XmR charts for several measures at the same day/month Xmr charts are often used in statistical process control (see for example). They make sense, but I wonder what one does, if one has several measure per day/month etc. Should one just use the average (e.g. arithmetic mean)?
This code taken from here simulates 12 measurements, one for each month:
library(ggplot2)
library(ggQC)

set.seed(5555)
Golden_Egg_df <- data.frame(month=1:12,
    egg_diameter = rnorm(n = 12, mean = 1.5, sd = 0.2)
)
Golden_Egg_df$egg_diameter[3] <- 2.5

options(repr.plot.width = 5, repr.plot.height = 5)
XmR_Plot <- ggplot(Golden_Egg_df, aes(x = month, y = egg_diameter)) +
               geom_point() + geom_line() + 
               stat_QC(method = "XmR")

XmR_Plot 


 A: Classical statistical process control (SPC) operates only operates on a single sequence of data points (ranked time) without taking into account their temporal distance. That is, it does not matter whether the data points are seconds or years apart.
In many cases, this is an impermissible simplifaction of much richer data. If you have content knowledge classical SPC represents your phenomenon poorly, you could tweak it so that it does become representative. E.g.:

*

*Reduce to mean: If you think that densely spaced data points really are sampled from the same underlying single value rather (i.e., the mean is more of interest than the individual data points), data reduction using a mean is justified.


*Multivariate: If you think that individual workers will drift collectively but from each his/her own intercept and/or spread, a multivariate solution seems appropriate.


*Split data: If you think that individual workers will drift independently of each other, you probably want to do statistical process control on each one individually.
