This table shows Number of currant Household and currant trips Zone A Zone B Zone C No. of No. of No. of person/Household = person/Household = person/Household = Auto/ Household 1 2-3 >3 No. of No. of No. of No. of No. of No. of Н.Н trips Н.Н trips Н.Н trips 1100 220 1500 600 1800 1080 1 950 380 1200 600 120 90 >1 80 100 200 300 50 80 This table shows Number of future Household Zone A Zone B Zone CTH No. of No. of No. of Auto/ Household person/Household = person/Household = person/Household 1 2-3 >3 3000 5000 6000 1 1500 4000 800 >1 300 700 200

This table shows Number of currant Household and currant trips Zone A Zone B Zone C No. of No. of No. of person/Household = person/Household = person/Household = Auto/ Household 1 2-3 >3 No. of No. of No. of No. of No. of No. of Н.Н trips Н.Н trips Н.Н trips 1100 220 1500 600 1800 1080 1 950 380 1200 600 120 90 >1 80 100 200 300 50 80 This table shows Number of future Household Zone A Zone B Zone CTH No. of No. of No. of Auto/ Household person/Household = person/Household = person/Household 1 2-3 >3 3000 5000 6000 1 1500 4000 800 >1 300 700 200

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

See similar textbooks

Similar questions

3
Describe the Analysis of Variables?
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:
Sales of Volkswagen’s popular Beetle have grown steadilyat auto dealerships in Nevada during the past 5 years (seetable below). The sales manager had predicted before thenew model was introduced that first year sales would be410 VWs. Using exponential smoothing with a weight ofa 5 .30, develop forecasts for years 2 through 6.
7
Use quantitative methods for the data shown below: Period 1 2 3 4 5 6 7 Observation 24 34 36 37 41 44 45 i) Naïve method: ii) Three period moving average iii) Three period Weighted moving average iiii)Four-period Moving Average iiiii)Four-period Weighted Moving Average b. Plot the original time series and comment on the appropriateness of a linear trend.
Develop a 3-week moving average for the following time series Time Time Series Period Value |1 13 2 12 3 17 |4 15 6 15 15 Find the value for MSE. If needed round your intermediate calculations to no more than 2 decimal places. Round your final answer to 2 decimal places
Pls answer b and c
From the following time series data of sale project the sales for the next three years. Year 2001 2002 2003 2004 2005 2006 2007 Sales (*000 units) 80 90 92 83 94 99 92
Consider the following from an estate agent business: Month Houses Sold Jan 380 Feb 360 Mar 390 Apr 400 May 390 June 380 July 394 Aug 454 Sep 460 Oct 460 Nov 430 Dec 370 a) Calculate the three point moving averages (quarters) and seasonal variation for the above data b) A detective figures he has a one in nine chance of recovering stolen property. His out-of-pocket expenses for the investigation are $9,000. If he is paid his fee only if he recovers the stolen property, what should he charge clients in order to breakeven? c) At the races, your horse, has a probability of 1/20 of coming 151, 1/10 of coming 2nd and a probability of ¼ in coming 3rd. First place pays $4,500 to the winner, second place $3,500 and third place $1,500. Hence, is it worth entering the race if it costs $1,000? d) Your company plans to invest in a particular…
38
Student Performance Results Pre- Post- Pre- Post- Student ID LG1 (%) LG1 (%) LG2 (%) LG2 (%) Pre- Assessment Assessment Assessment Assessment Assessment Assessment LG3 (%) 1 70 85 75 80 90 80 098 Post- LG3 (%) 85 2 75 90 90 80 85 75 90 96 3 80 85 75 1 90 70 85 4 75 90 70 85 75 00 90 5 70 85 75 90 90 6 75 50 00 80 95 85 90 80 85 75 90 7 80 85 75 90 90 70 85 8 75 90 70 85 75 90 9 70 85 75 90 80 85 10 ° 75 55 90 80 85 75 90 11 80 85 75 90 00 12 75 90 20 70 70 85 85 75 90 13 70 85 75 90 80 85 55 14 75 90 80 85 75 90 15 80 85 75 90 70 85 16 75 90 70 85 75 90 17 70 85 75 90 80 85