# accuracy of the modified unbiased forecast using the Mean Absolute Deviation (MAD) method

I'm supposed to be getting the answer 3.17 but I keep getting 86. What am I doing wrong with this code?

`````` #load package
library(Metrics)
library(tidyverse)
library(DescTools)
library(ggpubr)
library(InventoryAccountingWeek_1_exercise_1.7)
#define observed and predicted values
Month <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
Forecast <- c(115, 121, 117, 104, 109, 123, 113, 115, 92, 102, 101, 108)
Demand <- c(101, 113, 99, 90, 94, 109, 106, 100, 87, 89, 92, 96)
#calculate mean absolute error between vectors
mae(Forecast, Demand)
#1. What is the mean forecast error?
# 12
# The forecast is biased because it isn't zero.
# subtract the mean forecast error from each month's forecast to make it unbiased.
Forecast+Demand
# define data
data <- data.frame(Demand, Forecast)
#calculate MAD for columns in data frame
# Calculate the accuracy of unbiased forecast using MAD
mean <- c((Forecast)/12)
mean
mean(data)
110/12
mae(Forecast, Demand)
#subtract the mae from demand for each month
accuracy <- c(101-12, 113-12, 99-12, 90-12, 94-12, 109-12, 106-12, 100-12, 87-12, 89-12, 92-12, 96-12)
accuracy
#calculate the forecast error by subtracting the demand from the unbiased forecast.
mean(accuracy) (edited)``````

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