This tool performs Parametric Distribution Analysis for uncensored and right censored data. It can be used to determine an appropriate distribution to fit your data by assessing how closely the plot points fit the cumulative density function. You can also use the Anderson-Darling or Pearson correlation coefficient to assess the goodness-of-fit. In addition, you can estimate percentiles and survival probabilities and visualise your data in survival, cumulative failure and hazard plots.
At the Excel Menu (For Excel 2007, go to Add-ins first)
Choose ProcessMA > Reliability and Survival > Distribution Analysis (Right Censoring) > Parametric Distribution Analysis
In Variable, select the column containing the data (Numeric)
In Frequency, select the column containing frequency counts (Optional, Positive integer)
In Assumed Distribution, choose the distribution to fit
Under Censor Tab, in Censor, select the column containing censoring indicator (Optional, Exactly 2 distinct values - censored and uncensored)
Under Censor Tab, in Censor Value, choose the indicator you use to indicate censoring
Under Estimate Tab, in Estimation Method, choose the method used to estimate distribution parameters
Under Estimate Tab, in Method, choose the method used to estimate plot points
Under Estimate Tab, in Set Shape/Scale At, enter a known shape and scale parameter for the variable (Optional, Numeric)
Under Result Tab, check Show Parameter Estimates, if you want to estimate distribution parameters
Under Result Tab, check Show Characteristics, if you want to calculate distribution characteristics
Under Result Tab, check Estimate Percentiles, if you want to calculate percentile values for specific percent
Under Result Tab, in Percent, enter the percent values you want to calculate percentiles (Optional, Numeric, >0 & <100, Separate multiple entries with commas)
Under Result Tab, check Estimate Survival Probabilities, if you want to calculate survival probabilities for values for specific times
Under Result Tab, in Times, enter the time value you want to calculate survival probabilities (Optional, Numeric, >0, Separate multiple entries with commas)
Under Result Tab, in Confidence Level, enter the desired confidence level (Numeric, >0 & <1)
Under Graph Tab, check Probability Plot, if you want to display probability plot
Under Graph Tab, check Probability Density Plot, if you want to display probability density plot
Under Graph Tab, check Survival Plot, if you want to display survival plot
Under Graph Tab, check Cumulative Failure Plot, if you want to display cumulative failure plot
Under Graph Tab, check Hazard Plot, if you want to display hazard plot
Under Graph Tab, check Show Confidence Interval, if you want to display confidence interval in the above plots
Click OK
You work in a manufacturing plant making widgets and you want to find out the time at which 5% of widgets will fail. You have collected failure times for 50 widgets but some widgets dropped out of the test. The failure times marked ‘F’ indicates actual failure times and ‘C’ indicates censored units. You want to find out the times at various percentages of widgets fail and the proportion of widgets that survive more than 150 months.
Open data worksheet by choosing ProcessMA > Tools > Data
Choose ProcessMA > Reliability and Survival > Distribution Analysis (Right Censoring) > Parametric Distribution Analysis
In Variable, select CD - Failure Times
In Assumed Distribution, choose Lognormal
Under Censor Tab, in Censor, select CE - Censor
Under Censor Tab, in Censor Value, choose C
Under Result Tab, check Show Parameter Estimates
Under Result Tab, check Show Characteristics
Under Result Tab, check Estimate Percentiles
Under Result Tab, check Estimate Survival Probabilities
Under Result Tab, in Times, enter 150
Click OK
From the table of percentiles, you can see that it takes 124.3363 months for 5% of the widgets to fail. From the table of survival probabilities, you can see that 70.34% survive more than 150 months.
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