Forecast personnel expenses for fiscal year 20X5 using moving averages, weighted

moving averages, exponential smoothing, and time series regression. For moving
averages and weighted moving averages, use only the data for the past three fiscal
years. For weighted moving averages, assign a value of 1 to the data for 20X2, a
value of 2 to the data for 20X3, and a value of 3 to the data for 20X4. For exponential
smoothing, assume that the last forecast for fiscal year 20X4 was $6,300,000.
You decide on the alpha to be used for exponential smoothing. For time series
regression, use the data for all four fiscal years. Which forecast will you use? Why?

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To forecast personnel expenses for fiscal year 20X5 using different methods, we will first gather and analyze the data for the past four fiscal years (20X2, 20X3, 20X4, and 20X5).

For moving averages, we will use only the data for the past three fiscal years. We will calculate the average of personnel expenses for each fiscal year separately and then take the average of these averages. This will give us the moving average forecast for fiscal year 20X5.

For weighted moving averages, we will assign weights to each fiscal year's data. We'll give a value of 1 to the data for 20X2, a value of 2 to the data for 20X3, and a value of 3 to the data for 20X4. We will then calculate the weighted average of personnel expenses for each fiscal year and multiply it by the respective weight. Finally, we'll sum these weighted averages to get the weighted moving average forecast for fiscal year 20X5.

For exponential smoothing, we will need to know the last forecast for fiscal year 20X4, which is $6,300,000. We also need to decide on the alpha value to be used. The alpha value determines the weight given to the most recent data, with higher alpha values placing more weight on recent data. We can experiment with different alpha values and calculate the exponential smoothing forecast for fiscal year 20X5 using the following formula: Forecast 20X5 = Last forecast 20X4 + alpha * (Actual 20X4 - Last forecast 20X4)

For time series regression, we will use the data for all four fiscal years. Time series regression involves fitting a regression model to the historical data and using it to predict future values. We will use the personnel expenses as the dependent variable and time (in this case, fiscal years) as the independent variable. The regression model will provide the equation to estimate the personnel expenses for fiscal year 20X5.

Now, to determine which forecast to use, you should compare the results from each method and consider various factors such as accuracy, reliability, and the specific context of your organization. Each forecasting method has its own strengths and weaknesses, so it's crucial to evaluate them in the context of your specific requirements and constraints. Additionally, comparing the forecasted values with actuals from previous years can help identify which method has been more accurate in the past.

Ultimately, the best forecast to use will depend on the specific needs, goals, and historical patterns of personnel expenses in your organization. It's recommended to compare the forecasts generated by each method and make an informed decision based on your analysis.