10.06.2016 Views

eldo_user

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

Monte Carlo Analysis<br />

Additional Uncertainty Analyses (the .mcm File)<br />

==> D’Agostino-Pearson Hypothesis Test of Normality:<br />

Null Hypothesis H0 : Distribution is Normal<br />

Alternate Hypothesis HA: Distribution is not Normal<br />

Number of Observations : 1000<br />

*** Output Measure : MAX<br />

Test Statistic K2 : 3.62537e-02<br />

P-value : 9.82036e-01<br />

Alpha Level<br />

Conclusion<br />

90.00 % Accept H0<br />

95.00 % Accept H0<br />

99.00 % Accept H0<br />

*** Output Measure : BW<br />

Test Statistic K2 : 5.43884e+00<br />

P-value : 6.59130e-02<br />

Alpha Level<br />

Conclusion<br />

90.00 % Reject H0<br />

95.00 % Accept H0<br />

99.00 % Accept H0<br />

*** Output Measure : IVDD<br />

Test Statistic K2 : 2.37019e+00<br />

P-value : 3.05716e-01<br />

Alpha Level<br />

Conclusion<br />

90.00 % Accept H0<br />

95.00 % Accept H0<br />

99.00 % Accept H0<br />

Capability Indices<br />

Capability indices or process capability ratios are statistical indicators of the ability of a process<br />

to produce output within specification limits.<br />

Under the assumption of normality we provide the probability of being within an interval of<br />

specifications [L, U]:<br />

The function represents the cumulative distribution function of the normal distribution<br />

with parameters (μ, σ).<br />

The capability indices are given in the last columns of the table.<br />

Eldo® User's Manual, 15.3 499

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!