楼主写错了吧，应该是Probability plot概率图吧？
Use the probability plot to assess whether a particular distribution fits your data. The plot consists of:
· plot points, which represent the proportion of failures up to a certain time. The plot points are calculated using a nonparametric method, which assumes no parametric distribution. The proportions are transformed and used as the y variable, while their corresponding values may be transformed and used as the x variable.
· the fitted line, which is a graphical representation of the percentiles. To make the fitted line, Minitab first calculates the percentiles for the various percents, based on the chosen distribution. The associated probabilities are then transformed and used as the y variables. The percentiles may be transformed, depending on the distribution, and are used as the x variables. The transformed scales, chosen to linearize the fitted line, differ depending on the distribution used.
· a set of approximately 95.0% confidence intervals for the fitted line.
Because the plot points do not depend on any distribution, they would be the same (before being transformed) for any probability plot made. The fitted line, however, differs depending on the parametric distribution chosen. So you can use the probability plot to assess whether a particular distribution fits your data. In general, the closer the points fall to the fitted line, the better the fit.
Minitab provides two goodness-of-fit measures to help assess how the distribution fits your data: the Anderson-Darling statistic for both the maximum likelihood and the least squares methods and the Pearson correlation coefficient for the least squares method.
The Anderson-Darling statistic is a measure of how far the plot points fall from the fitted line in a probability plot. Minitab uses an adjusted Anderson-Darling statistic, in which points in the tails are weighted more. A smaller Anderson-Darling statistic indicates that the distribution fits the data better.
For least squares estimation, Minitab calculates a Pearson correlation coefficient. If the distribution fits the data well, then the plot points will fall on a straight line. The correlation measures the strength of the linear relationship between the X and Y variables. The correlation will range between 0 and 1, and higher values indicate a better fitting distribution.
Use the Anderson-Darling statistic and Pearson correlation coefficient to compare the fit of different distributions.
You can enter up to 10 samples per analysis. Minitab estimates the probabilities independently for each sample. All of the samples display on a single plot, in different colors and symbols, which helps you to compare their distributions.
Graphical output consists of a single probability plot. If you have more than one sample, each sample is represented on the plot, using different symbols and colors. If the points in a probability plot are within the confidence intervals, you can judge that the fit of that distribution is a good one. Usually, points outside the confidence limits occur mostly in the tails. For small probabilities, points above the upper confidence limit indicate that there are more data in the left tail than one would expect. For large probabilities, points below the lower limit indicate that there are more data in the right tail than one would expect. The opposite conditions imply less data than expected.
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philip (威望:0) (上海 闵行) 石油化工
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Use the probability plot to assess whether a particular distribution fits your data. The plot consists of:
· plot points, which represent the proportion of failures up to a certain time. The plot points are calculated using a nonparametric method, which assumes no parametric distribution. The proportions are transformed and used as the y variable, while their corresponding values may be transformed and used as the x variable.
· the fitted line, which is a graphical representation of the percentiles. To make the fitted line, Minitab first calculates the percentiles for the various percents, based on the chosen distribution. The associated probabilities are then transformed and used as the y variables. The percentiles may be transformed, depending on the distribution, and are used as the x variables. The transformed scales, chosen to linearize the fitted line, differ depending on the distribution used.
· a set of approximately 95.0% confidence intervals for the fitted line.
Because the plot points do not depend on any distribution, they would be the same (before being transformed) for any probability plot made. The fitted line, however, differs depending on the parametric distribution chosen. So you can use the probability plot to assess whether a particular distribution fits your data. In general, the closer the points fall to the fitted line, the better the fit.
Minitab provides two goodness-of-fit measures to help assess how the distribution fits your data: the Anderson-Darling statistic for both the maximum likelihood and the least squares methods and the Pearson correlation coefficient for the least squares method.
The Anderson-Darling statistic is a measure of how far the plot points fall from the fitted line in a probability plot. Minitab uses an adjusted Anderson-Darling statistic, in which points in the tails are weighted more. A smaller Anderson-Darling statistic indicates that the distribution fits the data better.
For least squares estimation, Minitab calculates a Pearson correlation coefficient. If the distribution fits the data well, then the plot points will fall on a straight line. The correlation measures the strength of the linear relationship between the X and Y variables. The correlation will range between 0 and 1, and higher values indicate a better fitting distribution.
Use the Anderson-Darling statistic and Pearson correlation coefficient to compare the fit of different distributions.
You can enter up to 10 samples per analysis. Minitab estimates the probabilities independently for each sample. All of the samples display on a single plot, in different colors and symbols, which helps you to compare their distributions.
Graphical output consists of a single probability plot. If you have more than one sample, each sample is represented on the plot, using different symbols and colors. If the points in a probability plot are within the confidence intervals, you can judge that the fit of that distribution is a good one. Usually, points outside the confidence limits occur mostly in the tails. For small probabilities, points above the upper confidence limit indicate that there are more data in the left tail than one would expect. For large probabilities, points below the lower limit indicate that there are more data in the right tail than one would expect. The opposite conditions imply less data than expected.