Past sales for months 1 to 5 of a large equipment are shown below.Month1 2 3 4 5Actual Demands 12 17 20 19 24Forecast month 6 sale by fitting a least-square straight-line trend.

From the given information we forecast for 6 using least squared equation

Answered: Past sales for months 1 to 5 of a large…

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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Marie Bain is the production manager at a company that manufactures hot water heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The company's sales history (in thousands of units) is shown in the table below: Period Demand 1 12 2 15 3 16 4 16 5 18 6 20 a. Use 2 year moving average to forecast the demand for periods 5 and 6. b. Use three-year weighted moving average with weight 3, 2, 1 (largest weight is for the most recent data) to forecast the demand for periods 5 and 6. c. Use exponential smoothing with smoothing constant of 0.2 to forecast the demand for periods 5 and 6, assuming the forecast for period 4 is 15. d. Usetrend-adjustedexponentialsmoothingtoforecastthedemandforperiods5and6, assuming α=0.2, β=0.4, S3=15, and T3=1. e. What is the most accurate method?
Estimate the lowest price at which the demand is zero. $ . Round to the nearest dollar. Refer to screenshot attached below thanks!
1) Your company manufactures, among other things, pencil cases. Noting that sales have fallen in recent years, you decide to see if it is possible to straighten the market trend. You raise the sales volume as well as the price over the past few years. The results are given opposite, the comparison being made in constant dollars. Year Price ($) Annual sales in thousands year price annual sales 1982 1,50 35 1983 1,75 31 1984 1,95 28 1985 2,30 24 1986 3,00 10 a) Graph the data. b) Using these data, construct a model describing the relationship between price and sales volume. c) The company wishes to dispose of what it can produce without asking workers to work overtime without stopping its other productions, i.e. 20,000 cases. What should be the price for sell off production? d) Calculate the correlation coefficient. What does this coefficient tell you?
Refer to the graph below. If a price floor of $125 was imposed in this market, which of the following is most likely to happen? Price (dollars per printer) $125 $100 $75 Supply Demand 17.5 18.5 19.5 20.5 21.5 Quantity (millions of printers per month) OA. A quota of 21.5 million printers would be instituted, to prevent overproduction. OB. The government could set a quota of 21.5 million printers, to prevent underproduction. OC. A quota of 19.5 million printers would be instituted, to prevent overproduction. OD.A quota of 18.5 million printers would be instituted, to prevent overproduction.
B3 Calculate (predict) t = 11 for each trend method. Create a Linear prediction for Federal Debt-GDP = Create an Exponential prediction for Federal Debt-GDP = Create a Polynomial (order 5) prediction for Federal Debt-GDP = Year t Federal Debt - GDP 2011 1 96.018 2012 2 100.061 2013 3 100.810 2014 4 102.818 2015 5 100.246 2016 6 105.337 2017 7 104.806 2018 8 105.632 2019 9 107.047 2020 10 128.332
Consider the 12 weeks of demand in the following table. Calculate the CMA for Weeks 2 through 11. What is the CMA for Week 4? Round your answer to the nearest whole number. Week Demand 1 23 2 19 3 21 4 22 5 19 6 22 7 19 8 20 9 22 10 22 11 19 12 21
TABLE 8.1 2016 Marginal Tax Rates, Standard Deductions, and Exemptions Unmaried, diverced, or legally separated Married and ach parteer files eparate tar rete Married and berh partren file single tar retee Unmarried and paying mare than half the cest of sepperting ekildr pareet Married Filing Separately Married Filing Jointly Head of Household Тах Rate Single 10% up to $9275 up to $9275 up to $18,550 up to $13.250 15% $9276 to $37,650 $9276 to $37,650 $18,551 to $75,300 $13,251 to $50,400 25% $37,651 to $91,150 $37,651 to $75,950 $75,301 to $151,900 $50,401 to $130,150 28% $91,151 to $190,150 S75,951 to $115,725 $151,901 to $231,450 $130,151 to $210,800 33% $190,151 to $413,350 SI15,726 to $206,675 $231,451 to $413,350 $210,801 to $413,350 35% $413,351 to $415,050 $206,676 to $233,475 $413,351 to $466,950 $413,351 to $441,000 39.6% mure than $441,000 more than S415.050 more than $233.475 mure than $466,950 Standard Deduction $6300 $600 $12,600 $9300 Exemptions (per person) $4050 S4050…
3- Given the following demand. What is the forecast for period 6 and 18. Period demand base trend forecast (a = .70 and B=.60) 70 80 70 90 110 80 10 4 123 tS6
Consider the following. March April May Month Demand. January 29 February March June April July May June July What is the forecast for July based on a three-month weighted moving average applied to the above past demand data and using the weights: data)? Show all the forecasts for April through June along with the answer Month Demand Forecast January 29 February 46 58 59 46 74 58 59 = 74 88 53 10 10 1:35:14 and (largest weight is for most recent 10
Given the number of calls per week received by a dental clinic, what is the forecasted number of calls for week 12 considering that the forecast for week 1 was 47 and a smoothing constant a -0.2? CALLS 52 35 25 36 45 35 44 WEEK 1 2 3 4 5 6 7 869 10 34 calls 35 calls 33 calls 38 calls 30 35 25
The following table reports prices and usage quantities for two items in 2009 and 2011. (Round your answers to the nearest integer.) Quantity Unit Price ($) Item 2009 2011 2009 2011 A 1,400 1,700 8.50 6.75 B 4 2 630.00 1,400.00 (a) Compute price relatives for each item in 2011 using 2009 as the base period. Item Price Relative A B (b) Compute an unweighted aggregate price index for the two items in 2011 using 2009 as the base period. I2011 = (c) Compute a weighted aggregate price index for the two items using the Laspeyres method using 2009 as the base period. I2011 = (d) Compute a weighted aggregate price index for the two items using the Paasche method using 2009 as the base period. I2011 =
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Past sales for months 1 to 5 of a large equipment are shown below. Month 1 2 3 4 5 Actual Demands 12 17 2019 24 Forecast month 6 sale by fitting a least-square straight-line trend.