Attributes charts: p, np, c, u • shewhartr

library(shewhartr)

When the quality characteristic is binary (defective / non-defective) or a count of defects per unit, classical variables charts are the wrong tool. Counts and proportions live on bounded supports and follow Binomial / Poisson distributions; pretending they are normal makes the chart limits wrong, sometimes badly.

Data	Distribution	Chart
Proportion defective (variable n)	Binomial	`shewhart_p()`
Number defective (constant n)	Binomial	`shewhart_np()`
Defect count per unit (constant exposure)	Poisson	`shewhart_c()`
Defect count per unit (variable exposure)	Poisson	`shewhart_u()`

p chart with variable n

claims_p records 30 days of insurance-claim quality control. Each day, a variable number of claims (n) is processed and a count of errors (defects) is observed.

fit <- shewhart_p(claims_p, defects = defects, n = n, index = day)
broom::tidy(fit)
#> # A tibble: 3 × 4
#>   chart line    value note           
#>   <chr> <chr>   <dbl> <chr>          
#> 1 p     CL     0.0601 ""             
#> 2 p     UCL   NA      "varies with n"
#> 3 p     LCL   NA      "varies with n"

Because n varies day-to-day, the limits also vary day-to-day:

broom::augment(fit) |> head(10)
#> # A tibble: 10 × 11
#>      day  .obs .defects    .n .value .center .sigma .upper .lower
#>    <int> <int>    <int> <int>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#>  1     1     1        7   134 0.0522  0.0601 0.0205  0.122      0
#>  2     2     2        8   140 0.0571  0.0601 0.0201  0.120      0
#>  3     3     3        6   129 0.0465  0.0601 0.0209  0.123      0
#>  4     4     4        3   100 0.03    0.0601 0.0238  0.131      0
#>  5     5     5        6   105 0.0571  0.0601 0.0232  0.130      0
#>  6     6     6        6   109 0.0550  0.0601 0.0228  0.128      0
#>  7     7     7        4    82 0.0488  0.0601 0.0262  0.139      0
#>  8     8     8        3   138 0.0217  0.0601 0.0202  0.121      0
#>  9     9     9        6    90 0.0667  0.0601 0.0251  0.135      0
#> 10    10    10        6   125 0.048   0.0601 0.0213  0.124      0
#> # ℹ 2 more variables: .flag_nelson_1_beyond_3s <lgl>, .flag_any <lgl>

The default limits = "3sigma" uses the normal approximation $\bar p \pm 3\sqrt{\bar p (1 - \bar p)/n_i}$ . This is fine when $n_i \bar p \gtrsim 5$ and $n_i (1-\bar p) \gtrsim 5$ . For small $n$ or extreme $\bar p$ , switch to exact binomial limits:

shewhart_p(claims_p, defects = defects, n = n, index = day,
           limits = "binomial")

c chart and Poisson honesty

pcb_solder has 50 PCBs and a mean defect count of about 6. The default 3-sigma c-chart works fine here:

fit_c <- shewhart_c(pcb_solder, defects = defects, index = board)
#> Warning: c_bar = 5.76 is small; the normal approximation is poor.
#> ℹ Consider `limits = "poisson"` for exact limits.
broom::tidy(fit_c)
#> # A tibble: 3 × 3
#>   chart line  value
#>   <chr> <chr> <dbl>
#> 1 c     CL     5.76
#> 2 c     UCL   13.0 
#> 3 c     LCL    0

But if c_bar were small (say 2 or 3), the lower limit under the normal approximation would be negative — which makes no sense for a count. The package warns when this is likely:

small_means <- data.frame(unit = 1:50, defects = rpois(50, lambda = 2))
suppressWarnings(
  fit_low <- shewhart_c(small_means, defects = defects, index = unit)
)
broom::tidy(fit_low)
#> # A tibble: 3 × 3
#>   chart line  value
#>   <chr> <chr> <dbl>
#> 1 c     CL     2.02
#> 2 c     UCL    6.28
#> 3 c     LCL    0

For low-mean Poisson processes, use exact quantile limits:

fit_low_exact <- shewhart_c(small_means, defects = defects, index = unit,
                            limits = "poisson")
broom::tidy(fit_low_exact)
#> # A tibble: 3 × 3
#>   chart line  value
#>   <chr> <chr> <dbl>
#> 1 c     CL     2.02
#> 2 c     UCL    7   
#> 3 c     LCL    0

George Box’s advice — don’t transform if you can model the right distribution — applies. The exact Poisson limits use $q(0.99865)$ and $q(0.00135)$ of $\mathrm{Poisson}(\bar c)$ , the same coverage probability as classical 3-sigma limits but without the normal approximation.

np chart for constant n

When subgroup size is constant, the np chart plots the count rather than the proportion. Useful for direct interpretation when n is a round number:

fit_np <- shewhart_np(
  data.frame(day = 1:30, defects = rbinom(30, size = 200, prob = 0.04)),
  defects = defects,
  n       = 200,
  index   = day
)
broom::tidy(fit_np)
#> # A tibble: 3 × 3
#>   chart line  value
#>   <chr> <chr> <dbl>
#> 1 np    CL      7.4
#> 2 np    UCL    15.4
#> 3 np    LCL     0

u chart for variable exposure

When the inspection size differs (e.g. fabric rolls of different length, machine-hours of different duration), the right chart is u — defects per unit of exposure:

set.seed(1)
df_u <- data.frame(
  roll    = 1:25,
  defects = rpois(25, lambda = 4 * runif(25, 0.5, 1.5)),
  m2      = runif(25, 0.5, 1.5)
)
fit_u <- shewhart_u(df_u, defects = defects, exposure = m2, index = roll)
broom::tidy(fit_u)
#> # A tibble: 3 × 4
#>   chart line  value note                  
#>   <chr> <chr> <dbl> <chr>                 
#> 1 u     CL     4.33 ""                    
#> 2 u     UCL   NA    "varies with exposure"
#> 3 u     LCL    0    ""

References

Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley. Chapter 7.
Ryan, T. P. (2011). Statistical Methods for Quality Improvement (3rd ed.). Wiley. (On the inadequacy of 3-sigma limits for low-mean Poisson counts.)
Box, G. E. P., Hunter, W. G., & Hunter, J. S. (2005). Statistics for Experimenters (2nd ed.). Wiley.