It can either use the normal approximation of a Monte Carlo based -pvalue. To identify which treatment(s) is/are responsible for rejecting H0, a multiple comparison procedure can be used, XLSTAT allows using the procedure suggested by Cabilio and Peng (2008), with two alternative ways to compute the p-value of the paired comparisons. If the p-value is such that the H0 hypothesis has to be rejected, then at least one treatment is different from another. Here we have only one column corresponding to one sample to test. The data format is One sample per column. Press the Range selector and select the column B that contains the Math average. In order to avoid freezing Excel because of too long computations, it is possible with the two latter methods to set the maximum time that should be spent computing the p-value. Go over to the menu Parametric tests and select the option One-sample t-test and z-test. The more resamplings are performed, the better the estimation of the p-value. A confidence interval on the p-value is provided. The user must set the number of resamplings.
#Xlstat hypothesis testing series
Since there is no way to be perfect, we will find a way to be least erroneous.The goal of the test proposed by Page (1963) is to allow analyzing rigorously the results of a study carried out within the framework of a complete design, to verify if a series of several treatments should be considered as not different, or if alternatively a ranking of the treatment makes sense. Hence Six Sigma practitioners must understand the cost of making both the mistakes and choose the mistake they would rather make. Trying to avoid one error too much leads to the other error. wrongly considering an input to be a vital inputīoth Alpha and Beta errors are related to one another. There is always a possibility that a mistake could be made and that the conclusion reached after hypothesis testing may be invalid. But if this is done there is a risk of Alpha error i.e. Hypothesis testing is all about statistical analysis. This error can be avoided by decreasing the confidence level for the hypothesis. An example would be the failure to mention location as an important factor of the service being rendered at Branch A, when in reality the location may be the cause of the excellent service. missing the vital inputīeta error is the risk of stating that there is a particular input which is not a part of the vital few inputs when in reality it is. But if this is done there is a risk of Beta error i.e. This error can be avoided by increasing the confidence level for the hypothesis. An example would be stating that location of Branch A makes its service better than branch B, when in reality location may not be an important factor determining service at all. Here is the explanation:Īlpha error is the risk of stating that there is a particular input which is a part of the vital few inputs when in reality it is not. In some situations it is better to be exposed to one error than to the other. It is basically a trade-off, if you try to protect yourself from one error, you expose yourself to the other. The Six Sigma project person therefore have to choose between the two possible error types that could be made. The hypothesis testing method takes this into account. There is still a possibility that a mistake could be made and that the conclusion reached after hypothesis testing may be invalid. Your solution should be this document with all the blanks filled in. A significance level of a 0.05 is used for each hypothesis test and a confidence level of 95 is used for each confidence interval estimate. Assume all required conditions have been met for each question. It also depends upon the fact that the critical few inputs were possibly identified earlier. Instructions: Select the best XLSTAT printout to answer each question. It depends upon the data that was collected at the measure stage. Hypothesis testing is all about statistical analysis.
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