used in more sophisticated analyses.]
Sometimes It’s Right to Say “Maybe”
If the prediction looks good at both extremes, you can
be relatively confident that this is a good opportunity.
If it looks meaningfully bad at either extreme, you delay
your decision:
“Gee! This sounds interesting, but your numbers are a
bit too fuzzy for me to make a decision. Please go back
and collect some more data. If the estimate stands up,
and the margin of error can be brought down, I might
be able to say “Yes.””
Practical Issues
• If it looks good, either now or on a second visit, be
sure to get details on the estimation study in writing
as part of your deal. (Then you can sue for fraud if
you learn the rep was lying.)
• The risk analysis I’ve described is quite simplistic. You
can (and will learn to) do better. But you’ll need the
margin of error for any approach.
General Discussion
How would our answer ($530 ± $36) change, if
there were 400,000 subscribers (instead of
100,000)?
• It wouldn’t change at all! “N” doesn’t appear
in our formulas.
• The precision of our estimate depends on the
sample size, but NOT on the size of the
population being studied.
• This is WONDERFUL!!!
(Continued)
What if there had been only 4,000 subscribers?
• Still no change.
What if there had been only 100 subscribers?
• Still no change.
But wait!
Ahhh!! … Everything we’ve said so far, and the
formulas we’ve derived, are for an estimation
procedure involving simple random sampling
with replacement.
Technical Detail #1
If we’d used simple random sampling without replacement:
• E[Xwo] =  , the procedure is still right on average
Nn
• StDev(Xwo) = (/n)·
N1
: this is somewhat different!
• Xwo is still approximately normally distributed
(from the Central Limit Theorem)
For Simple Random Sampling
without Replacement
s
Nn
x  1.96

n N1
But for typical managerial settings, this extra factor is
just a hair less than 1. For example, if N = 100,000 and
n = 100, the factor is 0.9995.
So in managerial settings the factor is usually ignored,
and we’ll use
s
x  1.96
n
for both types of simple random sampling.
Technical Detail #2
s
In coming up with x  1.96
n
, we cheated … twice!
• We invoked the Central Limit Theorem to get the 1.96,
even though the CLT only says, “The bigger the bunch of
things being aggregated, the closer the aggregate will
come to having a normal distribution.”
– As long as the sample size is a couple of dozen or more, OR
even smaller when drawn from an approximately normal
population distribution, this cheat turns out to be relatively
innocuous.
• We used s instead of .
– This cheat is a bit more severe when the sample size is small.
So we cover for it by raising the 1.96 factor a bit.
Very Technical Detail #2
By how much do we lift the 1.96 multiplier?
To a number that comes from the t-distribution
with n-1 “degrees of freedom.”
This adjusts for using estimates of variability
(such as s) instead of the actual variability (such
as ), and for deriving these estimates from the
same data already used to estimate other things
(such as x for ).
Correcting for Using s Instead of 
t-distribution
95%
95%
95%
degrees of
degrees of
degrees of
central
central
central
freedom
freedom
freedom
probability
probability
probability
1
12.706
11
2.201
21
2.080
2
4.303
12
2.179
22
2.074
3
3.182
13
2.160
23
2.069
4
2.776
14
2.145
24
2.064
5
2.571
15
2.131
25
2.060
6
2.447
16
2.120
30
2.042
7
2.365
17
2.110
40
2.021
8
2.306
18
2.101
60
2.000
9
2.262
19
2.093
120
1.980
10
2.228
20
2.086
∞
1.960
Note that, as the sample size grows, the correct “approximately 2”
multiplier becomes closer and closer to 1.96.
Pictorially
And How Do We Do This?
Fortunately, any decent statistical software these days will count
degrees of freedom, look in the appropriate t-distribution tables,
and give us the slightly-larger-than-1.96 number we should use.
In general, just think
(your estimate) ± (~2) ·
(one standard deviation’s worth of uncertainty
in the way the estimate was made)
as in
x  (~ 2) 
s
n
where the (~2) is determined
by the computer
Polling
If the individuals in the population differ in some
qualitative way, we often wish to estimate the
proportion / fraction / percentage of the population
with some given property.
For example: We track the sex of purchasers of our
product, and find that, across 400 recent
purchasers, 240 were female. What do we estimate
to be the proportion of all purchasers who are
female, and how much do we trust our estimate?
First, the Estimate
Let
240
pˆ 
 0.6  60% .
400
Obviously, this will be our estimate for the
population proportion.
But how much can this estimate be trusted?
And Now, the Trick
Imagine that each woman is represented by a
“1”, and each man by a “0”.
Then the proportion (of the sample or
population) which is female is just the mean of
these numeric values, and so estimating a
proportion is just a special case of what we’ve
already done!
The Result
Estimating a mean:
s
x  (~ 2) 
n
Estimating a proportion:
pˆ (1 - pˆ )
pˆ  (~ 2)
n
[When all of the numeric values are either 0 or 1, s takes the
special form shown above.]
0.6(1- 0.6)
The example: 0.6  (~ 2)
, or 60%  4.8% .
400
Multiple-Choice Questions
If the Republican Party’s candidate were to be
chosen today, which one would you most prefer?
• Romney, Cain, Bachman, Perry, Gingrich,
Santorum, Paul, Huntsman, none
The results are reported as if 9 separate “yes/no”
questions had been asked.
If the Republican Party’s candidate were to be
chosen today, which of these would have your
approval?
The same reporting method is used.
Choice of Sample Size
• Set a “target” margin of error for your
estimate, based on your judgment as to how
small will be small enough for those who will
be using the estimate to make decisions.
• There’s no magic formula here, even though
this is a very important choice: Too large, and
your study is useless; too small, and you’re
wasting money.
Estimating a Proportion: Polling
Pick the target margin of error.
• Why do news organizations always use 3% or
4% during the election season?
– Because that’s the largest they can get away with.
pˆ (1 - pˆ )
0.5(1- 0.5) 1
(~ 2)
 (~ 2)

n
n
n
So, for example, n=400 (resp., 625, or 1112) assures a
margin of error of no more than 5% (resp., 4%, or 3%).
Estimating a Mean: Choice of Sample
Size
Set the target margin of error.
s
 t arget
• Solve (~ 2) 
n
target = $25.
s  $180.
Set n = 207.
From whence comes s?
• From historical data (previous studies) or from
a pilot study (small initial survey).
The “Square-Root” Effect : Choice of
Sample Size after an Initial Study
• Given the results of a study, to cut the margin
of error in half requires roughly 4 times the
original sample size.
• And generally, the sample size required to
achieve a desired margin of error =
2
 originalmarginof error 

  originalsamplesize
 desiredm argin of error
How to Read Presidential-Race Polls
• When reading political polls, remember that
the margin of error in an estimate of the “gap”
between the two leading candidates is roughly
twice as large as the poll's reported margin of
error.
• The margin of error in the estimated “change
in the gap” from one poll to the next is nearly
three times as large as the poll's reported
margin of error.
Summary
• Whenever you give an estimate or prediction to someone, or accept an
estimate or prediction from someone, in order to facilitate risk analysis
be sure the estimate is accompanied by its margin of error:
A 95%-confidence interval is
(one standard-deviation’s-worth of uncertainty
(your estimate) ± (~2) · inherent in the way the estimate was made)
• If you’re estimating a mean using simple random sampling:
s
x  (~ 2) 
n
• If you’re estimating a proportion using simple random sampling:
pˆ (1 - pˆ )
pˆ  (~ 2)
n
Hypothesis Testing
A statement has been made. We must decide
whether to believe it (or not). Our belief decision
must ultimately stand on three legs:
• What does our general background knowledge
and experience tell us (for example, what is the
reputation of the speaker)?
• What is the cost of being wrong (believing a false
statement, or disbelieving a true statement)?
• What does the relevant data tell us?
Making the “Belief” Decision
• What does our general background knowledge and
experience tell us (for example, what is the reputation of the
speaker)? – The answer is typically already in the manager’s
head.
• What is the cost of being wrong (believing a false statement,
or disbelieving a true statement)? – Again, the answer is
typically already in the manager’s head.
• What does the relevant data tell us? – The answer is typically
not originally in the manager’s head. The goal of hypothesis
testing is to put it there, in the simplest possible terms.
• Then, the job of the manager is to pull these three answers
together, and make the “belief” decision. The statistical
analysis contributes to this decision, but doesn’t make it.
Our Goal is Simple:
• To put into the manager’s head a single phrase
which summarizes all that the data says with
respect to the original statement.
“The data, all by itself, makes me ________ suspicious, because
the data, all by itself, contradicts the statement ________ strongly.”
{not at all, a little bit, moderately, quite, very, overwhelmingly}
• We wish to choose the phrase which best fills the
blanks.
What We Won’t Do
• Compute Pr(statement is true | we see this data).
(This depends on our prior beliefs, instead of just on the
data. It requires that we pull those beliefs out of the
manager’s head.)
What We Will Do
• Compute Pr(we see this data | statement is true).
This depends just on the data. Since we don’t expect to
see improbable things on a regular basis, a small value
makes us very suspicious.
This is Analogous to the British System
of Criminal Justice
• The statement on trial – the so-called “null
hypothesis” – is that “the accused is innocent.”
• The prosecution presents evidence.
• The jury asks itself: “How likely is it that this
evidence would have turned up, just by chance, if
the accused really is innocent?”
• If this probability is close to 0, then the evidence
strongly contradicts the initial presumption of
innocence … and the jury finds the accused
“Guilty!”
Example: Processing a Loan Application
• You’re the commercial loan officer at a bank, in the process of reviewing a
loan application recently filed by a local firm. Examining the firm’s list of
assets, you notice that the largest single item is $3 million in accounts
receivable. You’ve heard enough scare stories, about loan applicants
“manufacturing” receivables out of thin air, that it seems appropriate to
check whether these receivables actually exist. You send a junior
associate, Mary, out to meet with the firm’s credit manager.
• Whatever report Mary brings back, your final decision of whether to
believe that the claimed receivables exist will be influenced by the firm’s
general reputation, and by any personal knowledge you may have
concerning the credit manager. As well, the consequences of being wrong
– possibly approving an overly-risky loan if you decide to believe the
receivables are there and they’re not, or alienating a commercial client by
disbelieving the claim and requiring an outside credit audit of the firm
before continuing to process the application, when indeed the receivables
are as claimed – will play a role in your eventual decision.
Processing a Loan Application
• Later in the day, Mary returns from her meeting. She reports that the
credit manager told her there were 10,000 customers holding credit
accounts with the firm. He justified the claimed value of receivables by
telling her that the average amount due to be paid per account was at
least $300.
• With his permission, she confirmed (by physical measurement) the
existence of about 10,000 customer folders. (You decide to accept this
part of the credit manager’s claim.) She selected a random sample of 64
accounts at random, and contacted the account-holders. They all
acknowledged soon-to-be-paid debts to the firm. The sample mean
amount due was $280, with a sample standard deviation of $120.
• What do we make of this data? It contradicts the claim to some extent,
but how strongly? It could be that the claim is true, and Mary simply came
up with an underestimate due to the randomness of her sampling (her
“exposure to sampling error”).
Compute, then Interpret
• What do we make of Mary’s data? We answer this question in two
steps. First, we compute Pr(we see this data | statement is true). More
precisely:
Pr (
conducting a study such as we
just did, we’d see data at least
as contradictory to the
statement as the data we are,
in fact, seeing
|
the statement is true, in
a way that fits the
observed data as well as
possible
)
• This number is called the significance level of the data, with respect to the
statement under investigation (i.e., with respect to the null hypothesis).
(Some authors/software call this significance level the “p-value” of the
data.)
• Then, we interpret the number: A small value forces us to say, “Either the
statement is true, and we’ve been very unlucky (i.e., we’ve drawn a very
misrepresentative sample), or the statement is false. We don’t typically
expect to be very unlucky, so the data, all by itself, makes us quite
suspicious.”
Null Hypothesis: “≥$300”
• Mary’s sample mean is $280. Giving the
original statement every possible chance of
being found “innocent,” we’ll assume that
Mary did her study in a world where the true
mean is actually $300.
• Let X be the estimate Mary might have
gotten, had she done her study in this
assumed world.
The significance level of Mary’s data, with
respect to the null hypothesis: “ ≥ $300”, is
Pr(X  $280|μ  $300)  9.36%
The probability that
Mary’s study would
have yielded a
sample mean of $280
or less, given that her
study was actually
done in a world
where the true
mean is $300.
 = $300
s/n = $120/64 = $15
the t-distribution
with 63 degrees
of freedom
9.36%
X
The “Hypothesis Testing Tool”
• The spreadsheet “Hypothesis_Testing_Tool.xls,” in the “Session 1”
folder, does the required calculations automatically.
From the sample data, fill in the yellow boxes below:
280
15
64
0
estimate/prediction of unknown quantity
measure of uncertainty
sample size
number of explanatory variables in regression, or
0 if dealing with a population mean
significance level of data with
respect to null hypothesis
Null hypothesis:
true value
≥
=
≤
300
9.3612%
18.7224%
100.0000%
(from t-distribution with 63 degrees of freedom)
And now, how do we interpret “9.36%”?
Coin_Tossing.htm
numeric significance
level of the data
interpretation: the data,
all by itself, makes us
the data supports the
alternative hypothesis
above 20%
not at all suspicious
not at all
between 10% and 20%
a little bit suspicious
a little bit
between 5% and 10%
moderately suspicious
moderately
between 2% and 5%
very suspicious
strongly
between 1% and 2%
extremely suspicious
very strongly
below 1%
overwhelmingly suspicious incredibly strongly
Processing a Loan Application
• So the data, all by itself, makes us “a bit
suspicious.” What do we do?
• It depends.
– If the credit manager is a trusted lifelong friend …
– If the credit manager is already under suspicion …
• What if Mary’s sample mean were $260?
– With a significance level of 0.49%, the data, all by
itself, makes us “overwhelmingly suspicious” …
The One-Sided Complication
• A jury never finds the accused “innocent.”
– For example, if the prosecution presents no
evidence at all, the jury simply finds the accused
“not proven to be guilty.”
• Just so, we never conclude that data supports
the null hypothesis.
– However, if data contradicts the null hypothesis,
we can conclude that it supports the alternative.
So, If We Wish to Say that Data
Supports a Claim …
• We take the opposite of the claim as our null
hypothesis, and see if the data contradicts that
opposite. If so, then we can say that the data
supports the original claim.
• Examples:
– A clinical test of a new drug will take as the null
hypothesis that patients who take the drug are
equally or less healthy than those who don’t.
– An evaluation of a new marketing campaign will take
as the null hypothesis that the campaign is not
effective.
And That’s It!
• With the languages of estimation and
hypothesis testing in place, it’s time to learn
REGRESSION ANALYSIS!