An improvement ratio or improvement amount is the improvement that you want a demonstration test to detect. Depending on the specified distribution, Minitab plots the probability of passing the demonstration test against improvement as either an improvement ratio or an improvement amount.
- Improvement ratio
- The true value divided by the minimum value you want to demonstrate. For example, an electronic components supplier has improved the design of a particular glass capacitor. The lifetime of the existing capacitor follows a Weibull distribution with a 5th percentile of 50 hours. They consider a 5th percentile of 150 hours to be a significant improvement in capacitor life. Hence, they want a demonstration test for the newly designed capacitor to pass if the improvement ratio is 150/50 = 3. The POP graph displays a plot of the probability of passing the demonstration test against the improvement ratio for the Weibull, exponential, lognormal, and loglogistic distributions.
- Improvement amount
- The minimum value you want to demonstrate subtracted from the true value. For example, a manufacturer improves the design of a thermostat used inside a gas water heater. The lifetime of the existing thermostat follows a normal distribution with a 5th percentile of 1825 hours. They consider a 5th percentile of 2010 hours to be a significant improvement in thermostat life. Hence, they want a demonstration test for the newly designed thermostat to pass if the true improvement amount is 2010 - 1825 = 185 hours. The POP graph displays a plot of the probability of passing the demonstration test against the improvement amount for the smallest extreme value, normal, and logistic distributions.
