NAIVE METHOD

##STEP 1

week <- 1:6
values <- c(17, 13, 15, 11, 17, 14)

##STEP 2

forecast_a <- values[-length(values)]
actual_a <- values[-1] 
mse_a <- mean((actual_a - forecast_a)^2)

##STEP 3

forecast_week7_a <- tail(values, 1)

cumulative_averages <- cumsum(values[-length(values)]) / (1:(length(values) - 1))

forecast_b <- cumulative_averages
actual_b <- values[-1]

##STEP 4

actual_b <- values[-1]
mse_b <- mean((actual_b - forecast_b)^2)

forecast_week7_b <- mean(values)

##STEP 5

better_method <- ifelse(mse_a < mse_b, "Most Recent Value", "Average of All Data")

##STEP 6

list(
  MSE_Most_Recent_Value = mse_a,
  Forecast_Week7_Most_Recent = forecast_week7_a,
  MSE_Average = mse_b,
  Forecast_Week7_Average = forecast_week7_b,
  Better_Method = better_method
)

## $MSE_Most_Recent_Value
## [1] 16.2
## 
## $Forecast_Week7_Most_Recent
## [1] 14
## 
## $MSE_Average
## [1] 8.272
## 
## $Forecast_Week7_Average
## [1] 14.5
## 
## $Better_Method
## [1] "Average of All Data"

$MSE_Most_Recent_Value [1] 16.2

$Forecast_Week7_Most_Recent [1] 14

$MSE_Average [1] 8.272

$Forecast_Week7_Average [1] 14.5

$Better_Method [1] “Average of All Data” ```