Exhibit 2Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2)Father's Day (late May - mid-June); and (3) all other times. Average weekly sales (in $'s) during each of these three seasonsduring the past four years has been as follows:Season Year 1 Year 2Year 3Year 4.18561995224122802012 1850143024081716 1072 11051560Estimate beta coefficients and the intercept of the following forecasting model with seasonality.Sales_t=b0+ b1'S1 + b2 S2123where S1 and S2 represent dummy variables for season 1 and season 2, respectively.How much of the variation in the sales is explained by the model above?Answers:a, 0.03b. 0.46c, 0.75d. 0.56

Answered: Image /qna-images/answer/f1f4a052-b8ad-4629-b031-a89fccbd6165.jpg

Answered: Exhibit 2 Business at Terry's Tie Shop…

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...

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The table to the right represents the annual percentage of smartphones sold in a certain region in 2011, 2012, and 2013 (projected). a. What conclusions can you reach about the market for smartphones in 2011, 2012, and 2013? b. What differences are there in the market for smartphones in 2011, 2012, and 2013? a. Select all that apply. A. Type 6 had the smallest or close to the smallest percentage of smartphones sold in all three years. B. Type 3 had the second largest percentage of smartphones sold in all three years. C. Type 1 had the largest percentage of smartphones sold in all three years. D. Type 5 had the smallest or close to the smallest percentage of smartphones sold in all three years. E. Type 2 had the second largest percentage of smartphones sold in all three years. Туре Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 2011 44% 21% 21% 10% 1% 3% 2012 69% 16% 4% 6% 3% 2% 2013 63% 17% 1% 3% 13% 3%
John Kittle, an independent insurance agent, uses a five-year moving average to forecast the number of claims made in a single year for one of the large insurance companies he sells for. He has just discovered that a clerk in his employ incorrectly entered the number of claims made four years ago as 1,400 when it should have been 1,200.a. What adjustment should Mr. Kittle make in next year’s forecast to take into account the corrected value of the number of claims four years ago?b. Suppose that Mr. Kittle used simple exponential smoothing with a = .2instead of moving averages to determine his forecast. What adjustment is now required in next year’s forecast? (Note that you do not need to know the value of the forecast for next year in order to solve this problem.)
Example 9. Represent the following data of the development expenditure of Central and Governments in India during 997 – 98, 1998 – 99, 1999 – 2000 Loans and advances bar diagram. - - Сapital Revenue Total Year 8,601 3,787 3,477 15,865 1997 – 98 10,335 4,456 4,036 18,827 1998 – 99 1999 – 2000 11,549 4,803 3,709 20,061 CS Scanned with CamScanner
The table below shows the one-year sales data for a certain part. Month Demand Month Demand January 687 July 662 February 599 August 747 March 524 September 748 April 521 October 577 May 685 November 519 June 667 December 742 Compute the one-step-ahead 3-month and 6-month moving-average forecasts for July through December. Compute the MAD for the forecasts obtained in part (a). What effect does increasing N from 3 to 6 have on the forecasts? Use the arithmetic average of the first six months of data as a baseline to initialize the exponential smoothing. Compute the one-step-ahead exponential smoothing forecasts for July through December, assuming α = 0.25. Compare the accuracy of the forecasts obtained in part (c) with the one-step ahead six-month moving-average forecasts obtained in part (a).
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The following table gives the annual revenue for two different companies (in billions of dollars) for a 10-year period. What is the five-number summary for Company A? Year 2007 2008 2009 2010 2011 99 57 2012 2013 2014 2015 2016 D Company A 96 100 102 106 100 96 98 85 82 Company B 53 62 70 72 77 70 90 92 83 ...... What is the five-number summary for company A? Q, =0 Q2 =0 Q3 =D Max = Min = %3D %3D (Type integers or decimals.)
Please help with the corrected answers in details: The following time series shows the sales of a particular product over the past 12 months. Month Sales 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110 What type of pattern exists in the data? The data appear to follow a trend pattern. The data appear to follow a horizontal pattern. The data appear to follow a cyclical pattern.The data appear to follow a seasonal pattern. (b) Use ? = 0.2 to compute the exponential smoothing values for the time series. (Round your answers to two decimal places.) Month t Time Series Value Yt Forecast Ft 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110 (c) Use a smoothing constant of ? = 0.4 to compute the exponential smoothing forecasts. (Round your answers to two decimal places.) Month t Time Series Value Yt Forecast Ft 1 105 2 135…
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Boran Stockbrokers, Inc., selects four stocks for the purpose of developing its own index of stock market behavior. Prices per share for a year 1 base period, January year 3, and March year 3 follow. Base-year quantities are set on the basis of historical volumes for the four stocks. Price per Share ($) Year 1 Stock Industry Quantity Year 1 March January Year 3 Base Year 3 A Oil 100 29.50 24.75 22.50 Computer 150 65.00 49.00 47.50 Steel 75 40.00 31.00 29.50 D Real Estate 50 18.00 6.50 3.75 Compute the price relatives for the four stocks making up the Boran index. (Round your answers to one decimal place.) Price Relative Stock January March A 2475.0 2250.0 B 7350.0 7125.0 2325.0 2212.5 D 325.0 187.5 Use the weighted average of price relatives to compute the January year 3 and March year 3 Boran indexes. (Round your answers to one decimal place.) IJan = 75.2 IMar = 70.9
20 Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times. Average weekly sales (in $'s) during each of these three seasons during the past four years has been as follows: Season Year 1 Year 2 Year 3 Year 4 1 1855 1932 2241 2280 2 2049 2168 2306 2408 3 992 5447 1105 1120 Determine a forecast for the average weekly sales in years 5 for season3. (Round to two decimal places)
1. Pretend that in a certain city the number of new cases of COVID was reported to be 200 new cases on Sunday. Let’s pretend that the number of new cases grows by 22% on Monday, and then after that falls by 22% on Tuesday Question C: What percent would the percent increase have to be on Monday to have a total change of zero% from Sunday to Tuesday? i.e. new covid cases go up by ???% on Monday and then down 22% on Tuesday for a total change of 0%. (Use decimals in answer if needed)
J 2 Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times. Average weekly sales (in $'s) during each of these three seasons during the past four years has been as follows: Season Year 1 Year 2 Year 3 Year 4 1 1829 1968 2241 2280 2 2047 2189 2306 2408 3 929 10129 1105 1120 Assuming that the sales is not experiencing any leaner increasing or decreasing trends. 1. Determine a forecast for the average weekly sales in year 5 for season1. (Round to two decimal places) Note: Please type a number and only a number in the text box.

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