Description Usage Arguments Examples

Computes the approximate aggregate cumulative learning curve formula by calculating the sum of all contributing hours from all departments for all production units 1 through n.

1 |

`t` |
vector of hours (or costs) for the first unit from departments 1 through m |

`r` |
vector of historical learning rates for departments 1 through m |

`n` |
total units to be produced across all departments |

`na.rm` |
Should |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Not run:
# A project is expected to get underway soon to produce 300
# widgets. Three departments will be involved. Historically,
# these departments have had learning curves of 85%, 87%, and
# 80% respectively. The first unit hours for these departments
# have been estimated at 70, 45, and 25 respectively. What is
# the total predicted hours required for the entire effort?
t <- c(70, 45, 25)
r <- c(.85, .87, .8)
agg_curve(t = t, r = r, n = 300)
## [1] 11000.96
## End(Not run)
``` |

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