A contour map is shown for a function f on the square R = [0, 8] x [0,8]. y 8H 0 30 40 50 38 40 x 8 (a) Use the Midpoint Rule with m = n = 2 to estimate the value of 1360 f(x,y) dA. (Round your answer to the nearest integer.) (b) Estimate the average value of f. (Round your answer to one decimal place.) 21.3 ×

A contour map is shown for a function f on the square R = [0, 8] x [0,8]. y 8H 0 30 40 50 38 40 x 8 (a) Use the Midpoint Rule with m = n = 2 to estimate the value of 1360 f(x,y) dA. (Round your answer to the nearest integer.) (b) Estimate the average value of f. (Round your answer to one decimal place.) 21.3 ×

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.4: Multiple Regression Models

Problem 16P: Suppose that a regional express delivery service company wants to estimate the cost of shipping a...

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Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
For the next 6 numbers: Refer to the Management Scientist output of a maximization LP problem. The constraints are defined as follows: Constraint 1: advertising budget ( ) Constraint 2: sales force availability ( ) Constraint 3: production level (=) Constraint 4: retail stores requirement ( ) Optimal Solution Objective Function Value - Variable. Constraint X1 X2 X1 X2 X3 X4 X3 X4 1 2 Variable 1 2 3 3 4 OBJECTIVE COEFFICIENT RANGES 4 Constraint RIGHT HAND SIDE RANGES Value Slack/Surplus Lover Limit 25.000 425.000 150.000 0.000 84.000 50.000 No Lover Linit No Lover Linit Lover Limit 48450.000 0.000 25.000 0.000 0.000 4950.000 1775.000 515.000 0.000 Reduced Costs Dual Prices Current Value 90.000 84.000 70.000 60.000 Current Value 0.000 0.000 0.000 45.000 5000.000 1800.000 600.000 150.000 3.000 0.000 60.000 -17.000 Upper Limit No Upper Linit 90.000 87.000 105.000 Upper Linit 5850.000 No Upper Limit 603.571 200.000
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(d) As part of a marketing program, a city block containing 4 households was selected and a sample of 3 households was sampled as follows. Jeremiah, the research assistant, identified the households and numbered each household from 1 to 4. He then want to list all combinations of the 4 households 2 at a time. These are: 1 and 2 1 and 3 1 and 4 2 and 3 2 and 4 3 and 4 Unfortunately, Jeremiah got this job because he was related to the Department Chairman, and he was very careless. He forgot the combina- tion 3 and 4. He chose a random number between 1 and 5 (it turned out to be 4) which corresponded to the combination 2 and 3. Thus households 2 and 3 were sampled. The variable of interest was out-of-pocket medical expenses incurred by the household (Table Q1 (d)). Table Q1 (d) : Out-Of-Pocket Medical Expenses Incurred by the Household Expenses ($) 345.00 Household 1 126.00 492.00 962.00 3 4 (ii) Based on Jeremiah's sampling procedure, what is the expected value of mean and variance of…
The manager of Mayo Department Store want to evaluatethe relationship between the sales of air conditioners andthe average weekly temperature. The following weeklysales data along with the average daytime temperaturesfor 10 one-week periods are shown in the following table:Week Air-conditioner Sales(in units) Average Temperature (in degrees Fahrenheit)1 100 782 85 703 103 854 110 905 70 756 115 947 150 1018 120 989 97 8610 85 80 a Develop a linear regression equation for air conditionersales as a function of the average weekly temperature.b Forecast air conditioner sales if the average weeklytemperature is 88 degrees Fahrenheit.c Evaluate the "goodness of fit" of the regressionequation by…