The Dominguez Hills Company has the following forecasts generated by two forecasting methods. Actual sales for the same time periods are shown below: YEAR SALES 850 FORECAST 1 FORECAST 2 833 1 843 2 869 876 3 858 870 921 910 827 902 795 846 4 5 6 7 8 850 845 905 845 890 815 835 842 864 805 821 Compute the tracking signal for each forecasting method. Do not round intermediate calculations. Round your answers to two decimal places. Use a minus sign to enter a negative value, if any. Tracking signal (method 1): Tracking signal (method 2): Why is tracking signal important in assessing the performance of a forecasting model? If the tracking signal falls -Select-preset control limits, there is a bias problem with the forecasting method. Assuming that the control limit for the tracking signal is ±4, the first forecasting method -Select-within the limits and the second forecasting method -Select- within the limits.

The Dominguez Hills Company has the following forecasts generated by two forecasting methods. Actual sales for the same time periods are shown below: YEAR SALES 850 FORECAST 1 FORECAST 2 833 1 843 2 869 876 3 858 870 921 910 827 902 795 846 4 5 6 7 8 850 845 905 845 890 815 835 842 864 805 821 Compute the tracking signal for each forecasting method. Do not round intermediate calculations. Round your answers to two decimal places. Use a minus sign to enter a negative value, if any. Tracking signal (method 1): Tracking signal (method 2): Why is tracking signal important in assessing the performance of a forecasting model? If the tracking signal falls -Select-preset control limits, there is a bias problem with the forecasting method. Assuming that the control limit for the tracking signal is ±4, the first forecasting method -Select-within the limits and the second forecasting method -Select- within the limits.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.3: Modeling Data With Power Functions

Problem 6E: Urban Travel Times Population of cities and driving times are related, as shown in the accompanying...

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Urban Travel Times Population of cities and driving times are related, as shown in the accompanying table, which shows the 1960 population N, in thousands, for several cities, together with the average time T, in minutes, sent by residents driving to work. City Population N Driving time T Los Angeles 6489 16.8 Pittsburgh 1804 12.6 Washington 1808 14.3 Hutchinson 38 6.1 Nashville 347 10.8 Tallahassee 48 7.3 An analysis of these data, along with data from 17 other cities in the United States and Canada, led to a power model of average driving time as a function of population. a Construct a power model of driving time in minutes as a function of population measured in thousands b Is average driving time in Pittsburgh more or less than would be expected from its population? c If you wish to move to a smaller city to reduce your average driving time to work by 25, how much smaller should the city be?
A certain manufacturing company has the following data on quantities shipped and unit costs for each of its four products. Products Base-PeriodQuantities (Year 1) Mean Shipping Costper Unit ($) Year 1 Year 5 A 2,500 10.50 15.90 B 5,000 16.25 31.00 C 6,000 12.20 17.40 D 2,500 20.00 35.50 Use the price data to compute a Paasche index for the shipping cost if year 5 quantities are 4,000, 3,000, 7,500, and 3,000 for products A, B, C, and D, respectively. (Round your answer to the nearest integer.)
The following data show Transportation Budget for North Carolina (Let year 1- 2003)Year = 2003 Budget (in millions) = 385Year = 2004 Budget (in millions) = 397Year = 2005 Budget (in millions) = 452Year = 2006 Budget (in millions) = 501Year = 2007 Budget (in millions) = 446Year = 2008 Budget (in millions) = 450Year = 2009 Budget (in millions) = 593Year = 2010 Budget (in millions) = 654Year = 2011 Budget (in millions) = 846Year = 2012 Budget (in millions) = 911Year = 2013 Budget (in millions) = 1483Year = 2014 Budget (in millions) = 1897a) Create a scatter plot and be sure to LABEL BOTH axis and give the chart an overall name. Have the graph display the “r-value” and the correct equation.b) What kind of correlation is this?c) Is this more of a linear or exponential model? Why?d) Predict the budget for 2020
IOG Technologies offers semiconductor and system solutions for automotive industrial and multimarket sectors for applications in communication. The following data relates to the production of three products manufactured by them: Product Price (RM) Quantity 2000 2016 2000 2016 BD-1005 3.00 3.20 60 80 LCM-2134 2.00 2.50 100 90 DDK-456 3.50 4.00 500 700 Determine the Laspeyres Price Index (LPI) and Paasche Price Index (PPI) for 2016 using 2000 as the base period.
The following data show Transportation Budget for North Carolina (Let year 1- 2003)Year = 2003 Budget (in millions) = 385Year = 2004 Budget (in millions) = 397Year = 2005 Budget (in millions) = 452Year = 2006 Budget (in millions) = 501Year = 2007 Budget (in millions) = 446Year = 2008 Budget (in millions) = 450Year = 2009 Budget (in millions) = 593Year = 2010 Budget (in millions) = 654Year = 2011 Budget (in millions) = 846Year = 2012 Budget (in millions) = 911Year = 2013 Budget (in millions) = 1483Year = 2014 Budget (in millions) = 1897 Predict the budget for 2020
Nenana, Alaska Ice Breakup Data 1917 - 2021 (Jan. 1 = day 1) (Alaska Standard Time) Year Year (since 1900) Day Number Date & Time 1917 17 119.4795 April 30 at 11:30 AM 1918 18 130.3983 May 11 at 9:33 AM 1919 19 122.6066 May 3 at 2:33 PM 1920 20 131.449 May 11 at 10:46 AM 1921 21 130.2795 May 11 at 6:42 AM 1922 22 131.5559 May 12 at 1:20 PM 1923 23 128.0837 May 9 at 2:00 AM 1924 24 131.6323 May 11 at 3:10 PM 1925 25 126.7726 May 7 at 6:32 PM 1926 26 115.6691 April 26 at 4:03 PM 1927 27 132.2378 May 13 at 5:42 AM 1928 28 126.6844 May 6 at 4:25 PM 1929 29 124.6538 May 5 at 3:41 PM 1930 30 127.7941 May 8 at 7:03 PM 1931 31 129.3913 May 10 at 9:23 AM 1932 32 121.4274 May 1 at 10:15 AM 1933 33 127.8128 May 8 at 7:30 PM 1934 34 119.5885 April 30 at 2:07 PM 1935 35 134.5642 May 15 at 1:32 PM 1936 36 120.5406 April 30 at 12:58 PM 1937 37 131.8365 May 12 at 8:04 PM 1938 38 125.8434 May 6 at 8:14 PM 1939 39 118.5601 April 29 at 1:26 PM…
Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 10 months. The forecasts and actual sales are as follows: Month Sales Forecast 1 Forecast 2 1 835 790 770 2 845 825 805 3 815 785 830 4 830 825 815 5 770 780 795 6 830 810 796 7 815 810 805 8 855 775 840 9 795 835 795 10 840 820 840 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.)
The data contained in the file FoodIndustry show the company/chain name, the average sales per store ($1,000s), and the food segment industry for 47 restaurant chains.† Company Segment 2012 U.S.AverageSales perStore($1,000s) Arby's Sandwich 993 Baskin-Robbins Snacks 207 Bojangles' Chicken 1,718 Boston Market Chicken 1,184 Burger King Burger 1,195 Carl's Jr. Burger 1,470 Checkers/Rally's Burger 903 Chick-fil-A Chicken 3,158 Chipotle MexicanGrill Mexican 2,113 Church's Chicken Chicken 707 CiCi's Pizza Pizza/Pasta 915 Culver's Burger 1,838 Dairy Queen Burger 545 Del Taco Mexican 1,100 Domino's Pizza Pizza/Pasta 710 Dunkin' Donuts Snacks 857 El Pollo Loco Chicken 1,475 Five GuysBurgers & Fries Burger 1,049 Hardee's Burger 1,145 In-N-Out Burger Burger 1,935 Jack in the Box Burger 1,379 Jamba Juice Snacks 715 Jason's Deli Sandwich 2,556 Jimmy John's Sandwich 879 Company Segment 2012 U.S.AverageSales perStore($1,000s) KFC…
The data contained in the file FoodIndustry show the company/chain name, the average sales per store ($1,000s), and the food segment industry for 47 restaurant chains.† Company Segment 2012 U.S.AverageSales perStore($1,000s) Arby's Sandwich 993 Baskin-Robbins Snacks 207 Bojangles' Chicken 1,718 Boston Market Chicken 1,184 Burger King Burger 1,195 Carl's Jr. Burger 1,470 Checkers/Rally's Burger 903 Chick-fil-A Chicken 3,158 Chipotle MexicanGrill Mexican 2,113 Church's Chicken Chicken 707 CiCi's Pizza Pizza/Pasta 915 Culver's Burger 1,838 Dairy Queen Burger 545 Del Taco Mexican 1,100 Domino's Pizza Pizza/Pasta 710 Dunkin' Donuts Snacks 857 El Pollo Loco Chicken 1,475 Five GuysBurgers & Fries Burger 1,049 Hardee's Burger 1,145 In-N-Out Burger Burger 1,935 Jack in the Box Burger 1,379 Jamba Juice Snacks 715 Jason's Deli Sandwich 2,556 Jimmy John's Sandwich 879 Company Segment 2012 U.S.AverageSales perStore($1,000s) KFC…
U.S. Manufactured General Aviation Shipments, 2009–2015 Year Qtr 1 Qtr 2 Qtr 3 Qtr 4 Total 2009 332 407 380 470 1,589 2010 240 368 281 426 1,315 2011 263 295 270 465 1,293 2012 305 365 343 518 1,531 2013 332 406 352 507 1,597 2014 343 366 368 521 1,598 2015 285 354 363 536 1,538 Use MegaStat or Minitab to calculate estimated seasonal indexes and trend. (Round your answers to 3 decimal places.) Qtr1Index Qtr2Index Qtr3Index Qtr4Index
The owner of a restaurant in Bloomington, Indianna, has recorded sales data for the past 10 years. He has also recorded data on potentially relevant variables. The following table gives data on sales and other potentially relevant variables for that particular restaurant. Sales (thousands of dollars) Year Population Advertising (thousands of dollars) Previous advertising (thousands of dollars) $15713 1 102558 $20 $30 $12937 2 101792 $15 $20 $12872 3 104347 $25 $15 $16227 4 106180 $30 $25 $15388 5 106562 $15 $30 $13180 6 105209 $25 $15 $17199 7 109185 $35 $25 $20674 8 109976 $40 $35 $20350 9 110659 $20 $40 $14444 10 111844 $25 $20 Consider the model: salest = 0 + 1t + 2popt + 3advt + 4advt-1 + t where salest = sales in year t, popt = size of the population residing within 10 kilometres of the restaurant, advt =…
NORTH–SOUTH AIRLINE CASENorthern Airline DataAirframe CostEngine CostAverage AgeYearper Aircraftper Aircraft(Hours)200151.8043.496,512200254.9238.588,404200369.7051.4811,077200468.9058.7211,717200563.7245.4713,2752006 84.7350.2615,215200778.7479.6018,390Southeast Airline DataAirframe CostEngine CostAverage AgeYearPer Aircraftper Aircraft(Hours)200113.2918.865,107200225.1531.558,145200332.1840.437,360200431.7822.105,773200525.3419.697,150200632.7832.589,364200735.5638.078,2591. Develop Regression equations for each cost category as dependent variables and average age as independent variables. Do this for each Airline. Look at graphs produced by QM2. From the regression results, what would you conclude about a) airframe vs engine costs for each airlineb) comparing airlines as to airframe costsc) comparing airlines as to engine costsd) Provide a brief summary report of your comparisons