The table below provides the sales data for JD Inc. for the year 1998. Given: a = 0.2, ß = 0.15, and y = 0.05(Hint: Use an additive Holt-Winters model.)Umbrella Sales of JD Inc.Year Month Units1111111111112222222222JanFebMarAprilMayJunJulyAugSepOctNovDecJan398045204480469042104418438044005140April 5120May5320JunJulyAugSepOctNovDecFebMar40004740472049205140464055405040492048805090224818What is the value of the mean absolute deviation for the given data?A) 7.62B) 340.73C) 14.82D) 8.104610

Given data: Sl. No. Month Values 1 Jan 3980 2 Feb 4000 3 Mar 4740 4 Apr 4720 5 May 4920…

Answered: The table below provides the sales data…

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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Similar questions

Suppose you needed $16,000 to maintain a particular standard of living in 1979. How much would you have needed in 1997 to maintain the same standard Average Annual Consumer Price Index (CPI) (1982-1984=100) CPI of living? Assume that all prices have risen at the same rate as the CPI. Year Year CPI Year CPI 103.9 152.4 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1973 44.4 1995 How much would you have needed? 107.6 1996 1997 1974 49.3 1975 1976 1977 156.9 160.5 163.0 53.8 109.6 56.9 113.6 1998 (Round to the nearest dollar.) 60.6 118.3 1999 166.6 1978 1979 1980 1981 1982 1983 172.2 177.1 2000 2001 2002 2003 2004 2005 65.2 124.0 72.6 130.7 82.4 90.9 96.5 99.6 136.2 140.3 144.5 148.2 179.9 184.0 188.9 195.3 On-Screen Keyboard Esc %23 1 2. * 3 4 8 Hom Tab q u i Del End e y
The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER SALES 1 220 2 232 3 201 4 193 5 163 6 194 7 155 8 136 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to the nearest whole number.
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2000 Price ($/lb) 2009 Seafood Qty. (Ib) Price ($/lb) Halibut 75,090 2.01 2.33 Lobster 83,180 3.62 3.09 Tuna 50,779 1.87 1.97 (a) Compute a price relative for each type of seafood. (Round your answers to one decimal place.) Seafood Price Relative Halibut Lobster Tuna (b) Compute a weighted aggregate price index for the seafood catch. (Round your answer to one decimal place.) I 2009 - Comment on the change in seafood prices over the nine-year period. (Enter your percentage as a positive value. Round your answer to one decimal place.) Seafood prices have --Select--- v by % over the 9-year period according to the index.
You collect data of average annual returns for Amazon Stock and Microsoft Stock and make the following table: (This data can be copied into Excel.) Year Amazon Return Microsoft Return 2013 0.5896 0.1221 2014 -0.2218 0.2416 2015 1.1778 0.1944 2016 0.1095 0.1200 2017 0.5596 0.3766 2018 0.2843 0.1874 2019 0.3216 0.5526 You use this data to answer the research question: are true population Amazon Returns greater than true population Microsoft Returns?
Evaluate the following
2015 2020 ITEM PRICE QUANTITY PRICE QUANTITY Cup of coffee 8.50 50 10.50 60 Toasted cheese sandwich 13.00 35 14.00 25 English muffin 9.00 120 9.50 138 The preliminary analysis of the data produced the following table: po qo p1 q1 poqo poq1 p1qo p1q1 8.50 50 10.50 60 425 510 525 630 13.00 35 14.00 25 455 325 490 350 9.00 120 9.50 138 1080 X 1140 1311 Σ W Y 2155 Using the table of analysis shown above, answer the following questions (use 2015 as the base year):
Month Sales Forecast January 1000 1043 February 3000 March 5000 April 4000 May 5000 June 6000 Compute the forecast for the month of July using simple exponential smoothing. Take alpha = 0.2
please help me solve these
Given the following, determine yesterday’s residual for sales if 18 people were working and sales were $24,000.
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the exponential smoothing method to forecast the number of sales to expect next month. Use the following value of alpha: 0.2. Month Demand Forecast 1 308 349.2 2 388 340.9 3 344 350.3 4 400 349.1 5 341 359.3
Please answer only 4.39

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