Estimates the Expected Number of Errors in a Data Entry Job
est_typing_err(S, F, prior = "postmen", quantity = "mean")
size of the evaluation sample
number of errors in the evaluation sample
can be either 'postmen', 'bad_postmen' or a named vector with prior values. See below for details.
can be either 'mean', '95q' or 'param'. See below for details.
The function calcualtes (Bayesian posterior) estimates for the expected number of errors (
quantity='mean') or the
upper bound of the 95% (Bayesian) credible interval (
quantity='95q') in the population of entered values when a
random sample of
S entered values contains
F errors. For a discussion on the type of inference, see Winkler
et al. (2002).
The calculations are done assuming a Beta-distribution prior over the proportion of errors for all entered values. The users must supply hyper-parameters for the Beta-prior. The function provides two informed default hyper-parameter sets. Both induce an informed prior and are formed from data about postmen's performance in typing post codes as reported in Baddeley/Longman (1978).
The first prior (
prior='postmen') says that the a-priori the proportion of errors relative to the total number of entered
values is about 1.4% with a variance 0.7. The second prior (
prior='bad_postmen') is more conservative saying that
it is about 2.1% with a variance of 1.1. Users can also supply their own prior using a named vector that contains the alpha
and beta hyper-parameters for the Beta prior (the output of
calc_beta_param can be used).
value or vector of the requested quanty/ies.
Baddeley, A. D., and D. J. A. Longman. 1978. "The influence of length and frequency of training session on the rate of learning to type." Ergonomics 21(8), 627-635.
Winkler, Robert L. and Smith, James E. and Fryback, Dennis G. 2002. "The Role of Informative Priors in Zero-Numerator Problems: Being Conservative Versus Being Candid". The American Statistician 56(1), 1-4.
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