- T-test is six sigma comparative method that provide confidence intervals for the manufacturing to make decision. To understand T-test, central limit theorem and hypothesis test concept are very important
- Central limit theorem
- If we repeatedly sample from a population and calculate the mean of each sample, we can create a new distribution composed of these means. This distribution is called the sampling distribution of xbar . This is a new data set composed of x bar’s. These new data points have a mean and a variance, δ2xbar. A relationship exists between δxbar and the δx of the original population:
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- where n is the size of the sample taken from the population.
From this equation we can see that as the sample size taken from the population increases,
the variance of the sampling distribution of the x ’s decreases.
The Central Limit Theorem (CLT) states another important point about the sampling distribution of x : The sampling distributions of x ’s are approximately normally distributed. As the sample size increases, the distribution of means comes closer to the normal distribution. Notice also how that as the sample size increases, the distribution of sample means gets tighter.


- More six sigma tools
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Six sigma tool 1 : Measurement system analysis (MSA)
Sig sigma tool 2 : T-Test
Six sigma tool 3 : Statistical Process Control (SPC)
Six sigma tool 4 : Deisgn of experiments (DOE)
Six sigma tool 5 : Sources of variation (SOV)
Other six sigma tools : Free excel six sigma tools (Cpk, p, %, ppm)