The article "A Robust Optimization Approach for the Capacitated Vehicle Routing Problemwith Demand Uncertainty" (I. Sungur, F. Ordonez, and M. Dessouky, IIE Transactions,2008:509-523) discusses methods to reduce transportation costs while satisfying demands.In one study, the percent demand that is unmet (y) and the percent of vehicle capacity (x)needed to meet the expected demand were recorded for 27 different scenarios. The resultsare presented in the following table.хУ0.5820.00.71.30.8110.91.21.30.20.81.40.91.40.41.79311930.8900.30.21.01.0891.0931.3911.3941.2940.7Compute the least-squares line for predicting unmet demand (y) from vehicle capacity(X).b. Compute 95% confidence intervals for B, and B,-Predict the unmet demand when the vehicle capacity is 93%.d.a.c.Find a 95% confidence interval for the mean unmet demand when the vehide capacityis 93%.Find a 95% prediction interval for the unmet demand for a scenario whose vehiclecapacity is 93%.e.

Given that, The values of demand and vehicle capacity

Answered: The article "A Robust Optimization…

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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An article in Technometrics by S. C. Narula and J. F. Wellington (Prediction. Linear Regression and a Minimum Sum of Relative Errors." Vol. 19. 1977) presents data on the selling price (y) and annual taxes (x) for 24 houses. The taxes include local, school and county taxes. The data are shown in the table below. Calculate the least square estimates of slope and intercept. Sale Price/1000 Taxes/1000 ... 25.9 4.9176 29.5 5.0208 27.9 4.5429 4.5573 5.0597 3.8910 25.9 29.9 29.9 30.9 28.9 35.9 5.0980 5.6039 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 30.0 5.0500 36.9 8.2464 41.9 6.6909 40.5 7.7841 43.9 9.0384 37.5 5.9894 37.9 7.5422 44.5 8.7951 37.9 6.0831 34.9 8.3607 8.1400 26.9 45.8 9.1416 Input answers up to 3 decimal places. Slope 3.324 Intercept 13.320 Blank 1 3.324 Blank 2 13.320
1. Consider the following regression model: Fram Risk Score; = Bo + B1 × Health Insurance; + u¿ The Framingham Risk Score predicts 10-year risk of cardiovascular disease based on age, cholesterol levels, blood pressure, blood sugar, use of medication for high blood pressure, and smoking. A higher score means worse overall cardiovascular health. A researcher who collects data and regresses the Fram Risk Score against Health Insurance (= 1 if have insurance) finds that B, 2. Consider the following regression model: Class Average; = Bo + B1 x Office Hours; + u; Class Average is the students' average grade in the class at the end of the term and Office Hours is the number of office hours held by the instructor over the entire term. A researcher who collects data and regresses Class Average against Office Hours finds that, surprisingly, B V A V A
: 39% in 30m 20 CALENDAR Travel to Home Today at 11:02 PM Green Street f... Lumen... Sign In or School.. ohm Rukayat Balogun오 | User Settings My Classes Log Out ork manager ges Forums Calendar Gradebook 1:30am Fall 2018> Assessment pplications of Systems of Equations Due in 11 hours, 28 minutes. Due Fri 12/21/2018 10:00 am Monico needs to mix a 10% alcohol solution with a 50% alcohol solution to create 100 milli!eters of a 26% solution. How many millileters of each solution must Monico use? Answer: Monico must mix -milli!eters of 10% solution and milli!eters of 50% solution. Points possible: 1 Unlimited attempts License Submit on
(b) Let xij be the amount shipped from source i to destination j in a 5X5 transportation problem, and let Cij be the corresponding transportation cost per unit. The amounts of supply at sources 1,2,3,4 and 5 are 210, 150,250,190 and 150 units respectively and the demands at destinations 1,2,3,4 and 5 are 205, 220,180,180 and 165 units respectively. The cost of shipping from source 1 to destinations 1,2,3,4 and 5 are 29, 28, 31, 27 and 32 respectively. The cost of shipping from source 2 to destinations 1,2,3,4 and 5 are 26, 27, 30, 37 and 31 respectively. The cost of shipping from source 3 to destinations 1,2,3,4 and 5 are 32, 38, 29, 27 and 35 respectively. The cost of shipping from source 4 to destinations 1,2,3,4 and 5 are 33, 39, 27, 29 and 33 respectively. The cost of shipping from source 5 to destinations 1,2,3,4 and 5 are 35,31, 34, 23 and 35 respectively. (i)Find the associated optimal cost. (ii)Determine each non basic variable.
3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В)
4
Table 1. The manager of Carpet City outlet store needs to be able to forecast accurately demand for Soft Shang carpet. Demand for the past 20 weeks appears in table 1 below. week Demand Forecast Error ABS. Dev Squared Error APE 1 20 2 28 3 22 4 18 5 22 6 22 7 22 8 26 9 23 10 23 11 23 12 27 13 25 14 22 15 23 16 14 17 14 18 15 19 11 20 16 21 22 23 24…
Consider the delivery time data discussed in Example 11.3 .a. Develop a regression model using the prediction data set. b. How do the estimates of the parameters in this model compare with those from the model developed from the estimation data? What does this imply about model validity? c. Use the model developed in part a to predict the delivery times for the observations in the original estimation data. Are your results consistent with those obtained in Example 11.3 ?
5
Square Feet Sum of Bedrooms and Bathrooms Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 1,610 5 70 227,900 800,000 2,146 6 59 284,900 700,000 816 4 70 149,900 FO000 600,000 2,183 6.5 48 309,900 40000 1,046 5.5 64 134,900 500.000 20000 5,183 10.5 21 440,000 400,000 • 2 000 1,150 4 62 150,000 1,000 4,000 5.000 6,000 2000 0 300,000 1,068 70 154,900 4000 0 5,570 7 50 700,000 200,000 6000 0 2,449 6. 53 257,000 100,000 BO00 0 1,950 59 239,900 1000 00 2,630 7.5 73 349,900 Square Feet 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 2,732 7.5 20 339,900 1,908 5 46 289,000 3,666 6.5 17 399,900 Sum of Bedrooms and Bathrooms Residual Plot Sum of Bedrooms and Bathrooms Line Fit Plot 80000 1,878 7 19 290,000 800,000 2,172 62 278,000 60000 700,000 40000 600,000 SUMMARY OUTPUT 20000 500,000 400,000 Regression Statistics 12 2000 0 Multiple R 0.949366054 300,000 R Square 0.901295904 4000 0 200,000 Adjusted R Square 0.878518035 6000 0 100,000 Standard Error 47571.46177…
A shoe wholesaler wants to optimize its shipment of shoes to its client retailers. To minimize their turnover time and avoid building up stocks that add costs, the wholesaler wants to know if weather conditions are important in determining the type of shoe demanded. Specifically, she / he would like to know if high temperatures affect the sale of closed, rubber-heeled walking shoes. He / she has the relevant data showing the temperature recorded at the location of its retail shops, as well as their record of sales of these walking shoes, during each day of the past year. What type of analysis should he / she conduct? What variables should be considered? Explain.

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