ss.ci | R Documentation |
Computes a confidence interval for the mean of the variable (parameter or feature of the process), and prints the data, a histogram with a density line, the result of the Shapiro-Wilks normality test and a quantile-quantile plot.
ss.ci(
x,
sigma2 = NA,
alpha = 0.05,
data = NA,
xname = "x",
approx.z = FALSE,
main = "Confidence Interval for the Mean",
digits = 3,
sub = "",
ss.col = c("#666666", "#BBBBBB", "#CCCCCC", "#DDDDDD", "#EEEEEE", "#FFFFFF", "#000000",
"#000000")
)
x |
A numeric vector with the variable data |
sigma2 |
The population variance, if known |
alpha |
The eqn\alpha error used to compute the |
data |
The data frame containing the vector |
xname |
The name of the variable to be shown in the graph |
approx.z |
If TRUE it uses z statistic instead of t when sigma is unknown and sample size is greater than 30. The default is FALSE, change only if you want to compare with results obtained with the old-fashioned method mentioned in some books. |
main |
The main title for the graph |
digits |
Significant digits for output |
sub |
The subtitle for the graph (recommended: six sigma project name) |
ss.col |
A vector with colors |
When the population variance is known, or the size is greater than 30,
it uses z statistic. Otherwise, it is uses t statistic.
If the sample size is lower than 30, a warning is displayed so as to
verify normality.
The confidence Interval.
A graph with the figures, the Shapiro-Wilks test, and a histogram.
Thanks to the kind comments and suggestions from the anonymous reviewer of a tentative article.
EL Cano
Cano, Emilio L., Moguerza, Javier M. and Redchuk, Andres. 2012. Six Sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York. https://link.springer.com/book/10.1007/978-1-4614-3652-2/.
ss.data.rr
ss.ci(len, data=ss.data.strings, alpha = 0.05,
sub = "Guitar Strings Test | String Length",
xname = "Length")
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