Characteristics of variable – actuarial estimation method

The median is a measure of the center of the distribution.

Nonparametric estimates do not depend on any particular distribution. Therefore, these estimates are useful when no distribution adequately fits the data.

Example output

Interpretation

The characteristics of the variable are calculated for the engine windings tested at 80° C.

The median (56.1905) is a resistant statistic because outliers and the tails in a skewed distribution do not significantly affect its values.

Additional time from Time T until 50% of running units fail – actuarial estimation method

Use the additional time table to determine how much additional time, from a fixed time, passes before a certain percentage of the currently surviving products will fail. For each "Time T", Minitab estimates the additional time that must pass until one-half of the currently surviving products fail.

Example output

Interpretation

For the engine windings at 80° C, 84% of the windings survive until 40 hours. After an estimated 20 more hours, an additional 42% ((0.84 x 0.5) x 100) of the windings that are still running at 40 hours are expected to fail.

Conditional probability of failure – actuarial estimation method

The conditional probability of failure indicates the probability that a product that has survived until the beginning of a particular interval will fail within the interval.

Example output

Interpretation

At 80° C, an engine winding that survived until 40 hours has a probability of 0.500000 (or a 50% chance) of failing in the interval of 40 to 60 hours.

The survival probabilities indicate the probability that the product survives until a particular time. Use these values to determine whether your product meets reliability requirements or to compare the reliability of two or more designs of a product.

Example output

Interpretation

At 80° C, 0.84, or 84%, of the engine windings survived at least 40 hours.

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