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What would this year's
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- 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.Make provision for analysing time series using five most popular statistical approaches?Obtain estimates of daily relatives for the number of customers at a restaurant for the evening meal, given the following data. Day 1 23456789GH23H 10 11 12 13 14 Number Served 87 82 83 99 145 142 48 80 79 87 92 134 138 41 Day 15 PEAREANNN8 16 17 18 19 20 21 22 23 24 25 26 27 28 Number Served 82 83 88 99 132 148 46 82 83 81 90 133 139 44 Click here for the Excel Data File: a. Use the centered moving average method. (Hint: Use a seven-day moving average.) (Round your intermediate calculations and final answers to 4 decimal places.)
- Compute seasonal relatives for this data using the SA method:Quarter Year 1 Year 2 Year 3 Year 41 2 3 7 42 6 10 18 143 2 6 8 84 5 9 15 11Develop Linear Regression Model. Forecast sales for 8, 14 and 17 years of education. Sales(1000s) Years of Education 24 10 18 9 20 11 22 6 26 13 23 16 21 18Seasonal relatives. Obtain estimates of quarter relatives for these data using the centered moving average method: YEAR 1 2 3 4Quarter: 1 2 3 4 1 2 3 4 1 2 3 4 1Demand: 14 18 35 46 28 36 60 71 45 54 84 88 58
- ces Obtain estimates of quarter relatives for these data using the centered moving average method. ( decimal points and standardize the final seasonal relatives so their total is four.) Quarter Demand Year 1 2 3 4 1 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 16 20 35 49 31 36 65 74 50 52 92 86 61 Quarter 1 2 3 4 1 2 3 4 1 2 3 4 1 Year MA 2 MA₂ Demand/MA2STAT 382 (page 3 of 10)-Google Chrome arn.squ.edu.om/mod/quiz/attempt.php?attempt D17403728icmid 8666708page-2 E-LEARNING SYSTEM (ACADEMIC) me Series forecasting for Business || Spring21 Time left 0:30:09 stion The predicted monthly sales for a company over the first six months of 2010 are 360, 385, 275, 300, 312, and 307. Find the standard deviation. put of O A. 33.74 O B. 41.04 O C. 40.72 O D. 41.72 IOUS PAGE NEXT PAGE3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.
- For the E-Commerce Retail Sales (Million$) data given in the table below, provide estimates from the 1st Quarter (Q1) of 2016 to the 3rd Quarter (Q3) of 2017 by using two models: 1) Single Exponential Smoothing with α=0.3 2) Moving Average with k=2. Calculate MAPE for each model. Quarter Year Actual Q1 2016 86802 Q2 2016 92004 Q3 2016 93795 Q4 2016 124651 Q1 2017 99491 Q2 2017 106590 Q3 2017 108291 Compare two models above (Single Exponential Smoothing and Moving Average) based on their accuracies. Which forecasting method appears to be better? Using the model you choose, provide forecast for the 4th Quarter (Q4) of 2017. Assuming that this model is overestimating, find the actual value of the Q4 of 2017 based on MAPE value. Some residual graphs from the first forecast model (including wider range of E-Commerce Retail Sales data) are given below. What do these graphs tell about the model? Explain each graph.Make a 15-year simple linear regression forecast. Example: Railroad Products Co. RPC Sales Car Loadings Year ($millions) (millions) 1 9.5 120 2 11.0 135 3 12.0 130 4 12.5 150 5 14.0 170 6 16.0 190 7 18.0 220Suppose these data show the number of gallons of gasoline sold by a gasoline distributor in Bennington, Vermont, over the past 12 weeks. Sales (1,000s of gallons) Week 1 17 2 21 3 19 23 5 18 6 16 7 21 8 19 9 23 10 21 11 16 12 23 (a) Using a weight of - for the most recent observation, for the second most recent observation, and - for third most recent observation, compute a three-week weighted moving average for the time series. (Round your answers to two decimal places.) Time Series Value Weighted Moving Average Forecast Week 1. 17 2 21 19 4 23 5 18 6 16 7 21