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- Demand for haircut at PogiPoints Barber Shop has increased steadily for the past few months as seen in the following time series data. Do the necessary computations and choose the best answer that would complete each statement. The demand forecast for Month 6 would be: * A. 565 haircuts B. 574 haircuts C. 578 haircuts D. 584 haircutsFigure 6(a) shows the time series of the annual suicide rate (per 100,000 inhabitants) in Australia, between 1915 and 2004. Figure 6(b) shows the first differences of the rate and Figure 6(c) shows the second differences. Suicide rato 1600- 14 00 10.00 8.00- 6.00- (a) 1915 1325 2.00- A EM 0.00- 2.00- (1920 1045 1955 Year 1965 MY way 1075 1985 4.00- 1995 -4.00- Figure 6(a), (b) & (c) (b) 1915 (929 1942 ·1960 Year Sist y 1385 1005 2.00- 0.00- 2.00- -4.00- (c) Ho 1915 1925 1938 ·1245 A9985 Year 1205 A 1975 A ·$353 easy Is the suicide rate stationary over the period? Identify the order of differencing d required to obtain stationarity in mean and in variance. Explain your answer.The data for this problem are the air freight shipments (in ton-miles) each month (beginning in January for one year) in the U. S. for 10 years. The following is a time series plot of this data. What are the most important features of this plot? Ton-miles 3000- 2500- 2000- 1500- 1000- 1 Time Series Plot of Ton-miles ܚ ܪܘܐܐܐ ܘ ܚܐ ܀ 12 24 36 48 60 Index 72 84 96 108 120
- Q. 27 What are thẹ various methods available for computing seasonal variations in time series ?Q.13 Describe models of a time series. CS Scanned with CamScannerTable 7 shows the seasonal factors for a monthly time series: Table 7 Month Seasonal Month Factor January -358.1 May February -196.3 June March 12.5 July April 187.4 August Describe the seasonal variation in this time series. Seasonal Factor 487.2 276.1 -27.9 -124.6 Month September October November December Seasonal Factor -136.7 92.6 70.4 -282.6
- Table 1 shows the monthly sales of tires over the last 5 years. Graphically analyze the time series and explain how you could use the simple model to forecast monthly sales. Table 1: Monthly tires sales over 5 years Month Years 1 January 19 February 20 March 20 April May June 22 27 27 July 24 August 30 September 32 October 36 November 37 December 42 LO 2 3 4 5 27 29 33 36 26 28 32 34 30 37 41 43 30 39 44 47 35 40 50 49 32 35 52 51 26 23 44 40 30 35 45 43 35 40 53 49 39 49 63 57 38 46 54 55 47 56 71 66An article in Quality Engineering presents viscosity data from a batch chemical process. A sample of these data is in the table. Reading left to right and up to down, draw a time series plot of all the data and comment on any features of the data that are revealed by this plot. Consider that the first 40 observations (the first 4 columns) were generated from a specific process, whereas the last 40 observations were generated from a different process. Does the plot indicate that the two processes generate similar results? Calculate the sample mean and sample variance of the first 40 and the second 40 observations.State the model used in time series analysis.
- Question 2: Find the trend for the time series using moving average method. Forecast for Year 4 quarter 4 Year Sales (000's) Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 25 42 55 36 16 50 53 40 28 58 69 59 2N 3.Question 1: Find the trend for the time series using moving average method. Forecast Week 4 Wednesday Week Absence due to sickness Monday Tuesday Wednesday Thursday Friday 7 8 11 18 3 8 10 13 21 6. 13 17 28 2. 3,Suppose a man invested $250 at the end of 1900 in each of three funds that tracked the averages of stocks, bonds, and cash, respectively. Assuming that his investments grew at the rates given in the table to the right, approximately how much would each investment have been worth at the end of 2007? His investment in the fund tracking stocks would be worth approximately $. (Do not round until the final answer. Then round to two decimal places as needed.) Category Stocks Bonds Cash Average Annual Return 6.7% 2.4% 0.7%