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Hypothesis Testing: are statistical tools used to draw conclusions based…
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1.-Proportion Test: used when there is only one factor for x and when there is one level of interest for x (the input)
2.- Proportion Test: used when there is only one factor for x and there are 2 levels of interest for x (the input).
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- State the null and alternative hypotheses.
- Set the confidence level for alpha.
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Answering the question whether something changed within the data, often after a team has modified an input or other part of the process.
The null hypothesis is abbreviated as Ho, and is usually a statement about the data that reflects no effect or no difference.
The alternative hypothesis is abbreviated as Ha, and is usually a statement that is likely to be true if the null hypothesis is not true.
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1.- Sample T Test (or Paired T Test): used when comparing means and when dealing with smaller samples or when standard deviation is known.
2.- Sample T Test: used when comparing means and compares the means between two samples of the different x factors.
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- Decide which hypothesis test you are going to use.
- Decide whether your sample size is fixed or whether you can select a sample sized based on your beta setting.
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- Interpret the p-value against your alpha setting, which tells you whether or not to reject the null hypothesis.
- Translate the statistical analysis into real-world, business-relevant language.
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One-Sample T: Since the p-value is less than the alpha value of 0.05, we reject the null hypothesis and accept the alternative.
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Hypothesis Testing in Analyze, Improve, and Control
For six Sigma teams, hypothesis testing is an activity typically founding the latter three phases of a DMAIC project, and most specifically in Analyze or Improve.
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Null Hypothesis: The assumed hypothesis; the statement that nothing has changed, or no statistical difference exists.
Alternative Hypothesis: The statement that something has changed or is statistically different - can be framed as not equal, greater than, or less than.
Alpha: The measurement of the risk of a Type I error - the error that occurs if the null hypothesis is rejected when it was actually true.
Beta: The measurement of the risk of a Type II error - the error that occurs if the null hypothesis is not rejected when it was actually false.
Test Statistic: A standard that is used to calculate the p-value to determine whether to reject the null hypothesis.
P-Value: the number typically used to compare to the alpha value to determine whether or not to reject the null hypothesis. The null hypothesis is rejected if the p-value is less than the alpha value.