Use simple exponential smoothing with a = 0.6 to forecast the tire sales for September through December. Assume that the forecast for August was for 46 sets of tires. Do your forecasts seem to be biased? Why or why not? Month Sales 53 August September35 October 48 November 40
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- 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…(Ch7) If the mean time between in-flight aircraft engine shutdowns is 12,500 operating hours, what is the 90 percentile on the distribution of the number of hours until the next shutdown? (hint: convert the mean time between events to the mean events per hour λ, then apply inverse exponential) Question 7Select one: a. 20,180 hours b. 18,724 hours c. 23,733 hours d. 28,782 hoursConsider 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 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 AverageThe following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Jan Feb Mar AprMonth 1 2 3 4Number of Accidents 30 40 70 105 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): y = ? + ?xDefine Forecasts and forecast errors in time series anaylsis?
- Suppose you have an extra six months of data on demands and prices, in addition to the data in the example. These extra data points are (350,84), (385,72), (410,67), (400,62), (330,92), and (480,53). (The price is shown first and then the demand at that price.) After adding these points to the original data, use Excel’s Trendline tool to find the best-fitting linear, power, and exponential trend lines. Then calculate the MAPE for each of these, based on all 18 months of data. Does the power curve still have the smallest MAPE?Can you explain what these two belows mean in regard of GMM and Maximum likelihood. What are we calculating and what is it used to Unconstrained optimizationConstrained optimizatioA firm experienced the demand shown in the following table. *Unkown future value to be forecast Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (with a w=0.9 and a w=0.3). Note The exponential smoothing forecasts may be begun by assuming Y t+1=Yt. Using the forecasts from 2005 through 2009, compare the accuracy of each of the forecasting methods based on the RMSE criterion. Which forecast would you have used for 2010? Why?
- In the trend model Tt = b0 + b1*Timet, suppose that b1 > 0. Is the series expected to grow or decline in future periods? Does this mean that the series grow or decline with certainty in every period?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…Consider the following model: yhat = 2.6+-0.9x² The prediction of y is yhat. What is the estimated marginal effect of x on y when x=2.7? PLZ MAKE SURE THIS IS RIGHT!!!