How do I make my non-normal data Minitab?

How do I make my non-normal data Minitab?

If your data are nonnormal you can try a transformation so that you can use a normal capability analysis. Choose Stat > Quality Tools > Capability Analysis > Normal. Click Transform. This transformation is easy to understand and provides both within-subgroup and overall capability statistics.

What if my data is not normally distributed?

Many practitioners suggest that if your data are not normal, you should do a nonparametric version of the test, which does not assume normality. But more important, if the test you are running is not sensitive to normality, you may still run it even if the data are not normal.

Can you calculate CPK on non-normal data?

With non-normal data, it is wrong to calculate a Cpk based on the raw data. A better approach is to determine what distribution best fits your process and data and then use the non-normal Ppk approach. The equations for Ppk are different for non-normal data than for normally distributed data.

How do you convert non-normal data to normal data?

Box-Cox Transformation is a type of power transformation to convert non-normal data to normal data by raising the distribution to a power of lambda (λ). The algorithm can automatically decide the lambda (λ) parameter that best transforms the distribution into normal distribution.

How do you know if a distribution is normal or not?

There are some common ways to identify non-normal data:

  1. The histogram does not look bell shaped.
  2. A natural process limit exists.
  3. A time series plot shows large shifts in data.
  4. There is known seasonal process data.
  5. Process data fluctuates (i.e., product mix changes).

Can you standardize non normal data?

1 Answer. The short answer: yes, you do need to worry about your data’s distribution not being normal, because standardization does not transform the underlying distribution structure of the data. If X∼N(μ,σ2) then you can transform this to a standard normal by standardizing: Y:=(X−μ)/σ∼N(0,1).

What is the difference between Cpk and PPK?

So the key takeaway is that Cpk is the potential of a process to meet a specification (short term) while Ppk is how the process actually did (long term). Another way to look at the difference is that Cpk is used for a subgroup of data, while Ppk is used for the whole process.