The following table shows a company's annual revenue (in billions of dollars) for 2009 to 2014. Period (t) Revenue ($ billions) Year 2009 2010 2011 2012 2013 2014 80- 70- 1 (a) Construct a time series plot. 0 2 1 5 6 2 DENK 5 6 3 23.8 Period 29.2 37.9 50.2 59.7 66.8 ⓇO 0 1 2 Period 1 2 Period 5 6 no 20 10- 0 1 2 3 Period. Ⓡ
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- The table below contains the average price paid for a new home in a certain area from 2000 to 2010. a. Construct a time-series plot of new home prices. b. What pattern, if any, is present in the data? Year Average_Price_($_thousands)2000 351.12001 330.52002 310.52003 296.72004 229.72005 182.32006 154.52007 156.32008 154.72009 154.52010 154.5Using the time series data in the table, respond to the following items. Period Sales 1 $ 615 2 676 3 761 4 710 5 784 6 801 7 852 8 698 9 1,193 10 1,115 11 1,231 12 1,259 13 1,495 14 1,229 15 1,652 16 1,337 17 1,673 18 1,613 Please show work, thanks.The following table shows Google's annual revenue (in billions of dollars) for 2009 to 2014.† Year Period (t) Revenue ($ billions) 2009 1 23.7 2010 2 29.3 2011 3 37.9 2012 4 50.2 2013 5 59.8 2014 6 66.7 (a)Construct a time series plot. -A time series plot contains a series of 6 points connected by line segments. The horizontal axis ranges from 0 to 7 and is labeled: Period. The vertical axis ranges from 0 to 80 and is labeled: Revenue ($ billions). The points are plotted from left to right at regular increments of 1 period starting at period 1. Initially, the points are plotted in an upward, diagonal direction. However, after the third point, the points are plotted in a downward, diagonal direction that becomes steeper as period increases. The points are between 23 to 71 on the vertical axis. -A time series plot contains a series of 6 points connected by line segments. The horizontal axis ranges from 0 to 7 and is labeled: Period. The vertical axis ranges…
- Using the time series data in the table, respond to the following items. Period Sales 1 $ 615 2 678 3 761 4 710 5 784 6 801 7 852 8 698 9 1,193 10 1,115 11 1,231 12 1,259 13 1,495 14 1,229 15 1,652 16 1,337 17 1,673 18 1,613 d-1. Compute all possible forecasts using a trend forecasting model using simple linear regression? (Round your answers to 3 decimal places.) Period Sales Predicted Sales Absolute Error 1 615 2 678 3 761 4 710 5 784 6 801 7 852 8 698 9 1,193 10 1,115 11 1,231 12 1,259 13 1,495 14 1,229 15 1,652 16 1,337 17 1,673 18 1,613 d-2. What is the MAD? (Round your answer to 3 decimal places.) d-3. What is the trend equation based on the regression analysis? (Round your answers to 3 decimal places.) Sales = __________ + _______________ time…Define the term 'time series data' and continue to discuss the components of a time series.An 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.
- #4) Commuter ridership in Athens, Greece, during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the data are given in the following table. Year Number of Tourists (millions) Ridership (hundreds of thousands) 1 6 11 2 11 16 3 8 16 4 10 14 5 19 28 6 18 26 7 16 21 8 20 25 9 24 45 10 18 28 11 11 18 12 19 35 a) Create a time series plot for the ridership. b) Using linear regression to see if using the year is a good predictor for the ridership. What is the regression equation? How accurate is the model? c) Using linear regression to see if using the number of tourists is a good predictor for the ridership. What is the regression equation? How accurate is the model? d) Which linear regression equation is better? What is the expected ridership if 10 million tourists visit the city next year? e) Excel FileThe demand for a product for the last six years has been 15, 15, 17, 18, 20, and 19. The manager wants to predict the demand for this time series using the following simple linear trend equation: trt = 12 + 2t. What are the forecast errors for the 5th and 6th years?The data in the table above are: Time-series Qualitative Quantitative and continuous Cross-sectional Quantitative and discrete
- The following ratio-to-moving averages for the seasonally adjusted series were found by the decomposition method applied on a time series representing quarterly sales for January 2018 to December 2020 period: a. Calculate the Seasonal Index for every quarter. b. If the trend is described by the trend line T^ = 1,000 + 30 t, what is the forecast for the fourth quarter of 2021?Analyse the time series of the following supermarket sales data and present the results in graphical form, including a forecast for the daily sales in week 5. Supermarket sales (K000) for a particular period Week 1 Week 2 Week 3 Week 4 Monday 22 22 24 26 Tuesday 36 34 38 38 Wednesday 40 42 43 45 Thursday 48 49 49 50 Friday 61 58 62 64 Saturday 58 59 58 58After its move in 1990 to La Junta, Colorado, and its new initiatives, the DeBourgh Manufacturing Company began an upward climb of record sales. Suppose the figures shown here are the DeBourgh monthly sales figures from January 2001 through December 2009 (in $1,000s). a) Produce a time series plot. Are there any trends evident in the data? Does DeBourgh have a seasonal component to its sales? b) Deseasonalize the data using Multiplicative model with a 0.5 weighted moving average. Produce a time series plot of the deseasonalized data and add a trendline. c) Forecast the sales from January to December of the year 2010. d) Include a discussion of the general direction of sales and any seasonal tendencies that might be occurrinG Month 2001 2002 2003 2004 2005 2006 2007 2008 2009 January 139.7 165.1 177.8 228.6 266.7 431.8 381 431.8 495.3 February 114.3 177.8 203.2 254 317.5 457.2 406.4 444.5 533.4 March 101.6 177.8 228.6 266.7 368.3 457.2 431.8 495.3 635 April 152.4 203.2…