Orange juice data – Part 2
orangejuice2.Rd
A full description of the problem is given in orangejuice
.
This dataset contains samples taken after the machine adjustment was made.
Usage
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))