© 1998, geoff kuenning introduction to statistics concentration on applied statistics statistics...
TRANSCRIPT
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© 1998, Geoff Kuenning
Introduction to Statistics
• Concentration on applied statistics• Statistics appropriate for measurement• Today’s lecture will cover basic
concepts– You should already be familiar with
these
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© 1998, Geoff Kuenning
Independent Events
• Occurrence of one event doesn’t affect probability of other
• Examples:– Coin flips– Inputs from separate users– “Unrelated” traffic accidents
• What about second basketball free throw after the player misses the first?
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© 1998, Geoff Kuenning
Random Variable
• Variable that takes values with a specified probability
• Variable usually denoted by capital letters, particular values by lowercase
• Examples:– Number shown on dice– Network delay
• What about disk seek time?
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© 1998, Geoff Kuenning
Cumulative Distribution Function (CDF)
• Maps a value a to probability that the outcome is less than or equal to a:
• Valid for discrete and continuous variables• Monotonically increasing• Easy to specify, calculate, measure
F a P x ax ( ) ( )
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© 1998, Geoff Kuenning
CDF Examples
• Coin flip (T = 1, H = 2):
• Exponential packet interarrival times:
0
0.5
1
0 1 2
0
0.5
1
0 1 2 3
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© 1998, Geoff Kuenning
Probability Density Function (pdf)
• Derivative of (continuous) CDF:
• Usable to find probability of a range:
f xdF x
dx( )
( )
P x x x F x F x
f x dxx
x
( ) ( ) ( )
( )
1 2 2 1
1
2
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© 1998, Geoff Kuenning
Examples of pdf
• Exponential interarrival times:
• Gaussian (normal) distribution:
01
0 1 2 3
0
1
0 1 2
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© 1998, Geoff Kuenning
Probability Mass Function (pmf)
• CDF not differentiable for discrete random variables
• pmf serves as replacement: f(xi) = pi where pi is the probability that x will take on the value xi
P x x x F x F x
pix x xi
( ) ( ) ( )1 2 2 1
1 2
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© 1998, Geoff Kuenning
Examples of pmf
• Coin flip:
• Typical CS grad class size:0
0.5
1
00.10.20.30.40.5
4 5 6 7 8 9 10 11
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© 1998, Geoff Kuenning
Expected Value (Mean)
• Mean
• Summation if discrete• Integration if continuous
E x p x xf x dxi ii
n
( ) ( )1
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© 1998, Geoff Kuenning
Variance & Standard Deviation
• Var(x) =
• Usually denoted 2• Square root is called standard deviation
E x p x
x f x dx
i ii
n
i
[( ) ] ( )
( ) ( )
2 2
1
2
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© 1998, Geoff Kuenning
Coefficient of Variation (C.O.V. or C.V.)
• Ratio of standard deviation to mean:
• Indicates how well mean represents the variable
C.V.
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© 1998, Geoff Kuenning
Covariance
• Given x, y with means x and y, their covariance is:
– typos on p.181 of book• High covariance implies y departs from
mean whenever x does
Cov( , ) [( )( )]
( ) ( ) ( )
x y E x y
E xy E x E y
xy x y
2
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© 1998, Geoff Kuenning
Covariance (cont’d)
• For independent variables,E(xy) = E(x)E(y)
so Cov(x,y) = 0• Reverse isn’t true: Cov(x,y) = 0 doesn’t
imply independence• If y = x, covariance reduces to variance
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© 1998, Geoff Kuenning
Correlation Coefficient
• Normalized covariance:
• Always lies between -1 and 1• Correlation of 1 x ~ y, -1
Correlation( , )x y xyxy
x y
2
xy
~1
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© 1998, Geoff Kuenning
Quantile
• x value at which CDF takes a value is called a-quantile or 100-percentile, denoted by x.
• If 90th-percentile score on GRE was 1500, then 90% of population got 1500 or less
P x x F x( ) ( )
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© 1998, Geoff Kuenning
Quantile Example
0
0.5
1
1.5
0 2
-quantile0.5-quantile
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© 1998, Geoff Kuenning
Median
• 50-percentile (0.5-quantile) of a random variable
• Alternative to mean• By definition, 50% of population is sub-
median, 50% super-median– Lots of bad (good) drivers– Lots of smart (stupid) people
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© 1998, Geoff Kuenning
Mode
• Most likely value, i.e., xi with highest probability pi, or x at which pdf/pmf is maximum
• Not necessarily defined (e.g., tie)• Some distributions are bi-modal (e.g.,
human height has one mode for males and one for females)
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© 1998, Geoff Kuenning
Examples of Mode
• Dice throws:
• Adult human weight:
0
0.1
0.2
2 3 4 5 6 7 8 9 10 11 12
Mode
Mode
Sub-mode
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© 1998, Geoff Kuenning
Normal (Gaussian) Distribution
• Most common distribution in data analysis
• pdf is:
• -x +• Mean is , standard deviation
f x ex
( )( )
1
2
2
22
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© 1998, Geoff Kuenning
Notationfor Gaussian Distributions
• Often denoted N(,)• Unit normal is N(0,1)• If x has N(,), has N(0,1)
• The -quantile of unit normal z ~ N(0,1) is denoted zso that
x
Px
z P x z( ) ( )
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© 1998, Geoff Kuenning
Why Is GaussianSo Popular?
• If xi ~ N(,) and all xi independent, then ixi is normal with mean ii and variance i
2i2
• Sum of large no. of independent observations from any distribution is itself normal (Central Limit Theorem) Experimental errors can be modeled
as normal distribution.
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© 1998, Geoff Kuenning
Central Limit Theorem
• Sum of 2 coin flips (H=1, T=0):
• Sum of 8 coin flips:
0
0.5
1
0 1 2
0
0.1
0.2
0.3
0 1 2 3 4 5 6 7 8
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© 1998, Geoff Kuenning
Measured Data
But, we don’t know F(x) – all we have is a bunch of observed values – a sample.
What is a sample?– Example: How tall is a human?
• Could measure every person in the world (actually even that’s a sample)
• Or could measure every person in this room
– Population has parameters
– Sample has statistics• Drawn from population
• Inherently erroneous
Measured Data
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© 1998, Geoff Kuenning
Central Tendency
• Sample mean – x (arithmetic mean)– Take sum of all observations and divide by the
number of observations• Sample median
– Sort the observations in increasing order and take the observation in the middle of the series
• Sample mode– Plot a histogram of the observations and choose
the midpoint of the bucket where the histogram peaks
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© 1998, Geoff Kuenning
Indices of Dispersion• Measures of how much a data set varies
– Range– Sample variance
– And derived from sample variance: • Square root -- standard deviation, S• Ratio of sample mean and standard deviation – COV
s / x– Percentiles
• Specification of how observations fall into buckets
sn
x xii
n2 2
1
1
1
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© 1998, Geoff Kuenning
Interquartile Range
• Yet another measure of dispersion• The difference between Q3 and Q1• Semi-interquartile range -
• Often interesting measure of what’s going on in the middle of the range
SIQRQ Q
3 1
2
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© 1998, Geoff Kuenning
Which Index of Dispersion to Use?
Bounded?
Unimodalsymmetrical?
Range
C.O.V
Percentiles or SIQR
But always remember what you’re looking for
Yes
Yes
No
No
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© 1998, Geoff Kuenning
• If a data set has a common distribution, that’s the best way to summarize it– Saying a data set is uniformly distributed is more
informative than just giving its sample mean and standard deviation
• So how do you determine if your data set fits a distribution?– Plot a histogram– Quantile-quantile plot– Statistical methods
Determining a Distribution for a Data Set
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© 1998, Geoff Kuenning
Quantile-Quantile Plots
• Most suitable for small data sets• Basically -- guess a distribution• Plot where quantiles of data should fall
in that distribution– Against where they actually fall in the
sample• If plot is close to linear, data closely
matches that distribution
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© 1998, Geoff Kuenning
ObtainingTheoretical Quantiles
• We need to determine where the quantiles should fall for a particular distribution
• Requires inverting the CDF for that distribution
qi = F(xi) xi = F-1(qi)
– Then determining quantiles for observed points
– Then plugging in quantiles to inverted CDF
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© 1998, Geoff Kuenning
Inverting a Distribution
• Many common distributions have already been inverted (how convenient…)
• For others that are hard to invert, tables and approximations are often available (nearly as convenient)
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© 1998, Geoff Kuenning
Is Our Example Data Set Normally Distributed?
• Our example data set was-17, -10, -4.8, 2, 5.4, 27, 84.3, 92, 445, 2056• Does this match the normal distribution?• The normal distribution doesn’t invert nicely
– But there is an approximation for N(0,1):
– Or invert numerically
x q qi i i 4 91 10 14 0 14. . .
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© 1998, Geoff Kuenning
Data For Example Normal Quantile-Quantile Plot
i qi yi xi1 0.05 -17 -1.646842 0.15 -10 -1.034813 0.25 -4.8 -0.672344 0.35 2 -0.383755 0.45 5.4 -0.12516 0.55 27 0.12517 0.65 84.3 0.3837538 0.75 92 0.6723459 0.85 445 1.03481210 0.95 2056 1.646839
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© 1998, Geoff Kuenning
Example NormalQuantile-Quantile Plot
-500
0
500
1000
1500
2000
2500
-1.65 -1.03 -0.67 -0.38 -0.13 0.13 0.38 0.67 1.03 1.65
Definitely not normal– Because it isn’t linear– Tail at high end is too long for normal
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© 1998, Geoff Kuenning
Estimating Populationfrom Samples
• How tall is a human?– Measure everybody in this room– Calculate sample mean – Assume population mean equals
• What is the error in our estimate?
xx
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© 1998, Geoff Kuenning
Estimating Error
• Sample mean is a random variable Mean has some distributionMultiple sample means have “mean
of means”• Knowing distribution of means can
estimate error
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Confidence Intervals
• Sample mean value is only an estimate of the true population mean
• Bounds c1 and c2 such that there is a high probability, 1-, that the population mean is in the interval (c1,c2):
Prob{ c1 < < c2} =1- where is the significance level and100(1-) is the confidence level
• Overlapping confidence intervals is interpreted as “not statistically different”
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© 1998, Geoff Kuenning
Confidence Intervals
• How tall is Fred?– Suppose average human height is 170 cmFred is 170 cm tall
Yeah, right – Suppose 90% of humans are between 155
and 190 cmFred is between 155 and 190 cm
• We are 90% confident that Fred is between 155 and 190 cm
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© 1998, Geoff Kuenning
Confidence Intervalof Sample Mean
• Knowing where 90% of sample means fall, we can state a 90% confidence interval
• Key is Central Limit Theorem:– Sample means are normally distributed– Only if independent– Mean of sample means is
population mean – Standard deviation (standard error) is
n
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© 1998, Geoff Kuenning
EstimatingConfidence Intervals
• Two formulas for confidence intervals– Over 30 samples from any
distribution: z-distribution– Small sample from normally
distributed population: t-distribution• Common error: using t-distribution for
non-normal population– Central Limit Theorem often saves us
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© 1998, Geoff Kuenning
The z Distribution
• Interval on either side of mean:
• Significance level is small for large confidence levels
• Tables of z are tricky: be careful!
x zs
n
1 2
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© 1998, Geoff Kuenning
Example of z Distribution
• 35 samples: 10 16 47 48 74 30 81 42 57 67 7 13 56 44 54 17 60 32 45 28 33 60 36 59 73 46 10 40 35 65 34 25 18 48 63
• Sample mean = 42.1. Standard deviation s = 20.1. n = 35
• 90% confidence interval is
x
42 1 1 64520 1
3536 5 47 7. ( . )
.( . , . )
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© 1998, Geoff Kuenning
The t Distribution
• Formula is almost the same:
• Usable only for normally distributed populations!
• But works with small samples
x ts
nn
1 2 1 ;
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© 1998, Geoff Kuenning
Example of t Distribution
• 10 height samples: 148 166 170 191 187 114 168 180 177 204
• Sample mean = 170.5. Standard deviation s = 25.1, n = 10
• 90% confidence interval is
• 99% interval is (144.7, 196.3)
x
170 5 1833251
10156 0 185 0. ( . )
.( . , . )
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© 1998, Geoff Kuenning
Getting More Confidence
• Asking for a higher confidence level widens the confidence interval– Counterintuitive?
• How tall is Fred?– 90% sure he’s between 155 and 190 cm– We want to be 99% sure we’re right– So we need more room: 99% sure he’s
between 145 and 200 cm
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For Discussion Next TuesdayProject Proposal1. Statement of hypothesis2. Workload decisions3. Metrics to be used4. Method
Reading for Next TimeElson, Girod, Estrin, “Fine-grained Network Time
Synchronization using Reference Broadcasts, OSDI 2002 – see readings.html
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For Discussion Today
• Bring in one either notoriously bad or exceptionally good example of data presentation from your proceedings. The bad ones are more fun. Or if you find something just really different, please show it.
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