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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Heteroscedasticity Stigler and Friedland (1983) conducted a study to determine whether the separation of company control from company ownership affects company profits. Using data from 69 companies in the United States, the authors estimate the following model: profiti = α + β1asseti + β2management_controli + ei Where i is company and: profiti = annual profit (in million dollar) asseti = company asset (in million dollar) management_controli = dummy variable that is worth one if the control of the company is held by the manager The regression results are presented in the table in the picture a. Explain whether the statements below are TRUE, FALSE, or CANNOT BE DETERMINED. "If there is a heteroscedasticity problem, the confidence interval of the OLS estimator is not valid." b. Determine the 95% confidence interval for the parameter 2, what can you conclude?
The attached cost-output data were obtained as part of a study of the economies of scale in operating a charter high school in Wisconsin:What type of cost-output relationship (linear, quadratic, cubic) is suggested by these statistical results?
6-58. A pump for a reservoir must be operated continuously (8,760 hours per year). In the event of a large storm, the electricity from the local utilitv's power grid may be interrupted for an indefinite period of time. To deal with this emergency situation, two mutually exclusive backup diesel generators are being investigated. One of them will be chosen for implementation. Relevant data are provided as follows: Generator A Generator B Initial investment $100,000 Annual operating $130,000 8,000 088 20AM and maint. 9,500 Useful life, years o 15 If salvage values of both generators are negligible and the study period is 60 years, which generator should be chosen? The MARR is 10% per year. (6.5) Togen
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
d and e
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 following table. Sale Price/1000 Taxes/1000 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 30.0 5.0500 36.9 8.2464 41.9 6.6969 40.5 7.7841 43.9 9.0384 37.5 5.9894 37.9 7.5422 44.5 8.7951 37.9 6.0831 38.9 8.3607 36.9 8.1400…
Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Which explanatory variables and interaction terms are significant and not significant at alpha = 5%? Explain your answer briefly.
2.
I estimate a multiple linear regression model for portfolio using market size and value and adding as usual a constant ( to make sure that E(errors)=0 as per the GM assumptions request ). Then I wish to identify monthly effects so I generate a dummy for each month of the year , from January to December included . EViews will Select one: a. send out an error message saying : near singular matrix . This is happening because I fell into the dummy variable trap because the sum of all the dummy variables is at any point in time equal to 1 . As a consequence the OLS estimator is not identified b. send out an error message saying : near singular matrix . This is happening because I fell into the dummy variable trap because the costant (c) multiplies the regressor 1 and the sum of all the dummy variable is at any point in time equal to 1 . As a consequence the model is not identified and no estimator can estimate the betas c. send out an error message saying : near singular…
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 ?
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…
13. A researcher estimates the following regression model: Yt = a + ßxt + Ut where yt and xt are both non-stationary variables. Which of the following statements is correct? a) This necessarily leads to spurious results b) The Durbin-Watson d statistic will be larger than the R² c) Including a time trend in the regression specification will provide meaningful results d) Differencing both variables may provide meaningful results but could lose some useful information

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