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## PowerPoint Slideshow about 'Probability Plots' - salome

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Introduction

- Probability plots allow us to assess the degree to which a set of data fits a particular distribution
- The idea is to scale the x-axis of a CDF such that the result would be a straight line if the data conforms to the assumed distribution

Uncertainty Analysis for Engineers

An Example

- Suppose we have a set of data that we suspect is normal.
- First, we form an empirical cdf
- [f,xx]=ecdf(x)

- Then scale cdf so that each unit on the axis corresponds to 1 standard deviation
- z=norminv(ff);

- Then we plot the data (sorted) against this new axis
- figure, plot(y,z,'+')

Uncertainty Analysis for Engineers

The Script

n=100;

x=normrnd(10,3,n,1);

y=sort(x);

[f, xx]=ecdf(x)

for i=1:n

ff(i)=(f(i)+f(i+1))/2;

end

z=norminv(ff);

figure, plot(y,z,'+')

Uncertainty Analysis for Engineers

Matlab has an alternative

- probplot('norm',x)
- Options are
- exponential
- extreme value
- lognormal
- normal
- rayleigh
- weibull

Uncertainty Analysis for Engineers

Vertical axis here is cdf, not number of standard deviations

Uncertainty Analysis for Engineers

Look at some lognormal data

Normal probability plot

Normal probability plot of log of data

Lognormal probability plot

Uncertainty Analysis for Engineers

Now some exponential data

Lognormal probability plot

Exponential probability plot

Uncertainty Analysis for Engineers

Facts

- On normal probability plots, the intercept is the mean
- On exponential paper, the slope is 1/
- Results at the extremes are expected to deviate from the straight line more than those in the middle
- On the other hand, for some data, multiple distributions will fit in the center, but not in the tails

Uncertainty Analysis for Engineers

Results

- We are dealing with samples, so our conclusions tend to be one of
- The model appears to be adequate
- The model is questionable
- The model is not adequate

Uncertainty Analysis for Engineers

Comparison

- Take some wind data (maximum measured wind velocity over a given period)
- 20 data points taken over 20 years
- Compare all 6 Matlab probability plots
- Compare looking at CDFs
- Compare other error measures

Uncertainty Analysis for Engineers

Goodness of Fit Statistics

- For discrete and continuous sampled data distributions
- Chi-square statistic
- Kolmogorov-Smirnoff (K-S) statistic
- Anderson-Darling (A-D) statistic
- Root Mean Square Error (RMS).
- Value is limited if there are fewer than about 30 data points.
- The lower the value, the closer the distribution appears to fit the data. But they do not provide a measure that the data actually come from the distribution.

Chi-square statistic

- This goodness-of-fit statistic measures
- The oldest, most commonly used
- Data are grouped into frequency cells and compared to the expected number of observations based on the proposed distribution.
- Definition
- Where O(i) is the observed frequency of the ith histogram bar and
- E(i) is the expected frequency from the fitted distribution of x values falling within the x range of the ith histogram bar.

- It can be overly sensitive to large errors

Chi-Squared Tests

- First we divide values into groups; suggestion is
- For example, if we have n=500 data points, then this gives us about 45 groups (I would use 50 for convenience)

Example (cont.)

- Sort data and divide into 50 cells
- Find upper and lower bound of values in each cell
- Calculate expected number of data points in each cell by subractingcdf of lower bound from cdf of upper bound

Example (cont.)

- Compare this value to the value of the chi-squared distribution for k-np-1 degrees of freedom and a desired confidence level, where np is the number of parameters in the model (eg, 2 for a normal distribution)

Example (cont.)

- In Matlab, we can get this chi-squared distribution from chi2inv(p,v), where p is the confidence level (0<p<1) and v is the number of DOF
- If our calculated value for chi-squared is less than chi-square distribution, then the fit is OK

Kolmogorov-Smirnov Test

- Compare measured cumulative frequency with CDF of assumed theoretical distribution
- Compare the maximum discrepancy between these two with a critical value of a test statistic and reject fit if former exceeds latter
- Good when we don’t have many data points

Kolmogorov-Smirnoff Statistic K-S is better than χ2because data are assessed at all points— But value determined by the one largest discrepancy

- Where Dn is the K-S distance,
- n is the total number of data points,
- F(x) is the distribution function of the fitted distribution,
- and Fn(x)=i/n and i is the cumulative rank of the data point.

- avoids problem of number of bars (bins).

- So it takes no account of lack of fit across entire distribution

More on K-S statistic

- The position of Dn along the x-axis is more likely to occur away from the low probability tails.
- This insensitivity to lack of fit at the extremes is corrected for in the Anderson-Darling statistic.

- Some statistical literature is critical about distribution fitting software that use this statistic as a goodness-of-fit test.
- Because the statistic assumes the fitted distribution is fully specified so that the critical region of the curve can be checked.

Process

- Sort n data points
- Make a step-wise cdf
- (cdfplot in Matlab)

- Fit data to a model to obtain model cdf
- Find maximum difference between these two cdf’s over each of the steps in the first cdf
- Look up comparison data in tables

What is the Significance Level?

- Our hypothesis is that the fit is a good fit.
- If difference in cdf’s exceeds that of the test statistic, we reject the hypothesis
- There are two possibilities:
- Fit really is bad, or
- We are rejecting a good fit

- Significance level is probability that we are rejecting a good fit

Fit Tests in Matlab

- chi2gof(x) – normal distribution only
- kstest(x,CDF)

Matlab Code

w1=data(:,4); n=numel(w1);

hist(w1,25); ncells=128; npoints=51;

nused=ncells*npoints;

datasort=sort(w1(1:nused));

a=4.52997; b=2.72931; %from dfittol

chisqr=0;

upperbound=min(datasort);

for i=1:ncells

indx=(i-1)*(npoints)+1;

thisset=datasort(indx:indx+npoints-1);

lowerbound=upperbound;

upperbound=max(thisset);

ee=n*(gamcdf(upperbound,a,b)-gamcdf(lowerbound,a,b));

chisqr=chisqr+(npoints-ee)^2/ee;

end

chisqr

param=2; v=ncells-param-1;

chpdf=chi2inv(0.95,v)

KS Test

a=13.9761; %from dfittool

b=2.34506; %from dfittol

cdfvals=wblcdf(xvals,a,b);

kstest(datasort,[xvals' cdfvals'])

Anderson-Darling Statistic

- This is a more sophisticated and complex version of the K-S,
- It is more powerful because
- The f(x) weights the observed distances by the probability that the value will be generated at that x value.
- This helps focus the difference measure more equitably.

- The vertical distances are integrated over ALL values of x rather than just looking at the maximum.
- This makes maximum use of the observed data

- The f(x) weights the observed distances by the probability that the value will be generated at that x value.

Root Mean Square Error

- RMS error is available as a test statistic in BESTFIT for expert data that is sampled using percentiles.
- Measures the area between the distribution fit and the data.
- The smaller the better. Does not provide fine distinction.

Back to Example - Tests

- All pass KS test except exponential and rayleigh at 5% significance level
- Same holds at 2% level

Uncertainty Analysis for Engineers

Script

Rayleigh and Exponential fail test (kx=kr=1)

vel=[78.2 75.8 81.8 85.2 75.9 78.2 72.3 69.3 76.1 74.8 ...

78.4 76.4 72.9 76.0 79.3 77.4 77.1 80.8 70.6 73.5];

sortv=sort(vel);

subplot(2,3,1),probplot('ev',vel)

subplot(2,3,2),probplot('norm',vel)

subplot(2,3,3),probplot('lognorm',vel)

subplot(2,3,4),probplot('weibull',vel)

subplot(2,3,5),probplot('exponential',vel)

subplot(2,3,6),probplot('rayleigh',vel)

x=68:0.1:86;

alpha=0.02;

zev=evfit(vel);

sev=evcdf(x,zev(1),zev(2));

kev=kstest(sortv,[x' sev'],alpha)

[an bn]=normfit(vel);

sn=normcdf(x,an,bn);

kn=kstest(sortv,[x' sn'],alpha)

zx=expfit(vel);

sx=expcdf(x,zx);

kx=kstest(sortv,[x' sx'],alpha)

zl=lognfit(vel);

sl=logncdf(x,zl(1),zl(2));

kl=kstest(sortv,[x' sl'],alpha)

zw=wblfit(vel);

sw=wblcdf(x,zw(1),zw(2));

kw=kstest(sortv,[x' sw'],alpha)

zr=raylfit(vel);

sr=raylcdf(x,zr);

kr=kstest(sortv,[x' sr'],alpha)

figure,plot(x,sev,'o',x,sn,'r',x,sx,'b',x,sl,'+',x,sw,'m',x,sr,'c',...

'LineWidth',2)

legend('EV','Norm','Exp','LogN','Weibull','Rayleigh')

hold on

cdfplot(vel)

Uncertainty Analysis for Engineers

A Second Example

- A new controller was installed on 96 diesel locomotives
- The mileage at failure for each was recorded
- 37 failed at less than 135,000 miles
- All we know about the others is that each lasted beyond 135k miles
- Thus, we have to “censor” the data
- We assume we have 96 data points, but only plot 37
- This is important when we compute CDF
- Goal is 80,000 mile warranty

Uncertainty Analysis for Engineers

Censoring in Matlab

- Tell Matlab which data points are censored and how many of each there are
- We’ll use a lognormal plot in the example because failure mechanism indicates this is appropriate

Uncertainty Analysis for Engineers

Uncensored “Normal” probability plot

Uncertainty Analysis for Engineers

Un-Censored “Lognormal” Plot

Uncertainty Analysis for Engineers

Censored “Lognormal” Plot

Uncertainty Analysis for Engineers

Conclusions

- The lognormal distribution looks like a good fit
- Probability of failure is approximately 15%

Uncertainty Analysis for Engineers

Script

d=[22.2 37.5 46.0 48.5 51.5 ...

53 54.5 57.5 66.5 68 ...

69.5 76.5 77 78.5 80 ...

81.5 82 83 84 91.5 93.5 ...

102.5 107 108.5 112.5 113.7 ...

116 117 118.5 119 120 ...

122.5 123 127.5 131.0 132.5 134]

data=[d'; 135];

bools=zeros(size(data));

bools(end)=1;

freq=bools*59+1;

probplot('logn',data)

figure, probplot('logn',data,bools,freq)

Uncertainty Analysis for Engineers

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