Unit 5 Dropbox Assignment Answers By Insert Your Name 628882

Unit 5 Dropbox Assignment Answers By Insert Your Name Herein The Sum

In the summary tables below, insert only the answers. You will show work after the summary section. Question 1 MAD MSE Question 2 a) Moving average forecast for year 13 b) Weighted moving average forecast for year 13 c) MAD for part a d) MAD for part b e) Recommended forecast method (justify): Question 3 R-squared for Linear model R-squared for polynomial model Regression formula for linear model Regression formula for polynomial model Recommended forecast method (justify): Work Show all your work for the questions below. Question 1 Show the errors you calculated. Question 2 Show the two forecasts and the errors. Question 3 Show the polynomial and linear trendline charts from Excel charting. Do not use multiple regressions for this question. Unit 5 [GB513: Business Analytics] 1 of 5 Unit 5: Assignment This Assignment requires you to use Excel. Make sure to use the Unit 5 Assignment template located in Course Resources when you turn in your answers. Question 1 Determine the error for each of the following forecasts. Then, calculate MAD and MSE. Period Value Forecast Error 1 202 — — Question 2 The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown here are factory orders in the United States over a 13-year period ($ billion). First, use the data to develop forecasts for years 6 through 13 using a 5-year moving average. Unit 5 [GB513: Business Analytics] 2 of 5 Then, use the data to develop forecasts for years 6 through 13 using a 5-year weighted moving average. W eight the most recent year by 6, the previous year by 4, the year before that by 2, and the other years by 1. Answer the following questions: a) What is the forecast for year 13 based on the 5-year moving average? b) What is the forecast for year 13 based on the 5-year weighted moving average? c) What is the MAD for the moving average forecast? d) What is the MAD for the weighted moving average forecast? e) Which forecasting model is better? Year Factory orders 1 2,512.,739.,874.,934.,865.,978.,092.,052.,145.,114.,257.,654. Question 3 The “Economic Report to the President of the United States” included data on the amounts of manufacturers’ new and unfilled orders in millions of dollars. Shown here are the figures for new orders over a 21-year period. Use the charting tool in Excel to develop a regression model to fit the trend effects for the data. Use a linear model and then try a polynomial (order 2) model. Make sure the charts show the line formula and the r-squared value. Include both charts in your report. Then, answer the following question: — How well does either model fit the data? Which model should be used for forecasting? Explain using the relevant metrics. Unit 5 [GB513: Business Analytics] 3 of 5 Year Total Number of New Orders 1 55,475,000,000,000,000,000,000,000

Paper For Above instruction

The assignment from Unit 5 of the Business Analytics course involves analyzing time series data to generate forecasts using different modeling techniques and evaluating their effectiveness. The tasks encompass calculating forecast errors, determining the most appropriate forecasting method based on accuracy metrics, and applying regression analysis to model trend effects. This comprehensive approach enables a deeper understanding of forecasting methods and their application in business contexts, particularly when analyzing manufacturing and economic data over time.

Introduction

Forecasting plays a vital role in business analytics, providing insights for decision-making based on historical data. The assignment requires applying various forecasting methods—specifically moving averages, weighted moving averages, and regression analysis—to real-world data, and judging their effectiveness through error metrics such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and R-squared. This process highlights how different models fit data trends and aid in accurate predictions for future periods.

Question 1: Calculating Errors, MAD, and MSE

In the first task, students are asked to determine the forecast errors for each period by subtracting the forecasted value from the actual value. The errors serve as the basis for calculating aggregate accuracy metrics such as MAD and MSE. MAD conveys the average magnitude of forecast errors, while MSE penalizes larger errors by squaring the deviations. These metrics are instrumental in comparing different forecasting models and selecting the most reliable one.

Question 2: Developing Forecasts Using Moving Averages

The second question involves designing forecasts for factory orders over a 13-year span using two methods: the simple moving average (SMA) with a 5-year window, and a weighted moving average giving greater importance to more recent years. This application emphasizes the importance of selecting appropriate weights and window sizes to capture underlying trends without overreacting to short-term fluctuations.

The forecast for year 13 is derived as the average of the previous five years' actual data for the simple moving average. For the weighted moving average, specific weights are assigned to the last four years, with the most recent year receiving the highest weight, thus reflecting a bias towards recent data. The comparison of MAD values for both models provides a quantitative basis for selecting the better technique. Typically, weighted moving averages tend to adapt more quickly to recent changes, but the choice depends on the data's nature and stability.

Question 3: Regression Analysis for Trend Modeling

The third task calls for fitting both linear and polynomial regression models to 21-year economic order data. The process involves charting the data alongside the trendlines, including formulas and R-squared values, which measure model fit. The linear model captures a straight-line trend, suitable for data with consistent growth or decline, whereas the polynomial (order 2) model accounts for curvature, capturing more complex patterns.

By evaluating the R-squared values, students can discern which model provides a better explanation of the data variability. Generally, higher R-squared indicates a superior fit. In cases where the polynomial model significantly improves the R-squared over the linear model, it suggests that the trend is non-linear and warrants a polynomial approach. Nonetheless, caution must be exercised to avoid overfitting, especially with higher-degree polynomials.

Discussion and Conclusion

The assignment illustrates the importance of selecting suitable forecasting models based on accuracy metrics and data behavior. Moving averages are straightforward and useful for smoothing out short-term fluctuations. Regression models, especially polynomial ones, are powerful in capturing complex trends but require careful interpretation of fit metrics. In forecasting business and economic data, combining these approaches—using moving averages for short-term predictions and regression models for understanding long-term trends—is often most effective.

The analysis underscores that no single model is universally best; instead, the choice depends on data characteristics and the purpose of forecasting. Employing R-squared and error metrics helps analysts make informed decisions, improving forecast accuracy and supporting strategic planning in business environments.

References

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