Open ProfMohammed opened 3 months ago
Interesting. Improved code:
set.seed(91)
y <- rpois(100,10)
c_bar <-mean(y)
lcl <- ((c_bar + 1 / 12)^(2 / 3) - 3 * (2 / 3)*(c_bar)^(1 / 6))^(3 / 2) + 1 / 4
ucl <- ((c_bar + 1 / 12)^(2 / 3) + 3 * (2 / 3) * (c_bar)^(1 / 6))^(3 / 2) - 3 / 4
print(c(c_bar,lcl,ucl))
plot(y, type='b', ylim=c(1,25))
abline(h=c(c_bar,lcl,ucl), col='blue')
qicharts2::qic(y, chart = 'c')
How does this work for C charts with very large numbers? I often find that in such cases, the control limits become "too" narrow and the I chart may be more correct.
This is exactly the kind of thing our technical notes section should discuss. The limitations of each chart and what we advise folks to do when they are at the limits - ie switch to i-chart. Your skill with simulation may be particualrly useful here. I wonder if we might have a chapter on "how to stress test a chart using simulation"...?
To cite this article: Rudolf G. Kittlitz Jr. (2006) Calculating the (Almost) Exact Control Limits for #a C-Chart, Quality Engineering, 18:3, 359-366, DOI: 10.1080/08982110600719472
To link to this article: https://doi.org/10.1080/08982110600719472
you can copy and paste the codesnip below -
set.seed(91) y <- rpois(100,10) c_bar <-mean(y)
lcl <- ((c_bar+1/12)^(2/3) - 3(2/3)(c_bar)^(1/6))^(3/2)+1/4 ucl <- ((c_bar+1/12)^(2/3) + 3(2/3)(c_bar)^(1/6))^(3/2)-3/4
print(c(c_bar,lcl,ucl)) plot(y, type='b', ylim=c(1,25)) abline(h=c(c_bar,lcl,ucl), col='blue')