What would this year's forecast be if we were using the naive approach
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What would this year's
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- Use the information below to answer question 2x + 3y + 3z = 2 4x – 3y – 6z = 2 10x – 6y + 3z = 0 1. The co-factor for the first element in the coefficient matrix for this system is: A. -27 B. -45 C. 27 D. 45For the E-Commerce Retail Sales (Million$) data given in the table below, provide estimates from the 1st Quarter (Q1) of 2016 to the 3rd Quarter (Q3) of 2017 by using two models: 1) Single Exponential Smoothing with α=0.3 2) Moving Average with k=2. Calculate MAPE for each model. Quarter Year Actual Q1 2016 86802 Q2 2016 92004 Q3 2016 93795 Q4 2016 124651 Q1 2017 99491 Q2 2017 106590 Q3 2017 108291 Compare two models above (Single Exponential Smoothing and Moving Average) based on their accuracies. Which forecasting method appears to be better? Using the model you choose, provide forecast for the 4th Quarter (Q4) of 2017. Assuming that this model is overestimating, find the actual value of the Q4 of 2017 based on MAPE value. Some residual graphs from the first forecast model (including wider range of E-Commerce Retail Sales data) are given below. What do these graphs tell about the model? Explain each graph.Use quantitative methods for the data shown below: Period 1 2 3 4 5 6 7 Observation 24 34 36 37 41 44 45 i) Naïve method: ii) Three period moving average iii) Three period Weighted moving average iiii)Four-period Moving Average iiiii)Four-period Weighted Moving Average b. Plot the original time series and comment on the appropriateness of a linear trend.
- A time-series trend equation is 25.3 + 21x.What is your forecast for period 7? A. 32.3 B. 172.3 C. 25.3 D. 40.0 E. 324.1Given the attached data. Answer the following questions for a 7 period moving average. t At 1 5477 2 3338 3 4799 4 2852 5 6332 6 8385 7 4382 8 4184 9 14229 10 10385 11 11676 12 5271 13 8938 14 9279 15 13990 16 2890 17 13027 18 8970 19 6700 20 7065 21 12491 22 8748 23 8396 24 6102 25 7559 26 7997 27 11981 28 10680 29 5783 30 2501 31 7620 32 10194 33 7908 34 11985 35 5863 36 3024 37 8758 38 1640 39 6569 40 11728 41 6659 42 2830 43 10018 44 8448 45 12409 46 2083 47 6374 48 7438 49 4576 50 4795 MAD = Average(|A-F|) TS =SUM(A-F)/MAD MSE = Average(A-F)2 1. Compute your forecast for period 51. The potential answers are: A: 11221.6 units. B: 12461 units. C: 8020.83 units. D: 4414 units. E: 6589 units.The following table shows a tool and die company’s quarterly sales for the current year. What sales would you predict for the first quarter of next year? Quarter relatives are SR1 = 1.10, SR2 = .99, SR3 = .90, and SR4 = 1.01.Quarter 1 2 3 4Sales 88 99 108 141.4
- Quarter 1 2 31 11 14 172 20 23 263 29 32 354 38 41 44Compute seasonal relatives for this quarterly data.a. Use the SA method.(a) Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating selling price to taxes paid. What is the estimate of o²? (b) Find the mean selling price given that the taxes paid are x = 7.50. (c) Calculate the fitted value of y corresponding to x = 5.8980. Find the corresponding residual.The number of internal disk drives (in millions) made at a plant in Taiwan during the past 5 years follows: Year Disk Drives 1 142 2 156 3 184 4 204 5 210 a) Using simple linear regression the forecast for the number of disk drives to be made next year= 234.4 disk drives b) The mean squared error when using simple linear regression = 24.64 drives2 c) The mean absolute percentage error (mape) when using simple linear regression= [___]% (round your response to 1 decimal place)
- Make provision for analysing time series using five most popular statistical approaches?Based on the following regression ANOVA table, what is R2? Source df SS MS F Regression 4 1793.2356 448.3089 7.4854 Residual 45 2695.0996 59.8911 Total 49 4488.3352 0.6005 0.3995 0.8807 0.7230Explain the main uses of time study information in time series ?