Orange juice data -- Part 2

A full description of the problem is given in orangejuice.

This dataset contains samples taken after the machine adjustment was made.

data(orangejuice)

Format

A data frame with 64 observations on the following 4 variables:

D

number of defectives

size

sample sizes

trial

trial samples (TRUE/FALSE)

References

Montgomery, D.C. (1991) Introduction to Statistical Quality Control, 2nd ed, New York, John Wiley & Sons, pp. 155--159.

Examples

data(orangejuice2)
orangejuice2 <- transform(orangejuice2, d = D/size)
describe(orangejuice2, by = trial)
#> ── trial = FALSE ─────────────────────────────────────────────────────────────── 
#>        Obs   Mean   StdDev   Min Median   Max
#> sample  40 74.500 11.69045 55.00   74.5 94.00
#> D       40  5.450  2.25263  1.00    5.0 11.00
#> size    40 50.000  0.00000 50.00   50.0 50.00
#> d       40  0.109  0.04505  0.02    0.1  0.22
#> 
#> ── trial = TRUE ──────────────────────────────────────────────────────────────── 
#>        Obs    Mean  StdDev   Min Median   Max
#> sample  24 42.5000 7.07107 31.00  42.50 54.00
#> D       24  5.5417 2.14637  2.00   5.50 12.00
#> size    24 50.0000 0.00000 50.00  50.00 50.00
#> d       24  0.1108 0.04293  0.04   0.11  0.24
boxplot(d ~ trial, data = orangejuice2)

plot(d ~ sample, data = orangejuice2, type = "b", pch = ifelse(trial, 1, 19))