Forecasting Problems: Question 2 Your Answer Is Partially Co

Forecasting Problemsquestion 2your Answer Is Partially Correct Try A

Forecasting Problems Question 2 Your answer is partially correct. Try again. Hospitality Hotels forecasts monthly labor needs. (a) Given the following monthly labor figures, make a forecast for June using a three-period moving average and a five-period moving average. (Round answers to 2 decimal places, e.g., 15.25.) Month Actual Values: January 36, February 42, March 42, April 45, May - Period Moving Average, 5-Period Moving Average. (b) What would be the forecast for June using the naì¯ve method? (Round answers to 2 decimal places, e.g., 15.25.) Forecast for June. (c) If the actual labor figure for June turns out to be 45, what would be the forecast for July using each of these models? (Round answers to 2 decimal places, e.g., 15.25.) 3-Period Moving Average, 5-Period Moving Average, Naïve method. (d) Compare the accuracy of these models using the mean absolute deviation (MAD). (Round answers to 2 decimal places, e.g., 15.25.) MAD (3-period), MAD (5-period), MAD (naïve). (e) Compare the accuracy of these models using the mean squared error (MSE). (Round answers to 2 decimal places, e.g., 15.25.) MSE (3-period), MSE (5-period), MSE (naïve). (Question continues with more forecasting questions, which have been consolidated for clarity.)

Paper For Above instruction

Forecasting plays a crucial role in various industries, enabling businesses to anticipate future demand and allocate resources effectively. The problems presented highlight different forecasting techniques, including moving averages, naive methods, exponential smoothing, linear regression, seasonal indices, and methods for evaluating forecast accuracy.

Part A: Moving Averages and Naïve Forecast

The initial task involves forecasting monthly labor needs for a hotel using moving averages. With the given data: January (36), February (42), March (42), April (45), and May (unknown), the three-period moving average for June would be calculated by averaging the labor figures of March, April, and May. Since May data isn't provided explicitly, assuming its value or calculating based on available data, the forecast for June centers around the mean of the three most recent months. Similarly, the five-period moving average uses the last five months' data, which, with only four months of data, might require adjustments or initial estimates.

The naïve method assumes that the forecast for June equals the actual labor figure of May. This approach is simple but often less accurate when trends or seasonal patterns are present. When actual data for June is available, the forecast for July can be computed using each model: the last three months' average for the moving average models and the June actual for the naïve method.

Part B: Forecast Accuracy Metrics

Evaluating the models' performance involves calculating metrics such as MAD and MSE. The MAD measures average absolute deviations, indicating the typical forecast error, while MSE emphasizes larger errors by squaring the deviations. Comparing these metrics across models allows us to determine which forecasting method yields the most accurate predictions.

Part C: Exponential Smoothing and Seasonal Forecasting

In another scenario, a health clinic considers using exponential smoothing to forecast demand, selecting different smoothing constants (α = 0.7 and α = 0.1). By computing MAD for each, the model with the lower MAD provides a better historical fit, thus guiding the choice of α.

For seasonal demand forecasting at a ski resort, understanding seasonal patterns enables the calculation of seasonal indices. Using historical data from two years, the seasonal indices help to adjust the overall forecast of 4,020 visitors for each season based on their relative seasonal patterns.

Forecasts for each season are calculated as the product of the total annual demand and the seasonal index, with intermediate steps involving averaging seasonal demand and computing indices.

Part D: Daily and Trend Forecasts

Forecasting daily demand at a restaurant incorporates the average customer data across weeks. By averaging demand for each day over the past two weeks, the forecast for the upcoming week assigns daily expectations, considering the seasonal variation and days closed.

Similarly, forecasting school enrollment employs a linear trend line derived from past data points, projecting into future years. This approach captures the steady increase in enrollment over time.

Part E: Regression and Correlation Analysis

Analyzing the relationship between temperature and resort attendance involves computing the regression equation and correlation coefficient, informing how well temperature predicts attendance. A strong correlation suggests reliable forecasts based on temperature data.

Using this relationship, forecasts for next month's attendance are derived, emphasizing the importance of accurate regression modeling for operational planning.

Part F: Seasonal Index Calculation and Forecasting

Small Wonder amusement park's seasonal attendance data enables the calculation of seasonal indices, which are pivotal in adjusting forecasts to reflect seasonal variations. Applying these indices to the expected annual attendance facilitates precise quarterly forecasts.

Exponential smoothing applied to quarterly data demonstrates how different smoothing constants affect forecast accuracy, guiding the selection of the most suitable model based on MAD and MSE.

Part G: Pies Sales and Method Evaluation

Forecasting pie sales using weighted moving averages incorporates previous demand data with assigned weights, providing nuanced forecasts sensitive to recent trends. Comparing this with naïve forecasts and analyzing MAD reveals the superior method.

Finally, assessing multiple forecasting methods over previous months employs MAD and MSE to identify the most effective approach, essential for enhancing future predictions.

Conclusion

The diverse examples illustrate the importance of selecting appropriate forecasting techniques aligned with data patterns and business contexts. Whether using simple moving averages, exponential smoothing, regression analysis, or seasonal adjustments, careful evaluation through accuracy metrics ensures reliable decision-making and resource planning.

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