Use exponential smoothing with a smoothing constant of 0.3 to forecast the demand for fertilizer in year 12. Assume that last period's forecast for year 1 is 5,000 bags to begin the procedure. Bags are shown in 1,000's in table below. Year Demand (Bags) 4 6. 3. 4 10 6. 8. 7. 8. 8. 6. 12 10 14 11 15 O A. 14.00 B. 14.87 O C. 11.79 D. 13.67

Use exponential smoothing with a smoothing constant of 0.3 to forecast the demand for fertilizer in year 12. Assume that last period's forecast for year 1 is 5,000 bags to begin the procedure. Bags are shown in 1,000's in table below. Year Demand (Bags) 4 6. 3. 4 10 6. 8. 7. 8. 8. 6. 12 10 14 11 15 O A. 14.00 B. 14.87 O C. 11.79 D. 13.67

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 7E: The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute...

See similar textbooks

Similar questions

Suppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through year 7. The data are: (c1p2) Year Retail Store Sales 1 $1,225 2 1,285 3 1,359 4 1,392 5 1,443 6 1,474 7 1,467 54 Chapter One a. Use the first naive forecasting model presented in this chapter to prepare a forecast of retail store sales for each year from 2 through 8. b. Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8.) c. Calculate the root-mean-squared error for your forecast series using the values for year 2 through year 7. 3. Use the second naive forecasting model presented in this chapter to answer parts (a) through (c) of Exercise 2. Use P 0.2 in…
A local moving company has collected data on the number of moves they have been asked to perform over the past two years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method to forecast the number of customers for the coming year. The equation for the trend line of yearly sales is Ft = 16 + 60t. Please forecast demand for each quarter in Year 3. (Round the forecasts to whole numbers and show all calculations). Complete the table below and forecast the sales of Year 3 by quarter. Year 1 Year 2 Year 3 Quarter Demand Seasonal Index Quarter Demand Seasonal Index Average Seasonal Index Forecast 1 20 1 27 2 40 2 45 3 45 3 55 4 31 4 41 Total Average
If the annual rate of world oil consumption t years after 1970 is modeledby the equation R(t) = 16.1e^.07t (billions of barrels per year), how much total oil (net change) was used between 1976 and 1980?
Consider the following time series data: Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 Compute MSE using the most recent value as the forecast for the next period. What is the forecast for month 8? Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for month 8? Which method appears to provide the better forecast?
A researcher has a sample of 6 annual observations {94, 104, 102, 99, 111 and 107} for the CPI in country Z for the period 2015 to 2020, and wants to forecast CPI for the years 2021, 2022 and 2023. The researcher uses 3 different forecasting models: A, B and C. Model A is an AR(1) model with no drift and with an estimated autoregressive coefficient = 0.7. Model B is a MA(1) model with no constant and with an estimated MA coefficient = -0.4 (note the minus !). Model C is a random walk model with no drift. The error terms over the 2015-2020 period were estimated to have the values: {3, -1, 2, 4, -3, 1}. a. Compute the 2021, 2022 and 2023 forecasted values for the consumer price index based on the three models. Show the formulas and the details of your calculations, and explain all the related symbols. b. Suppose that the actual values of the CPI over the 2021, 2022 and 2023 were {108, 114, 105}. Calculate the Root mean square error of the three model forecasts over the 2021-2023…
There has been coherent effort by various national agencies to educate and encourage reduction in energy consumption among Singapore household over the years. As part of your social science school project, you have chosen to research on whether such campaign has found success among households living in 1-room and 2-room HDB flats. You have downloaded the latest town gas consumption data for further analysis.Comment on the underlying pattern in the average quarterly household town gas consumptions over 2010 Q1 – 2021 Q2. Assume that today is 1st August 2021.
Which of the following is NOT TRUE about trend analysis? a) In trend analysis every item is expressed as an indexb) To compute index values, one of the years/ periods should be chosen as a base yearc) The purpose of trend analysis is to detect the trend of each item as of the previous yeard) The index values are computed by diving each item by the base year figure
Why is base 10 logarithm (log) derived as a natural logarithm (1/x2)? Please confirm that derivate of logarithm of base 10 is in this case 1/[x2*log(10)] to modify your answer. Thank you
21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.
Exponential smoothing gives more weight to the _______________ observations and less to the _______________ observations. Multiple Choice smaller, larger recent, older older, recent larger, smaller
Plot the logarithm of arrivals for each transportation mode against time, all on the same graph. Which now appears to be growing the fastest?
The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute the equation of a trend line (similar to Equation 5.4) for these sales data to forecast sales for the next year. (Let 2004=0,2005=1, etc., for the time variable.) What does this equation forecast for sales in the year 2014? Use a first-order exponential smoothing model with a w of 0.9 to forecast sales for the year 2014.