Leaderboard

The Laney’s P chart is based on the working principle of a p chart used for attribute data– the difference being that it adjusts for very large sample sizes(usually > 5000), to provide an accurate assessment of control points than in a p chart. It is difficult to assess the statistical control of the proportion due to the over dispersion of results, with a normal p chart. Points on a p chart are shown out of control due to over dispersion. Laney’s sigma Z is a measure of over dispersion. While attribute p chart shows points out of control ,applying same data to plot Laney’s p chart shows points in control. The advantage of the p chart is that it gives us the proportion defective in y axis & np chart gives us the whole number of defective in y axis. while Laney's p chart has the advantage of using varying control limits - it avoids the variation due to over dispersion, in case of higher sample size ( of more than 5000). The disadvantage of p chart is that its predictions may not be accurate if the population has varying sub groups. Hence under such specific conditions of sample size more than 5000 with varying sub groups Laney's P chart is used. Let us take example of our central Govt planning to set up Temporary health care bedded hospitals in Tier 1 towns to tackle the pandemic. And let us say the each Tier 1 city has population varying between 5000 ~ 35000 , out of which % proportion of the people who are attacked by the COVID need hospitalization in let us say 10 such cities is the vital input to the govt to plan the priority. In this case use of p chart would give us over dispersion of results.so the statistical dept shall use Laney's p chart to calculate the proportion using varying control limits and also addressing the variation of the large sample using the Laney's sigma which is not expressed as binomial as in a normal p chart. In this way the govt can really find the tier 1 cities for which the number of persons to be treated exceeds the broader control limits set by Laney's p chart. This is very useful to disregard the variation due to over dispersion or under dispersion .