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April 19, 2022 02:02
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source("manifesto_functions.R") | |
### OPTIONAL A/B Case Study | |
### Code Slide #1 | |
# Declare list to hold configurations | |
base_configs <- list() | |
# Amount of simulation | |
base_configs["n_draws"] <- 100000 | |
# Product A Events (bugs, phis, malware etc) | |
base_configs["a_n_events"] <- 34 | |
# Product B Events (bugs, phis, malware etc) | |
base_configs["b_n_events"] <- 37 | |
# Time Frame: Months(12), Weeks(52), Days(365 | |
base_configs["time_multiple"] <- 365 | |
### Slide #2 | |
# Beliefs about error counts prior to testing | |
event_priors <- list() | |
event_priors["median_errors"] <- 0.20 | |
event_priors["edge_errors"] <- 0.45 | |
# True Error Counts | |
event_counts <- list() | |
event_counts["product_a_errors"] <- 10 | |
event_counts["product_b_errors"] <- 13 | |
### Slide #3 | |
# Forecasted range of error response hours that can happen per event | |
eng_hour_range <- list() | |
eng_hour_range["mode"] <- 3 # Expect hours | |
eng_hour_range["low"] <- 1 # Low hours | |
eng_hour_range["high"] <- 5 # Max hours | |
# Forecasted cost of responding to an error event for ONE HOUR | |
eng_cost_range <- list() | |
eng_cost_range["mode"] <- 600 # Expected cost per event | |
eng_cost_range["low"] <- 200 # Low end cost per event | |
eng_cost_range["high"] <- 2000 # High end cost per event | |
### Slide #4 | |
# Year 1 Deterministic Cost of Product A & B license or subscription etc | |
product_costs <- list() | |
product_costs["prod_a_cost"] <- 72000.00 | |
product_costs["prod_b_cost"] <- 65000.00 | |
product_costs["prod_a_run"] <- 10000 # Additional Operational Cost | |
product_costs["prod_b_run"] <- 10000 # Additional Operational Cost | |
runProductCostComparison(base_configs, event_priors, event_counts, | |
eng_hour_range, eng_cost_range, product_costs) |
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