Lower values represent less burning and, hence, greater flame resistance. The manufacturer tests untreated material, Coating A, and Coating B. Related post: Identifying the Distribution of Your Data Using Empirical CDF Plots to Compare Multiple Samplesįor these data, a manufacturer assessed the burn resistance of untreated and treated fabric by holding samples over a flame for a set amount of time and measuring the burn length. Alternatively, use a distribution test to identify the distribution of your data. However, it’s easier to use Q-Q plots to determine how well your data fit a distribution. When your data follow the fitted distribution, you can use percentiles based on that distribution instead of the data percentiles.įor the casting data, it appears that the strength measurements follow the normal distribution. Assessing the Fit of a Probability DistributionĬompare the empirical CDF to the fitted CDF to determine how well your data fit the distribution.

In other words, 72.7% of the samples have strength measurements less than 0.8. Be sure that the probability distribution provides a good fit for your data!įor example, a strength of 0.8 is at approximately the 70th percentile-72.7 to be precise. Alternatively, use the fitted CDF to determine the percentile using the fitted distribution. To find the data percentile for an observation, identify its value on the vertical Y-axis.

Reading cdf files r