Assume aggregating the inventory in a single warehouse increases the transportation costby $45,000. If a CSL of 95% is desired and the correlation coefficient is estimated to be0.5 (all other parameters unchanged), is it a feasible choice to aggregate the demand?What if the correlation coefficient is estimated to be 0.2? What is the conclusion?

Given that the inventory in a single warehouse will increase the transportation cost by $45,000. The…

Answered: Assume aggregating the inventory in a…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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4. Consider the following multiple regression results, where the dependent variable is the number of movie tickets sold per week, X, is the ticket price, and X2 is the cost of DVD rental. -51.0918X¡1 + 41.4607X¡2 (37.0184) (13.130) (0.584) Sp t = (-6.800) SSR = 17,023 SSE = 6,262 = 23,285 SST = п 3D 20 Complete the missing entries in the output. 1 | Are the slope coefficients, b, and b2, individually statistically significant (a = 0.10 2. 3. Calculate the standard error of the regression (s.) and the R2.
Need only handwritten solution only (not typed one).
Where we observe multicollinearity in a multiple regression analysis, two of our independent variables, x-1 and x-4, are so highly correlated they’re almost indistinguishable with respect to their relationship with our dependent (y) variable. Why is that a problem?
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
13. In the regression equation Log(Cars_Sold) = 1500 + 2.3*Advertisement, what is the most appropriate interpretation of the slope coefficient? OA. a. $1 increase in advertisement, increases cars sold by 230% O B. b. $1 increase in advertisement increases cars sold by 2.3 OC. c. 1% increase in advertisement increases cars sold by 0.023 OD. d. 1% increase in advertisement increases cars sold by 230%
9. A wildlife researcher is interested in predicting a mammal’s lifespan (in years) based on its average number of hours of sleep per night. Data was obtained from a large random sample of mammals, and the regression equation turned out as follows: Predicted lifespan = 36.93 – 1.64 (average hours of sleep) Which one of the following statements is a correct interpretation of this equation? 1. Approximately 1.64% of the variability in lifespan can be explained by the regression equation. 2. As lifespan increases by one year, average hours of sleep is predicted to decrease by 1.64 hours. 3. As lifespan increases by one year, average hours of sleep is predicted to increase by 36.93 hours. 4. As average hours of sleep increases by one hour, lifespan is predicted to increase by 36.93 years. 5. As average hours of sleep increases by one hour, lifespan is predicted to decrease by 1.64 years.
Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?
Ankylosing spondylitis is an inflammatory disease which affects the spine and sacroiliac joint and can lead to the fusing of vertebrae in the spine. Exercise is important in helping manage pain, therefore a pharmaceutical company wanted to test if their new biologic would increase the time a patient could exercise before experiencing discomfort. The following table gives the time (in minutes) seven patients were able to exercise before reporting pain, both at baseline and then again after six months of taking the biologic.Exercise Time (in minutes)Subject 1 Baseline 30 Post-treatment 40Subject 245 40Subject 350 60Subject 435 37Subject 555 60Subject 620 25Subject 775 75Assume the population(s) of all exercise times is (are) normally distributed. Use JMP to test whether the evidence suggests that the time a person with ankylosing spondylitis can exercise before experiencing discomfort increases after taking this new biologic. Validate the test statistic from JMP by computing it manually.…
2. I surveyed 150 adults in the U.S. and asked them how many hours of TV the watched on average per week. I then ran a regression of # of hours of TV on whether or not they were a college graduate (=1 if yes, =0 if no), their age in years, the number of children in their household, and whether or not they live a cold climate (=1 if latitude is greater than 41.2, =0 otherwise). The results fro the regression are shown in the table below. Estimate p-value College Graduate -0.8 0.003 Age 0.1 0.150 Number of Children 0.10 0.521 Cold Climate 1.8 0.047 Intercept -1.23 0.041 (a) What is the dependent variable in this regression? (b) What are the independent variable(s) in this regression? (c) What is the unit of analysis? (d) What is the sample size? (e) What is one binary variable used in this analysis? (f) What is one ratio variable used in this analysis? (g) What is the predicted number of TV hours watched by a 50 year old, colleg graduate, with no children at home who lives in Arizona…
The following table gives the regression results of consumption behaviour between female and male. Consumption (C), in RM Model 1 Model 2 Constant 0.7515 0.6587 (0.024) (0.037) Y 0.8603 0.5442 (0.0301) (0.2881) Gen 0.2001 (0.0004) Y*Gen 0.3105 (0.0076) Adjusted R 0.78 0.78 No. of Observations 47 47 Note: Y=income (RM) Gen= Gender ( It is equal to 1 if person is female; 0 is for male) Figures in the parentheses are p- values a) Identify which are the qualitative, quantitative and interaction variable. Refer to Model 1: b) Write the regression equations for female and male. Draw the regression lines for female and male on the same diagram. What can you c) say? d) Interpret all the estimated slope coefficients. e) Conduct an appropriate test to determine whether variable gender is statistically significant at 5%. Should this variable be dropped from this regression model.
In a simple regression analysis (where y is a dependent and x an independent variable), if the slope is positive, then it must be true that there is a positive correlation between x and y there is a negative correlation between x and y if x is increased, y must decrease None of the answers is correct.

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