#Naive Forecasting #Method 1

#Time Series Data week <- 1:6 #This is the independent variable - time sales <- c(17,13,15,11,17,14) #dependent variable

#Part A. Most recent value as Forecast forecast_a <- sales[-length(sales)] #Excludes the last sale actual_a <- sales[-1] #Exclude the first sale mse_a <- mean((actual_a - forecast_a)^2) mse_a #Mean Square error is 16.2

#Forecast the sales for week 7 forecast_week7_a <- tail(sales,1) forecast_week7_a #Interpretation: The number of sales projected for week 7 is 14.

#Part B. Average of all data as forecast #Note: Were still working with the same data set in line 4 & 5

cumulative_averages <- cumsum(sales[-length(sales)]) / (1:(length(sales) -1 )) cumulative_averages forecast_b <- cumulative_averages actual_b <- sales[-1] #Exclude the first value mse_b <- mean((actual_b - forecast_b)^2) mse_b #Mean square error is 8.27

#Forecast the sales for week 7 forecast_week7_b <- mean(sales) #Average of all weeks as forecast for week 7 forecast_week7_b #Interpretation: The number of sales projected for week 7 is 14.5

#Part C. Comparison better_method <- ifelse(mse_a < mse_b, “Most Recent Value”, “Average of All Data”)

#Results 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 )