(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 = 2.8450+ - 0.0124X(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.)$34.1billion Annual retail store revenue for a technology company from 2007 to 2017 are shown below.Year Period2007200820092010201120122013201420152016201735302520151015+0234567891011(a) Construct a time series plot.Retail Store Revenue($ billions)3.915+01 2 3 4 56.1106.7779.28014.32719.02820.42821.66228.30926.99830.703AM6Period7 8 9 10 11 12What 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.The time series plot shows an upward trend.3530252015105-0-0123 4 56Period7 8 9 10 11 1235302520151050-01 23 45 6 7 8 9 10 11 12PeriodO353025201510500VM+1 2 3 4 5 6 7 8 9 10 11 12PeriodⓇ

we use excel softwear enter data in excel click on insert clcik on scatter plot

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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Exercise 1 Over the years, the Malaysia grocery distribution and sales industry has grown. Today, the total industry sales are in the tens millions of dollars. Major retailer such as DIY and Eco, added food to complement their merchandise sales. For many companies, forecast of industry demand are necessary to plan for short and long term decision. Using time series data below, forecast of monthly industry demand. Month (2020) Month (2021) January February March April May Demand Demand June 138 103 July August September October 131 142 149 122 110 121 175 130 November 194 June 126 December 166 July 140 a) Graph the time series data. What do you observe? b) Compute all possible forecast using exponential smoothing with a smoothing constant of 0.4. c) Compute all possible forecast using exponential smoothing with a smoothing constant of 0.9. d) Compute the MADS for each forecast model. e) What forecast model would you choose? Why? ENG 99 hp
b and c part
Forecasting Methods: Perform Trend Series Analysis (sales projection for the year 10, 12 and 17) Make based on the sales data below:Year 1: 24,333; Year 2: 33,524; Year 3: 26,778; Year 4: 30,567; Year 5: 27,564; Year 6: 24,555; Year 7: 21,777 and Year 8: 25,669.
Please answer ASAP. The first question contains data while the second picture contains the question.
Dokument1 - Word Suchen Mohammec twurf Layout Referenzen Sendungen Überprüfen Ansicht Hilfe Aav A AaBbCcDc AaBbCcDc AaBbC AABBCCD 1 Standard 1 Kein Lee.. Überschrif... Überschrif... Absatz Formatvorlagen The following data for economic growth and interest rate per year are given: Economic growth p.a. Interest rate p.a. Arithmetic mean 2.4% 1.9% Standard deviation 3,0% 2,0% -10, 0% 4.5% Minimum |-1,0% Maximum 8.0% The correlation coefficient according to Bravais-Pearson between economic growth p.a. and interest rate p.a. is -0.81. What economic growth p.a. do you expect on the basis of a linear regression if the interest rate is -0.30% p.a.? Is the value reliable? Give reasons for your answer. I=
The final grade in a particular course is determined by grades on the midterm and final. The grades for five students and the two grading systems are modeled by the following matrices. Call the first matrix A and the second B. Complete parts (a) and (b). Click to view the matrices. a. Describe the grading system that is represented by matrix B. Grading system 1 assigns 50% of the final grade to the midterm and 50% of the final grade to the final. Grading system 2 assigns 20% of the final grade to the midterm and 80% of the final grade to the final. b. Compute the matrix AB and assign each of the five students a final course grade first using system 1 and then using system 2. (89.5-100=A, 79.5-89.4 = B, 69.5-79.4 = C, 59.5-69.4D, below 59.5 = F) Grade System 1 System 2 ... Student 1 Student 2 Student 3 Student 4 Student 5 (Type A, B, C, D, or F.)
Coca-Cola Revenues ($ millions), 2014 to 2019 Quarter 2014 2015 2016 2017 2018 2019 Quarter 1 10.58 9.94 10.28 9.12 7.63 8.02 Quarter 2 12.57 11.63 11.54 9.70 9.42 10.00 Quarter 3 11.98 11.77 10.63 9.08 8.78 9.51 Quarter4 10.87 10.13 9.41 7.51 5.36 9.07 What is the trend model for the deseasonalized time series? (Round your answers to 2 decimal places.) State the model found when performing a regression using seasonal binaries. (A negative value should be indicated by a minus sign. Round your answers to 4 decimal places.) Use the regression equation to make a prediction for each quarter in 2020. (Enter your answers in millions rounded to 3 decimal places.)
In the theory of time series, earthquakes can be considered as a. Seasonal Variation b. Cyclical Variation c. Irregular Variations d. Secular Trend
Based on the following graph, what is the year when the largest decrease in GDP took place ? GDP (change YoY) 6% 5% 4% 4% 3% 3% 3% 3% 2% 2% 1% -1% -1% -8% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010o 2011 2012 2013 2009 2001 2005 2007
Can I please get some help. Thank you!
Describe this chart from a technical analysis perspective. If you were an investor what would you read from this chart?
Please round answers according to the question

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