PDF: Probability Distribution Function CDF: Cumulative ~
prob: Probability of success prob, 0 ≤ prob ≤ 1 must be true.
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binomial PDF, CDF
binpdf(trials,prob,x); bincdf(trials,prob,x)
binpdf(6,1/6,2) = 0.2009387600823062
bincdf(23,1/3,10) = 0.8931235700291735
- trials Number of trials (Integer)
- x Position {PDF}, Upper limit {CDF} (Integer less than trials)
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chi-square PDF, CDF
chipdf(x,df); chicdf(x,df)
chipdf(7.2,9) = 0.10397390837927548
chicdf(18.1,9) = 0.9659690712671236
- x Position {PDF}, Upper limit {CDF}
- df Degrees of freedom (Integer > 0)
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Gaussian PDF, CDF
normpdf(x,mean,dev); normcdf(lo,hi,mean,dev)
normpdf(.7,.9,.5) = 0.73654028066467
normcdf(0,12,11.5,1) = 0.6914624830813982
- lo, hi {CDF} Lower limit, Upper limit
- x Position {PDF}
- mean Mean (Default 0)
- dev Standard Deviation (Default 1)
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inverse Gaussian CDF
invnorm(area,mean,dev)
invnorm(.6914624830813982,11.5,1) = 12.000000061941217
- area Area under distribution curve
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geometric PDF, CDF
geompdf(prob,x); geomcdf(prob,x)
geompdf(.5,2) = 0.25
geomcdf(.5,7) = 0.9921875
- x Position {PDF}, Upper limit {CDF}
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Poisson PDF, CDF
poispdf(mean,x); poiscdf(mean,x)
poispdf(7.2,10) = 0.07702676555986275
poiscdf(7.2,10) = 0.886676646816657
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Student-t PDF, CDF
studpdf(x,df); studcdf(x,df)
studpdf(7.2,10) = 1.7301686669799527×10-5
studcdf(2,1.4) = 0.3824221618651978
- df Degrees of freedom (Real number > 0)
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