The following screenshot shows the shaded area under the curve that is above the upper specification limit. Here is an example of a process with a mean of 100, a standard deviation of 10 and an upper specification limit of 120. View Probability to see where target values fall in a distribution.Two Distributions to compare the shape of distribution curves based on different parameters.Vary Parameters to see how changing parameters will affect the distribution.View Single to display a single probability distribution plot.Select Graph > Probability Distribution Plot, and then choose one of the following options: These plots can be used for example to highlight the effect of changing the distribution parameters or to show where target values fall in a distribution. You may also use the Probability Distribution Plots in Minitab to clearly communicate probability distribution information in a way that can be easily understood by non-experts. You can then use the transformed data with any analysis that assumes the data follow a normal distribution. You may transform your non-normal data using the Box-Cox or Johnson transformation methods so that it follows a normal distribution. In our case, the data does not appear to follow a normal distribution as the points are not close to a straight line. Here is a screenshot of the graph if only the normal distribution has been selected.Ī given distribution is a good fit if the data points approximately follow a straight line and the p-value is greater than 0.05. This will produce the output both in a graph and the session window. As a result, the Z-score values are negative for every data point that has an associated p less than 0.5 and positive for those that have a p greater than 0.5. To find out the probability distribution that best fit the data, select Stat > Quality Tools > Individual Distribution Identification, specify the column of data to analyze, in this case ‘glucose level’, then specify the distribution to check the data against, and then click OK. We know from the normal distribution properties that when the data value equals the mean or 0, the probability of data points < 0 the probability of data points > 0 0.5.Remember to copy the data from the Excel worksheet and paste it into the Minitab worksheet. For this example, you may use the glucose level worksheet. Let’s look at an example where a hospital is seeking to detect the presence of high glucose levels in patients at admission. It allows to easily compare how well your data fit various different distributions. You may use the Individual Distribution Identification in Minitab to confirm that a particular distribution best fits your current data. Minitab can be used to find the appropriate probability distribution of your data. Once you find the appropriate model, you can then perform your statistical analysis in the right manner. It is always a good practice to know the distribution of your data before proceeding with your analysis. You cannot conclude that the data do not follow a normal distribution.There are different shapes, models and classifications of probability distributions including the ones discussed in the probability distributions article. Because the p-value is 0.463, which is greater than the significance level of 0.05, the decision is to fail to reject the null hypothesis. In these results, the null hypothesis states that the data follow a normal distribution. You do not have enough evidence to conclude that your data do not follow a normal distribution. P-value > α: You cannot conclude that the data do not follow a normal distribution (Fail to reject H 0) If the p-value is larger than the significance level, the decision is to fail to reject the null hypothesis. P-value ≤ α: The data do not follow a normal distribution (Reject H 0) If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis and conclude that your data do not follow a normal distribution. A significance level of 0.05 indicates a 5% risk of concluding that the data do not follow a normal distribution when the data do follow a normal distribution. Usually, a significance level (denoted as α or alpha) of 0.05 works well. To determine whether the data do not follow a normal distribution, compare the p-value to the significance level.
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