@@ -881,35 +881,3 @@ anomalies and identifying root causes via fault trees is one such
881881generalizable approach that can serve as a guide in a wide variety of
882882data analyses.
883883
884- Fault Tree Notation {#sec: notation }
885- ===================
886-
887- Comprehensive details on constructing fault trees can be found in
888- Vesely, et al. [ @vesely1981fault ] and Ericson [ @ericson2011fault ] . We
889- provide an abbreviated summary here that is sufficient for understanding
890- the fault trees in this paper.
891-
892- If we consider the system shown in
893- Figure [ 1] ( #modelSLR ) {reference-type="ref" reference="modelSLR"}, we
894- could implement it with the following R code.
895-
896- ## Read in data from CSV file
897- dat <- read.csv("dataset.csv")
898-
899- ## Fit linear model
900- fit <- lm(y ~ x, data = dat)
901-
902- ## Extract estimated coefficients
903- b <- coef(fit)
904-
905- ## Return `b'
906-
907- If we assume that the code underlying the execution of the system works
908- as expected and that the data are properly formatted and clean, then the
909- code will execute and coefficient vector ` b ` will be returned. If we are
910- to consider what are the failure modes of this system, it must be noted
911- that the coefficient vector ` b ` is the * only* output from the system
912- according to Figure [ 1] ( #modelSLR ) {reference-type="ref"
913- reference="modelSLR"}. Therefore, any failure modes associated with the
914- system must correspond to the coefficient values.
915-
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