Why is -99999 showing in my Office Buddy or ERS reports?
What to do if a value of -999999 shows up in SPC Office Buddy or ERS
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written: 06/20/2017
last modified: 09/16/2024

SYMPTOM

When running charts or calculating statistics in SPC Office Buddy, -99999 is returned for some of the values.


CAUSE

The -99999 value is displayed when the data did not pass the normality test.


RESOLUTION

If you would like to ignore the normality test, and keep from ever seeing data displayed as -99999 use the following directions: In SPC Office Buddy, go to Tools - Options - Non-Normal Data and check the Ignore normality results when using Prolink Charting box. In ERS, go to Tools - Options - Statistical Settings and choose the Ignore Normality option for the Use the following test for normality setting in the Normality Testing for Capability area. MORE INFORMATION
The test of normality in Cpk and Histogram charts is based on the Chi Squared test for goodness of fit - the standard hypothesis test.

To summarize the equations, you could say the test of normality makes a statement "this data comes from a normal distribution" (called the "null hypothesis" or "H sub zero") and challenges the data to disprove it. The method of disproving it is to calculate the deviation in this data from the "ideal" normal distribution. The deviation is represented by the symbol "Chi Squared", hence the name of this test. The deviation is compared to a critical value at a specific risk, and if greater than the critical value the null hypothesis is rejected and the data is considered non-normal. The table that says "for a specific risk, the critical value = X" is a standard mathematical table. The published tables for critical value by risk are not continuous, so intermediate values are calculated by linear interpolation.

In any hypothesis test, there is always a risk that you reject a null hypothesis that is correct (say “non-normal” when the data actually came from a normal process) or fail to reject a null hypothesis that is incorrect (say “normal” when the data came from a non-normal distribution). The risk of incorrectly saying “non-normal” is called the “Producer’s risk” or Alpha. The risk of incorrectly saying “normal” is called the “Consumer’s risk” or Beta.