(c) Use the trend equation developed in part (b) to forecast retail store revenue (in $ billions) for 2018. (Round your answer to one decimal place.)$billion Annual retail store revenue for a technology company from 2007 to 2017 are shown below.Retail Store Revenue($ billions)Year Period2007O20082009201020112012201320142015201620173530-25201510-15203(a) Construct a time series plot.45678910113.9156.1106.7773530-2520MLNM1510-50 +0 1 2 3 4 5 6 7 8 9 10 11 120 1 2 3 4 5 6 7 8 9 10 11 12Period9.28014.32719.02820.42821.66228.30926.99830.7030 1 2 3 4 5 6 7 8 9 10 11 12PeriodWhat type of pattern exists in the data?O The time series plot shows a cyclical pattern.O The time series plot shows a horizontal pattern.O The time series plot shows a downward trend.O The time series plot shows an upward trend.3530+252015+1050Period(b) Using statistical software, develop a linear trend equation for this time series to forecast revenue (in $ billions). (Round your numerical values to four decimal places.)T₂ =3530-25201050 +6785 6 7 8 9 10 11 12Period0 1 2 3 4 5Ⓡ

Here we use excel Software enter data in excel click on insert click on scatter

Answered: Annual retail store revenue for a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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China GDP Year ($ trillions) 2005 2.3 2008 4.6 2011 7.5 2014 10.5 2017 12.0 2020 14.5 2023 17.5 (Source Bloomberg.com U.S. GDP ($ trillions) 13.1 + 14.7 15.5 2032 2035 17.4 19.4 20.6 21.8 China GDP U.S. GDP ($ trillions) 23.2 24.6 26.1 27.7 29.4 30.6 Year ($ trillions) 2026 21.2 2029 25.6 30.9 37.3 2038 45.1 2040 51.1 2. China's GDP vs. United States' GDP I. It is widely predicted that China's economy, mea- sured by Gross Domestic Product (GDP), will even- tually overtake that of the United States. Table 3.17 shows the GDPs for China and the United States from 2005 and projected to 2040 under the assump- tion that China's GDP will grow at an average annual rate of 6.5% and that the United States' GDP will grow at an average annual rate of 2%. Investigate when China's GDP will reach that of the United States under these assumptions about the countries" growth rates by using the following steps. 1. a. Create a scatter plot for the data, with x equal to the number of years after 2000 and…
Walmart Revenue The annual revenue of Walmart is given in the table below (source ). Walmart Annual Revenue (Billions of US dollars) 559.151 Year 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 523.964 514.405 500.343 485.873 482.130 485.651 476.294 468.651 446.509 421.849 408.085 404.254 377.023 348.368 312.101 284.310 Apply linear regression to the data in the table to find a model y = mx + b, where y is Walmart's annual revenue in billions of US dollars, and a is the number of years since 2000. Use the model y = mx + b with m rounded to the nearest tenth and 6 rounded to the nearest
The following table shows a company's annual revenue (in billions of dollars) for 2009 to 2014. Year Period (t) Revenue ($ billions) 2009 23.8 2010 2 29.3 2011 3 38.0 2012 4 50.2 2013 59.8 2014 66.8 (a) Construct a time series plot. 80 T 80- 80- 80 T 70 70 70 70 60 60 60- 60 50 50- 50 0- 50- 40 40- 40 40 30 30 30 30 20 20 20 20 10 10 10 10- 0- 0- 1 3 4 5 6. 7. 1 2 3 4 5 6 7 0 1 2 3 4 6. 7 0 1 3 4 7 Period Period Period Perjod What type of pattern exists in the data? The time series plot shows a downward curvilinear trend. O The time series plot shows a downward linear trend. O The time series plot shows an upward curvilinear trend. O The time series plot shows an upward linear trend. (b) Develop a linear trend equation for this time series to forecast revenue (in billions of dollars). (Round your numerical values to three decimal places.) T: = (c) What is the average revenue increase per year (in billions of dollars) that this company has been realizing? (Round your answer to three…
Consider the following quarterly time series. Quarter 1 2 3 4 Year 1 923 Year 2 1,112 1,056 1,156 992 Year 3 1,078 1,243 1,124 1,124 1,254 1,301 1,198 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year based on the model developed in part b.
Using the time series data in the table, respond to the following items. Period Sales 1 $ 1,001 2 1,129 3 841 4 1,044 5 1,014 6 703 7 682 8 712 9 646 10 686 11 909 12 469 13 566 14 488 15 688 16 675 17 303 18 381 d-1. Compute all possible forecasts using a trend forecasting model using simple linear regression? (Round your answers to 2 decimal places.) Period Sales Predicted Sales Absolute Error 1 1,001 1,026.85 25.85 2 1,129 990.60 138.40 3 841 954.35 113.35 4 1,044 918.10 125.90 5 1,014…
Score GPA Hours Absences Gender78 3.15 3.0 4 Female75 2.71 4.0 1 Female79 3.57 3.5 2 Male85 3.26 6.5 0 Female85 3.75 2.0 0 Female84 3.20 4.5 3 Female85 3.16 5.0 3 Male80 3.35 1.5 0 Female74 2.75 3.5 6 Male81 3.04 6.5 1 Male76 2.53 3.0 0 Female85 2.92 6.0 1 Male
End of month S&P 500 End of month S&P 500 End of month S&P 500 End of month S&P 500 September-77 96.53 April-80 106.29 November-82 138.53 July-85 191.85 October-77 92.34 May-80 111.24 December-82 140.64 August-85 190.92 November-77 94.83 June-80 114.24 January-83 145.30 September-85 188.63 December-77 95.10 July-80 121.67 February-83 148.06 October-85 182.08 January-78 89.25 August-80 122.38 March-83 152.96 November-85 189.82 February-78 87.04 September-80 125.46 April-83 164.43 December-85 202.17 March-78 89.21 October-80 127.47 May-83 162.39 January-86 211.28 April-78 96.83 November-80 140.52 June-83 167.64 February-86 226.92 May-78 97.24 December-80 135.76 July-83 162.56 March-86 238.90 June-78 95.53 January-81 129.55 August-83 164.40 April-86 235.52 July-78 100.68 February-81 131.27 September-83 166.07 May-86 247.35 August-78 103.29 March-81 136.00 October-83 163.55 June-86 250.84 September-78 102.54 April-81 132.81 November-83 166.40…
The following time-series diagram tracks the performance of two stocks during the month of October. Describe the overall trend of the two stocks. By what percentage did the value of each stock increase or decrease during the month of October? Stock 2 $200 Stock 1 T$150 Stock 2 -$100 Stock 1 T$ 50 1 5 11 16 21 26 31 October a. Stock 2 increased by 200%. Stock 1 decreased by 50%. b. Stock 2 increased by 100%. Stock 1 decreased by 50% c. Stock 2 increased by 200%. Stock 1 decreased by 33% d. Stock 2 increased by 100%. Stock 1 decreased by 33%.
Use R programming or excel to answer the following. Visualize your answers. Consider the following quarterly time series: Quarter Year 1 Year 2 Year 3 1 923 1112 1243 2 1056 1156 1301 3 1124 1124 1254 4 992 1078 1198 Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Compute the quarterly forecasts for next year based on the model developed in part b.

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