Preparing an XmR plot is common when dealing with processes where a single product/item is made or measured and there is a significant time gap between the next production or observation. XmR plots can also be useful when dealing with outputs from a batch process rather than a continuous one. In this post, we will show how to make quick QC XmR plots with the ggQC package available on cran or github. cran: install.package("ggQC") To get us started, let’s simulate some data on the diameter of a golden egg produced monthly by a golden goose. set.seed(5555) Golden_Egg_df <- data.frame(month=1:12, egg_diameter = rnorm(n = 12, mean = 1.5, sd = 0.2) )…
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Control Charts with ggQC: XbarR
XbarR charts are useful when monitoring a continuous process over time and your taking multiple samples in a given period. Some examples might include, the first, middle, and last parts coming off an assembly line, subgroups of molded parts produced several at a time over several cycles, batch uniformity of continuously produced chemical / material. In this post, we will show how to make quick QC XbarR plots with the ggQC package available on cran or github. cran: install.package("ggQC") Generating an Xbar or XbarR plot with ggQC is simple. To get us started, let’s simulate some production line data on candles. The candles are shaped using a mold capable of producing…