The answer is that they use a different estimator. Note: Some people wonder why QI Macros results are a tiny bit different from some versions of other software. If the Range Chart looks okay, then calculate, plot, and evaluate the X Chart. If it is "out of control," so is the process. Process: Calculate, plot, and evaluate the Range Chart first. * "Introduction to Statistical Quality Control," Douglas C. K = number of subgroups ( a group of measurements produced under the same set of conditions)Ī 2, D 3 and D 4 are constants based on n Free Agile Lean Six Sigma Trainer Training.Animated Lean Six Sigma Video Tutorials.Introduction to Statistical Quality Control by Douglas C. The data on which the test was run Methods (by class) The name containsĪ standardized list of test run results: statisticįor the test statistic, lcl and ucl for the 95Ĭonfidence bounds, p for the p-value, signal status, and Named boolean of whether the test was run. 4 is nine consecutive points over the process mean.Ī named list of class mdsstat_test object, as follows:Įnglish description of what was analyzed status 3 is four of five consecutive points over the 1-sigma 2 is two out of three consecutive points over theĢ-sigma limit. We_rule has four possible values: 1 is one point over theģ-sigma limit. The most recentįunction xbar() is an implementation of the x-barĬontrol Chart test from the family of statistical process control tests Optional value of the in-control process standard deviation,ĭefault: NULL estimates the in-control process standard deviationįrom timepoints prior to the most recent timepoint in the time series usingĪ moving range calculation assuming an n=2 sampling approach. Measurement is then tested using this estimate. Prior to the most recent timepoint in the time series. Optional value of the in-control process mean, typically measuredĭefault: NULL estimates the in-control process mean from timepoints See details for descriptions.ĭefault: 1 represents the first Western Electric rule of one point Required integer from 1 to 4 representing the Because Shewhart does not perform well on time series with many 0 values,ĭefault: 1/3 requires no more than 1/3 zeros in events in (constrained by eval_period) containing zeroes for this algorithm to Required maximum proportion of events in df This will be used toĮstablish the process mean and moving range. Times counting in reverse chronological order to assess. Optional positive integer indicating the number of unique If specified, this will override the name of theĭefault: NA indicates no English description for plain dfĭata frames, or ts_event English description for df data frames Optional string indicating the English description of what Name is generated from mds_ts metadata.Įxample: c("Rate of Bone Filler Events in Canada"="rate") The name of the string is an English description of what wasĭefault: c("Count"="nA") corresponding to the event count column in Rate mustīe calculated in a separate column in df as it is not calculated byĭefault. Indicating the variable corresponding to the event count or rate. Usage, any data frame with the following columns:Įither the event count or rate of class numericįurther arguments passed onto xbar methods Required input data frame of class mds_ts or, for generic ) # Default S3 method: xbar ( df, analysis_of = NA, eval_period = NULL, zero_rate = 1 / 3, we_rule = 1, mu = NULL, sigma = NULL. ) # S3 method for class 'mds_ts' xbar ( df, ts_event = c ( Count = "nA" ), analysis_of = NA. test_as_row: Coerce mdsstat Test to 1-Row Data Frame.sprt: Sequential Probability Ratio Test.next_ev: Return next level up event Returns the variable name of the.next_dev: Return next level up device Returns the variable name of the.mds_ts: Sample List of 'mds_ts' Time Series.maude: Bone Cement MAUDE Events in 2017.input_param_checker: Check Input Parameters.gps: Empirical Bayes Gamma-Poisson Shrinker.fNA: Min/Max With All NA's Allowed Min and Max functions that.ewma: Exponentially Weighted Moving Average (EWMA).E2x2: Calculate expected for 2x2 table of observed Returns a vector.define_algos: Set List of Algorithms to Run.convert_date: Convert to Acceptable Date.char_to_df: Character Vector to Header of Empty Data Frame Converts a.bcpnn: Bayesian Confidence Propagation Neural Network.
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