The quarterly sales of a company (in millions of dollars) over the past three years are given in thefollowing table.Quarter 2007 2008 2009Quarter1428301Quarter2Quarter3Quarter4201636 401014183812b.a. Compute the four seasonal factors (Seasonal Indexes). Show all of your computations.The trend for these data is Trend = 20.82 +0.336 t (t represents time, where t-1 forQuarter 1 of 2007 and t-12 for Quarter 4 of 2009). Forecast sales for the first quarterof 2010 using the trend only. Show all of your computations.c. Forecast sales for the first quarter of 2010 using the trend and seasonal indexes andwrite your answer below. Show all of your computations.

200720082009Quarter 1142830Quarter 2201618Quarter 3364038Quarter 4101412Sum of yearly…

Answered: The quarterly sales of a company (in…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

See similar textbooks

Similar questions

For the following data of sales data. Use exponential smoothing to forecast sales in the next week. Use α = 0.1, and 0.4 and compare errors. Graph the original and forecasted data.
Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 300 275 February 400 375 March 425 350 April 450 425 May 400 400 June 460 350 July 400 350 August 300 275 September 375 350 October 500 November 550 December 500 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.5, the second most recent 0.25, and the third 0.25. Solve the problem using quarters, as opposed to forecasting separate months. Note: Round your answer to 2 decimal places.
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.
For the data in the following table, compute the additive quarterly seasonal forecast model for the number of ice creams sold by the store without trend. Round your values for the coefficients of the model to three decimal places. Answer Forecast= + Quarter 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 Number of Ice Creams 2868 4273 6820 3620 2401 4551 6981 3678 2685 4873 5972 3914 2032 4655 5964 Copy Data DQ₁ + ( DQ₂ + DQ3
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2000 2009 Seafood Qty. (Ib) Price ($/lb) Price ($/lb) Halibut 75,190 2.01 2.33 Lobster 83,080 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.) I2009 %D 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 hav v ---Select--- |by % over the 9-year period according to the index. increased decreased
Returns on stock X are listed below: Period Stock X 1 -5% 2 4% 3 3% 4 9% 5 1% 6 -3% 7 4% What is the mean of the data? Please specify your answer in decimal terms and round your answer to the nearest thousandth (e.g., enter 12.3 percent as 0.123).
Consider the following data: Monthly Profit of a Gym Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 6,090 Month Jan-12 Profit ($) 6,500 6.253 5,514 5,357 5,425 6,278 8,119 6,390 Step 3 of 5: Determine the exponentially smoothed forecast for the next time period using a smoothing constant, a, of 0.35. If necessary, round your answer to one decimal place.
Consider the following data: Month Jan-14 Feb-14 Profit ($) 16,113 16,263 Monthly Profit of an Auto Repair Shop Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 15,055 17,117 18,763 17,210 19,141 18,468 20,254 Step 3 of 4: Determine the exponential smoothing forecast for the next time period using a smoothing constant of 0.35. If necessary, round your answer to one decimal place.
The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 1 31 2 34 3 33 4 35 5 37 6 36 7 38 8 40 9 40 10 41 a. Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast ( F1�1 ) of 31. Note: Round your intermediate calculations and answers to 2 decimal places. b. Calculate the exponential smoothing with trend forecast for these data using an α of 0.30, a δ of 0.30, an initial trend forecast ( T1�1 ) of 1, and an initial exponentially smoothed forecast ( F1�1 ) of 30. Note: Round your intermediate calculations and answers to 2 decimal places. c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. Note: Round your intermediate calculations and answers to 2 decimal places. c-2. Which is best? multiple choice Exponential smoothing with trend forecast Single exponential smoothing forecast
The table below represents the monthly unemployment rates in the US from January of 2005 through May of 2016. Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2005 5.3% 5.4% 5.2% 5.2% 5.1% 5.0% 5.0% 4.9% 5.0% 5.0% 5.0% 4.9% 2006 4.7% 4.8% 4.7% 4.7% 4.6% 4.6% 4.7% 4.7% 4.5% 4.4% 4.5% 4.4% 2007 4.6% 4.5% 4.4% 4.5% 4.4% 4.6% 4.7% 4.6% 4.7% 4.7% 4.7% 5.0% 2008 5.0% 4.9% 5.1% 5.0% 5.4% 5.6% 5.8% 6.1% 6.1% 6.5% 6.8% 7.3% 2009 7.8% 8.3% 8.7% 9.0% 9.4% 9.5% 9.5% 9.6% 9.8% 10.0% 9.9% 9.9% 2010 9.7% 9.8% 9.9% 9.9% 9.6% 9.4% 9.5% 9.5% 9.5% 9.5% 9.8% 9.4% 2011 9.1% 9.0% 9.0% 9.1% 9.0% 9.1% 9.0% 9.0% 9.0% 8.8% 8.6% 8.5% 2012 8.2% 8.3% 8.2% 8.2% 8.2% 8.2% 8.2% 8.1% 7.8% 7.8% 7.8% 7.9% 2013 7.9% 7.7% 7.5% 7.5% 7.5% 7.5% 7.3% 7.2%…
please help me solve these
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2009 Seafood 2000 Qty. (lb) Price ($/lb) Price ($/lb) Halibut 75,190 2.01 2.43 Lobster 83,180 3.72 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 L (b) decimal place.) pute a weighted aggregate price index for the seafood catch. (Round your answer to 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 increased by % over the 9-year period according to the index.

SEE MORE QUESTIONS

Recommended textbooks for you

A First Course in Probability (10th Edition)

Probability

ISBN:

9780134753119

Author:

Sheldon Ross

Publisher:

PEARSON

A First Course in Probability

Probability

ISBN:

9780321794772

Author:

Sheldon Ross

Publisher:

PEARSON

A First Course in Probability (10th Edition)

Probability

ISBN:

9780134753119

Author:

Sheldon Ross

Publisher:

PEARSON

A First Course in Probability

Probability

ISBN:

9780321794772

Author:

Sheldon Ross

Publisher:

PEARSON

SEE MORE TEXTBOOKS