Transcript Ch.4: Probability and Statistics Variations due to:

©1997 by M. Kostic
Ch.4 (Ch.6 in current Text):
Probability and Statistics
Variations due to:
• Measurement System:
Resolution and Repeatability
• Meas. Procedure:
Repeatability
• Measured Variable:
Temporal & Spatial Var.
©1997 by M. Kostic
Statistical Measurement Theory
• Sample - a set of measured data
• Measurand - measured variable
• (True) mean value: (x’)
xmean
©1997 by M. Kostic
Mean Value and Uncertainty
x’= xmean ± ux @ P%
xmean is a P% probable estimate of x’
with uncertainty ux
Probability-Density Function
©1997 by M. Kostic
More dense
Range
Less dense
©1997 by M. Kostic
Histogram-Frequency distribution
K=7 intervals
nj=7>5
4
3
2
1
©1997 by M. Kostic
Mean value and Variance
©1997 by M. Kostic
Infinite Statistics
Probability-density function p(x) and Probability P%
p(x)=dP/dx
b=(x-x’)/s = dim’less deviation
For x=x’, b=0
©1997 by M. Kostic
Normal-Gaussian distribution
b=(x-x’)/s
68.27%
95.45%
99.73%
Normal-Gaussian distribution
©1997 by M. Kostic
½P(z1=1.02)=?
Z1=1.02
MathCAD
file
½P(z1=1.02)=34.61%
Also, z1( ½P=0.3461) =1.02
Finite Statistics
©1997 by M. Kostic
• Student-t distribution
50=P%
n=N-1
t
t(n=9,P=50%)=?
MathCAD
file
Also, P(n=9, t =0.703)=50%
and n(P =50%, t =0.703)=9
n, P, t are related
©1997 by M. Kostic
Standard Deviation of the Means
©1997 by M. Kostic
Standard Deviation of the Means (2)
y
N
2
1
( xi _ color - xcolor )

N - 1 i=1
S x _ color =
N
S mean =
S
i =1
x _ color ,i
 S x _ color,i
N
N 1
x
xi
©1997 by M. Kostic
Pooled Statistics
M replicates of N repeated measurements
P ooled mean or average:
M
x =
M
N
1
xi , j or if N j  const x =

MN j =1 i =1
N
j =1
M
j
N
j =1
xj
; n j = N j -1
j
P ooled standard deviation:
M
Sx =
M
N
1
2
(
x
x
)
=

i, j
j
M ( N - 1) j =1 i =1
1
M
M
 Sxj
j =1
2
or S x =
n
j =1
M
Sx =
Sx
MN
or S x =
Sx
M
N
j =1
j
Sx j
n
j =1
P ooled standard deviation of the mean :
j
j
©1997 by M. Kostic
Least-Square Regression
Arbitrary (our choice function): yc
yc ,i = f ( xi , a0 , a1 ,...a j , ...am )
y
where aj are coefficients to be found
The sum of deviations squared
should be minimum :
2
D =  d i = (yi - yc , i ) 2  min
yi
yc,i
i
i = 1,2,...n
i
(yi - yc , i ) = d i
x
xi
Given data points: { xi , yi }, i = 1,2,...n
©1997 by M. Kostic
Least-Square Regression (2)
Given data point s: {xi , yi }, i = 1,2,...n
t o curve- fit with an arbitrary(our choice function): yc
yc ,i = f ( xi , a0 , a1 ,...a j , ...am ) where a j are coefficient s t o be found
the sum of deviationssquared should be minimum:
D =  d i = (yi - yc , i ) 2  min ; i = 1,2,...n; then...
2
i
i
D
= 0  a j (for j = 0,1,2,...m) could be solved from (m  1) eqs.
a j
Click for Polynomial Curve-Fit
Click for Arbitrary Curve-Fit
©1997 by M. Kostic
Correlation Coefficient
Given data points: {xi , yi }, i = 1,2,...n and curve - fit function
yc ,i = f ( xi , a0 , a1 ,...a j , ...am ) wit h a j coefficients,
thecorrelation coefficient, r , is :
Coeff. of determination
r = 1where :
1
2
S xy =  (yi - yc , i ) 2
n
i
S xy2
S
2
y
If Sxy=Sy and Sxy=0, respectively
, 0  r 1
and
For the simplest,
zeroth order polynomial fit.
1
2
Sy =
(y
y
)
 i
N -1 i
Click for Polynomial Curve-Fit
2
Click for Arbitrary Curve-Fit
©1997 by M. Kostic
Data Outlier
%Pin (zOL)
%Pout(zOL)
Usually zOL= 3 or
zOL= zOL(Pout= 0.5-Pin=0.1/N)
if number of data N is large.
(For Pout=1%, zOL=2.33)
Keep data if within ± zOL
otherwise REJECT DATA
as Outliers
blimit= zOL = zOL(%Pin or %Pout)
©1997 by M. Kostic
Required #of Measurements
Mean precision interval: CI = 2d = u =  d = tn ,%P
2
Sx
; then..
N
 tn ,%P S x 
 ; since n = N - (m  1) m =0 = N - 1
N = 
 d 
the calculation procedure is iterative(unless N  , too large)