Normdf: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
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* '''x''' = matrix in which the sample data is stored, in the interval (-inf,inf). | * '''x''' = matrix in which the sample data is stored, in the interval (-inf,inf). | ||
:: for function=quantile - matrix with values in the interval (0,1). | |||
:: for function=random - vector indicating the size of the random matrix to create. | |||
* '''a''' = mode/location parameter (real). | * '''a''' = mode/location parameter (real). | ||
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===Examples=== | ===Examples=== | ||
====Cumulative | ====Cumulative==== | ||
<pre>>> prob = normdf('c',[1.9600 2.5758]) | |||
ans = | ans = | ||
0.9750 0.9950 | 0.9750 0.9950 | ||
>> x = -5:.1:5; | >> x = -5:.1:5; | ||
>> plot(x,normdf('c',x,0,1)), vline([ 0 ; normdf('q',[0.975; 0.995],0,1)]) | >> plot(x,normdf('c',x,0,1)), vline([ 0 ; normdf('q',[0.975; 0.995],0,1)]) | ||
</pre> | |||
====Density | ====Density==== | ||
<pre> | |||
>> prob = normdf('d',[1.9600 2.5758],0,1) | >> prob = normdf('d',[1.9600 2.5758],0,1) | ||
ans = | ans = | ||
0.0584 0.0145 | 0.0584 0.0145 | ||
>> x = -5:.1:5; | >> x = -5:.1:5; | ||
>> plot(x,normdf('d',x,0,1)), vline([0; normdf('q',[0.975; 0.995],0,1)]) | >> plot(x,normdf('d',x,0,1)), vline([0; normdf('q',[0.975; 0.995],0,1)]) | ||
</pre> | |||
====Quantile | ====Quantile==== | ||
<pre> | |||
>> | >> prob = normdf('q',[.975 .995],0,1) | ||
ans = | ans = | ||
1.9600 2.5758 | 1.9600 2.5758 | ||
</pre> | |||
====Random | ====Random==== | ||
<pre> | |||
>> prob = normdf('r',[4 1],0,1) | >> prob = normdf('r',[4 1],0,1) | ||
ans = | ans = | ||
-0.4326 | -0.4326 | ||
-1.6656 | -1.6656 | ||
0.1253 | 0.1253 | ||
0.2877 | 0.2877 | ||
</pre> | |||
===See Also=== | ===See Also=== | ||
[[ | [[betadf]], [[cauchydf]], [[chidf]], [[expdf]], [[gammadf]], [[gumbeldf]], [[laplacedf]], [[logisdf]], [[lognormdf]], [[paretodf]], [[raydf]], [[triangledf]], [[unifdf]], [[weibulldf]] | ||
Revision as of 13:20, 9 October 2008
Purpose
Normal / Gaussian distribution.
Synopsis
- prob = normdf(function,x,a,b)
Description
Estimates cumulative distribution function (cumulative, cdf), probability density function (density, pdf), quantile (inverse of cdf), or random numbers for a Normal distribution.
This distribution is used for many data types including physical attributes and sums of quantities. It is a symmetric distribution and the variance can be smaller, equal, or larger than the mean.
Inputs
- function = [ {'cumulative'} | 'density' | 'quantile' | 'random' ], defines the functionality to be used. Note that the function recognizes the first letter of each string so that the string could be: [ 'c' | 'd' | 'q' | 'r' ].
- x = matrix in which the sample data is stored, in the interval (-inf,inf).
- for function=quantile - matrix with values in the interval (0,1).
- for function=random - vector indicating the size of the random matrix to create.
- a = mode/location parameter (real).
- b = scale parameter (real and positive).
Note: If inputs (x, a, and b) are not equal in size, the function will attempt to resize all inputs to the largest input using the RESIZE function.
Note: Functions will typically allow input values outside of the acceptable range to be passed but such values will return NaN in the results.
Examples
Cumulative
>> prob = normdf('c',[1.9600 2.5758])
ans =
0.9750 0.9950
>> x = -5:.1:5;
>> plot(x,normdf('c',x,0,1)), vline([ 0 ; normdf('q',[0.975; 0.995],0,1)])
Density
>> prob = normdf('d',[1.9600 2.5758],0,1)
ans =
0.0584 0.0145
>> x = -5:.1:5;
>> plot(x,normdf('d',x,0,1)), vline([0; normdf('q',[0.975; 0.995],0,1)])
Quantile
>> prob = normdf('q',[.975 .995],0,1)
ans =
1.9600 2.5758
Random
>> prob = normdf('r',[4 1],0,1)
ans =
-0.4326
-1.6656
0.1253
0.2877
See Also
betadf, cauchydf, chidf, expdf, gammadf, gumbeldf, laplacedf, logisdf, lognormdf, paretodf, raydf, triangledf, unifdf, weibulldf