Transcript weird_259_2007

Weird and Normal
Homework
• Two people asked about the “correct” bin
size or number of bins for a histogram.
There are many algorithms but in general,
aim for 7 +/- 2. This is not a rule, just my
general suggestion.
Weird
• If you measure the height of a randomly
selected woman at Stanford, you could get
a woman who was 5’11”. It would be a bit
weird but not too odd. If you were to get a
sample of 10 random women and get an
average of 5’11”, it would be very weird.
• You want a method to quantify how
unusual that is.
Quantifying Weird
• To quantify that oddity you need to know one
thing. What is the pattern of the heights in the
population?
• You could describe that with an empirical
distribution (look where the data falls in a
histogram of real data) or with a theoretical
distribution (look where the data falls in
mathematical density). In either case you just
want to come up with a way to describe a
histogram of the data if you were looking at the
population.
Density
• There are a few commonly used density
functions to describe the patterns in the data.
Density functions you will care about are the
Poisson (for describing count data) and the
exponential (for describing survival), but
arguably the most important is the Gaussian,
aka Normal, density.
• The easiest way to look at a probability density
(continuous data) or mass function (categorical
data) is to download R and the Rcmdr package.
R stuff
• The closest place to get R is here:
cran.cnr.berkeley.edu/bin/windows/base/release.htm
• Once you get R, install the Rcmdr package (you don’t
need Rcmdr.HH) from USA (CA1) using the
Packages menu.
• Then type library(Rcmdr) and push Enter.
Exploring Distributions
• The tools you will want to use to explore
distributions are clearly labeled:
0.4
Normal Distribution:
= 0,
=1
0.2
0.1
0.0
Density
0.3
The normal is a two parameter
distribution. The mean describes
the location and the standard
deviation describes the shape.
-3
-2
-1
0
x
1
2
3
How does a normal curve work?
• You describe a normal curve using only
two bits of information: the mean and the
standard deviation.
– Location = the mean aka μ
– Shape = the variance aka σ
μ=0
σ=1
μ=0
σ=2
μ=4
σ=1
0.2
0.1
0.0
Density
0.3
0.4
SDs are where the curve goes from convex to concave and back.
-3
-2
-1
0
x
1
2
3
Distributions
• Lots of distributions are based on the formula
y  exp(  x )
m
• Y = exp(-x2) rescaled so the integral (area
under the curve) is one is the normal
distribution.
0.0
0.0
0.2
0.2
0.4
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y
y
0.6
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0.8
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1.0
1.0
0.0
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y
y
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0.6
0.8
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1.0
1.0
m=1
-3
-3
-2
-2
-1
-1
0
0
x
m=2
1
1
2
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-3
-3
-2
-2
-1
-1
0
x
x
m=3
m=4
0
x
1
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3
1
2
3
Moments
• The normal
distribution is
described by those
two parameters but in
real life you may want
to describe data with
several statistics.
y
n
 y  y 
n
2
 y  y 
n
3
Skew
 y  y 
n
4
Kurtosis
1.0
0.8
0.0
0.2
0.2
0.4
0.4
y
y2
0.6
0.6
0.8
Leptokurtosis
-0.792628724459993
1.0
-0.139899436960595
-2
-1
0
1
2
3
-3
-2
-1
0
1
-1.10186607399571
2
3
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Platykurtosis
0.0
0.2
0.4
y4
0.0
0.2
0.4
y3
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1.0
-0.992091841121729
0.8
x
1.0
x
0.8
-3
-3
-2
-1
0
x
1
2
3
-3
-2
-1
0
x
1
2
3
This is really a
probability mass
function because
the values are
counts, therefore
it is discrete.
The Poisson is a one parameter
distribution. The mean describes
the pattern of the counts.
Exponential Distribution: rate = 1
0.4
0.0
Density
0.8
The Exponential is a
one parameter
distribution. The rate
describes the patterns.
0
2
4
x
6
Looking at Your Data
• The bell shaped curve can be drawn so
that the area underneath it has any area
but it is typically tweaked so that its area is
1.
f ( x) 
1
 2
1 x 2
 (
)
2

e
There are only two interesting
parameters here:  and . The
other weird letters are just
constants =3.14159 e=2.71828
Why bother with the math?
• You want a way to describe the likelihoods
(probability) of every possible value that could
be drawn from a population. A mathematically
convenient way to do that is to say the total
probability (which by definition is 1) is
synonymous with the total area. If the area is
equivalent to probability, then it is obvious that a
small area corresponds to a small probability.
• If the area is a score between 0 and 1, then you
can quickly set rules on what is weird. If the
scale is from 0 to 1, then for me, something that
occurs .01 of the time is pretty odd.
In Hieroglyphics



1
2
1 x 2
 (
)
 e 2  dx
1
What is weird?
• No matter what  and  are, the area
between - and + is about 68%, the
area between -2 and +2 is about
95%, and the area between -3 and
+3 is about 99.7%. Almost all values
fall within 3 standard deviations.
• If an observed value is greater than the
mean plus two SDs, that is weird. The
same is true if it is less than the mean
minus two SDs. If it is more extreme than
the mean +/- 3 SD, it is VERY weird.
68% of
the data
95% of the data
99.7% of the data
If you speak math:
 
1
e

   2
1 x 2
 (
)
2 
  2
1 x
 (
1
2

e

  2  2
  3
1
e

 3  2

dx  .68
)2
1 x 2
 (
)
2 
dx  .95
dx  .997
How good is that rule for real data?
In theory, lots of distributions can be well
approximated by a normal distribution. If
something is caused by many independent
factors, look for it to be approximately normally
distributed.
Check some example data:
The mean of the weight of the women = 127.8
The standard deviation (SD) = 15.5
68% of 120 = .68x120 = ~ 82 runners
In fact, 79 runners fall within 1-SD (15.5 lbs) of the mean.
112.3
127.8
143.3
25
20
P
e
r
c
e
n
t
15
10
5
0
80
90
100
110
120
POUNDS
130
140
150
160
95% of 120 = .95 x 120 = ~ 114 runners
In fact, 115 runners fall within 2-SD’s of the mean.
96.8
127.8
158.8
25
20
P
e
r
c
e
n
t
15
10
5
0
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90
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110
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POUNDS
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150
160
99.7% of 120 = .997 x 120 = 119.6 runners
In fact, all 120 runners fall within 3-SD’s of the mean.
81.3
127.8
174.3
25
20
P
e
r
c
e
n
t
15
10
5
0
80
90
100
110
120
POUNDS
130
140
150
160
Working with a Normal
• You are still faced with the question of how
do you determine if a value you observe is
weird (unexpected) given the hypothetical
normal population distribution. To do that,
you just need to map the observed value
into the area on the curve. If it is out in a
range with a very small area, it would
occur rarely.
Example
• Suppose SAT scores roughly follow a
normal distribution in the U.S.
population of college-bound students
(with range restricted to 200-800), and
the average math SAT is 500 with a
standard deviation of 50, then:
– 68% of students will have scores between
450 and 550
– 95% will be between 400 and 600
– 99.7% will be between 350 and 650
0.008
0.006
0.004
0.002
0.000
320
360
400
440
480
520
560
600
640
Example
• BUT… What if you wanted to know the
math SAT score corresponding to the
90th percentile (=90% of students are
lower)?
1 x 500 2
P(X≤Q) = .90  Q
 (
)
1
 (50)
200
2
e
2
50
dx  .90
<math phobic students please don’t implode here>
Common Densities
• Wouldn’t it be nice if somebody had already
worked out what is weird (say > 90%) in a
distribution where the mean is 500 and the SD is
50?
• There are a couple of distributions where people
have actually worked out those values and some
weird people keep the values in their heads.
– The common IQ tests have a mean of 100 and a SD
of 15. Ask a developmental psychologist what score
is like the top 1% and they are likely to know.
A fine trick!
• Instead of relying on your ability to find
some savant who knows the percentiles
describing a density with a mean of 500
and SD of 50, you can convert the values
to a different scale where you know the
percentiles.
• People typically convert the data so that it
is measured in terms of standard
deviations from the mean.
Z (aka zee-score or zed-score)
• The typical conversion is to rescale your data so
the mean is 0 and a standard deviation is 1 unit.
So for the SAT example, a score of 500 is 0 on
the Z scale. A 550 is scored as 1 and 400 is -2
on the Z scale.
• You then become a master on the Z scale and
learn things like -2 to 2 on the scale covers
about 95% of the distribution. It really is about
+/- 1.96.
The Standard Normal (Z):
• The formula for the standardized normal
probability density function is less horrible and
you can easily get the probabilities out of a
statistical package:
1
p( Z ) 
e
(1) 2
1 Z 0 2
 (
)
2 1
1

e
2
1
 ( Z )2
2
The Standard Normal Distribution (Z)
All normal distributions can be converted
into the standard normal curve by
subtracting the mean and dividing by the
standard deviation:
X 
Z

Somebody calculated all the integrals for the standard
normal and put them in a table! So we never have to
integrate!
Comparing X and Z Units
500
0
600
2.0
X
Z
( = 500,  = 50)
( = 0,  = 1)
Example
• For example, what’s the probability of getting a math
SAT score of 575 or less, =500 and =50?
575  500
Z
 1.5
50
i.e., a score of 575 is 1.5 standard deviations above the
mean
575
 P( X  575) 
1
 (50)
200
2
1.5
1 x 500 2
 (
)
 e 2 50 dx 



1
2
1
 Z2
 e 2 dz
<math phobic students, please refrain from exploding here>
Where is that in the density?
• If you have R, it is trivially easy to look it
up:
0.4
Tails?
0.2
0.1
0.0
Density
0.3
About 93% of the
area is below the
1.5 Z mark.
-3
-2
-1
0
Z score
1
2
3
0.004
0.002
0.000
Density
0.006
0.008
Z scores are so last century…
350
400
450
500
Z score
550
600
650
QQ plots
• We have been talking about comparing a
data point against a hypothetical normal
distribution, but data is not really normally
distributed. How do you tell if a sample is
normally distributed? First and foremost,
you plot your values against a hypothetical
distribution.
Checking for Normality
You are good unless the
p-values here get small.
These tests are too
sensitive for many uses
(because of the CLT).
350
400
450
500
550
theScores$rnorm.1...100..mean...500..sd...50.
600
QQ with
Rcmdr
-2
-1
0
norm quantiles
1
2
CDF, PDF, Density
• There are four common ways of looking at
a distribution.
• If you start on the left side of a bell shaped
curve, you can draw how likely a value is
to be at or less than that value (the CDF)
or you can measure how likely a value is
at that location (sort of).
-2
0.0
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-1
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1
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-2
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z
1
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Quantile (Z)
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Probability
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-3
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-1
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z
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p
1
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3
z
1.0
0.0
0
100
150
0.1
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Probability density
50
frequency
0.4
Size Matters
• When you take a sample of size 1 from a
normally distributed population, you are unlikely
to get a sample mean which is far from the
population mean. If you have a sample size of
5, you are less likely to get a sample mean far
from the population mean. If you have a sample
size of 30, you are very unlikely to get a sample
mean from the population.
• How unlikely is that?
Sampling Distribution
• You need to know what the distribution of the
samples looks like. Intuitively, you know that
you are not likely to get very many samples, say,
of 30 people, where the sample mean (e.g.,
observed score is 600) is far from the population
mean (population mean is 500).
• As it happens, the means from the samples will
cluster around the population mean.
1500
1000
500
0
Frequency
2000
2500
Actual scores
300
400
500
scores
600
700
400
200
0
Frequency
600
800
Bunch of Means
300
400
500
bunchOfMeans
600
700
1500
0 500
Frequency
Actual Scores
300
400
500
600
700
600
700
600
700
scores
600
200
0
400
500
bunchOfMeans
200
600
Bunch of Means sample N = 20
0
The distribution of
the means from
sample size of 20 is
narrower still (and
bell-shaped).
300
Frequency
The distribution of
the means from
sample size of 5 is
narrower than the
original values (and
bell shaped).
Frequency
Bunch of Means sample N = 5
300
400
500
bunchOfMeans20
Sampling Distributions
• You can therefore describe the sampling
distributions as a bell shaped curve and
furthermore, use your knowledge about
the normal distribution to conclude what is
a weird sample. You just use the same
logic as you used with a single value.
Not Normal
• What happens when you calculate means
from a population distribution that does not
look normal?
Doors (hurt)
• This is a distribution describing the count
of hard things I walk into in a typical day.
On average I walk into about .7 doorframes a day.
4000
2000
0
Frequency
6000
Histogram of counts
0
2
4
counts
6
Sampling from a Poisson Distribution
• If you take a sample count of the number
of things I walk into in a day, you are not
likely to get a lot of days with 4 doors in 1
day. If you sample 5 random days and
calculate the mean, you could get an
average of me walking into 4 things but it
would be darn weird…
• Look at the means taken from 5000
samples of 5 days picked out at random.
Histogram of Means
2500
2000
1500
1000
500
Note this is drawn on the
original scale.
0
Frequency
As expected, you don’t see
many (if any) samples
where I walked into an
average of 4 things across 5
randomly selected days.
3000
Histogram of bunchOfMeans
0
2
4
bunchOfMeans
6
Relax the
Scale
600
400
200
0
Frequency
Look at the
pattern in the
means! It
looks sort of
kind of like a
normal
distribution!
800
1000
Histogram of bunchOfMeans
0.0
0.5
1.0
bunchOfMeans
1.5
2.0
600
0 200
Frequency
1000
Histogram of the means N = 5
0.0
0.5
1.0
1.5
bunchOfMeans
600
0 200
Frequency
1000
Histogram of the means N = 15
0.0
0.5
1.0
1.5
bunchOfMeans15
0
400
800
1200
Histogram of the means N = 30
Frequency
That looks like a
normal
distribution.
0.0
0.5
1.0
bunchOfMeans30
1.5
Weird and Normal
• The weird fact that the distribution of sample
means is shaped like a normal curve
irrespective of the original distribution (if your
sample size is big enough), is called the Central
Limit Theorem (CLT).
• If you can describe sample means using a bell
shaped curve, you can describe what is a weird
sample! You’re just using the same simple logic
as when you compare one person vs. a
population distribution.