Contemporary Logistics 12th edition Paul R. Murphy Jr., A. Michael Knemeyer - Solutions

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4. Another reason for the popularity of the logistic model is that the shape of the logistic function is linear.
3. One reason for the popularity of the logistic model is that the range of the logistic function, from which the model is derived, lies between 0 and 1.
2. Suppose the dependent variable for a certain multivariable analysis is systolic blood pressure, treated continuously. Then, a logistic model should be used to carry out the analysis.
1. We can use the logistic model provided all the independent variables in the model are continuous.
10. Which of the following general formulae is not appropriate for computing the effect of SOC controlling for SMK and SBP in Model 1? (Circle one choice.) Explain your answer.a. exp(bS), where bS is the coefficient of SOC in model 1.b. exp[~bi(X1i X0i)].c. P{exp[bi(X1i X0i)]}.
9. For Model 2, compute and interpret the estimated odds ratio for the effect of SOC, controlling for SMK and SBP? (Again, if you do not have a calculator, just state the computations that are required.)
8. If the study design had been case-control, what kind of measure of association could you have legitimately computed from the above models?
7. If the study design had been either case-control or cross-sectional, could you have legitimately computed risk estimates as you did in the previous exercises? Explain.
6. Using Model 2 results, compute the risk ratio that compares person 1 with person 2. Interpret your answer.
5. Compare the estimated risk obtained in Exercise 3 with that for person 1 in Exercise 4. Why are not the two risks exactly the same?
4. Using Model 2, compute the estimated risk for CVD death for the following two persons: Person 1: SOC ¼ 1, SMK ¼ 1, SBP ¼ 150. Person 2: SOC ¼ 0, SMK ¼ 1, SBP ¼ 150. (As with the previous question, if you do not have a calculator, you may just state the computations that are required.)
3. Using Model 1, compute the estimated risk for CVD death (i.e., CVD ¼ 1) for a high social class (SOC ¼ 1) smoker (SMK ¼ 1) with SBP ¼ 150. (You will need a calculator to answer this. If you do not have one, just state the computational formula that is required, with appropriate variable
2. For each of the above models, state the form of the estimated model in logit terms. Model 1: logit P(X) ¼ Model 2: logit P(X) ¼
1. For each of the models fitted above, state the form of the logistic model that was used (i.e., state the model in terms of the unknown population parameters and the independent variables being considered).Model 1: Model 2:
Repeat problem 11 assuming that you plan to use a model that contains age, weight of the mother at the last menstrual period and race.
Consider the low birth weight study. What sample size would be needed in a new study to be able to detect that the odds of a low birth weight baby decrease at a rate of 10 percent per 10 pound increase in weight at the last menstrual period, using a 5 percent type I error probability and 80 percent
Consider the low birth weight study. What sample size would be needed in a new study to be able to detect that the odds of a low birth weight baby among women who smoke during pregnancy is 2.5 times that of women who do not smoke, using a 5 percent type I error probability and 80 percent power?
Evaluate the fit of the usual and exact logistic regression models shown in Table 8.36.
Repeat problem 8 fitting models containing systolic blood pressure (SYS).
Using the data from the ICU Study described in Chapter 1, Section 1.6.1, attempt to fit the usual logistic regression model containing type of admission (TYP) using subjects 25 years of age or younger. Why does the usual MLE have problems in this example? Fit the exact logistic regression model.
Using the covariates in the population average model obtained in problem 5(a) explore alternative ways of including history of a low birth weight baby. For each model compute and interpret the estimates of mate of the odds ratio and a 95 percent confidence interval for the previous low birth weight
Using the cluster-specific and population average models obtained in problem 5(a) explore alternative ways of including the weight of the mother at the last menstrual period. For example, one alternative is to use the weight at the first birth as a cluster level covariate.Others representations are
Using the data from the longitudinal low birth weight study and considering all the covariates (be sure to consider the possibility of interactions among the covariates):(a) Find the best cluster-specific and population average models.(b) Evaluate the fit of the two models obtained in problem
The following exercise is designed to enhance the idea expressed in Figure 8.2 and Figure 8.3 that one way to obtain the proportional odds model is via categorization of a continuous variable.(a) Form the scatter plot of BWT versus LWT.(b) Fit the linear regression of BWT on LWT and add the
Using the final models identified in problems 1 and 2 compare the estimates of the coefficients obtained from fitting the multinomial logistic regression model to those obtained from fitting the adja-cent-category, continuation-ratio and proportional odds ordinal lo-gistic regression models. For
The data for the low birth weight study are described in Section 1.6.2. These data are used in Section 8.2 to illustrate ordinal logis- tic regression models via the four category outcome BWT4,Use the outcome variable BWT4 and fit the multinomial or baseline logistic regression model.In each of the
Data from the mammography experience study are described in Section 8.1. Use a subset of these data and fit a multinomial logistic regression model. For example, you may choose to use only the first 200 subjects. The purpose of the exercise is to obtain practice when there are more than two
The example in Sections 7.5 through 7.7 used only a few of the variables available in the Benign Breast Disease Study. Repeat the modeling using all the covariates.
Data from the 1-3 matched Benign Breast Disease Study are used in this chapter to illustrate methods for a 1-M matched study. The data are described in Table 7.10. Find the best logistic regression model for a 1-1 matched design using the first of the three con- trols. (Note: It would have been
Use the data from the NHANES III survey described in Table 6.1 to find the best model for assessing factors associated with high cholesterol (defined as TCP > 230 mg/100mL). Prepare a table of estimated odds ratios and 95 percent confidence intervals for all covariates in the final model. Compare
Fit the model in Table 6.3 using the suggested fractional polynomials for HSAGEIR and BMPWTLBS. Use this new, non-linear model to provide appropriate odds ratio estimates. Use STATA to import the coefficients from the design-based analysis to compute probabilities of response and assess
Use the data presented in Breslow and Zhao (1988) to perform the analyses reported in that paper and the analysis reported in Fears and Brown (1986).
We noted in Section 5.5 that an argument could be made for combining the previous and recent levels of IV drug use into one group yielding a dichotomous "never-ever" covariate. Evaluate the model using this covariate in place of history of IV drug use coded at three levels.Is the dichotomous
Obtain 95 percent confidence intervals for the estimates in Exercises 5 and 6.
As a more complicated exercise in using the method of logit differences used in Section 5.5, estimate the odds ratio for a 25 year old white subject with 1 previous drug treatment, no history of IV drug use and on the longer treatment at site A compared to a 30 year old non-white subject with 5
Use the method of logit differences to estimate the odds ratio for site A versus B within racial groups.
Fit the final model for the UIS shown in Table 5.10 and obtain the estimated covariance matrix of the estimated coefficients.
Repeat Exercise 2 for the models developed for the low birth weight data and the prostate cancer data in Chapter 4, Exercise 4.
In the exercises in Chapter 4, Problem 3, multivariable models for the ICU study were formed. Assess the fit of the model(s) that you feel was (were) best among those considered. This assessment should in- clude an overall assessment of fit and use of the diagnostic statistics. Does the model fit?
Repeat Exercises 1 and 3 for the Low Birthweight data and the Prostatic Cancer data.
Consider the variable vital status (STA) as the outcome variable and the remainder of the variables in the ICU data set as potential covariates. Use each of the variable selection methods discussed in this chapter to find a "best" model. Document thoroughly the rationale for each step in each
Consider the variable level of consciousness at ICU admission(LOC) as a covariate and vital status (STA) as the outcome vari-able. Compare the estimates of the odds ratios obtained from the cross-classification of STA by LOC and the logistic regression of STA on LOC. Use LOC = 0 as the reference
Selection of the scale for continuous covariates is an important step in any modeling process. The variable systolic blood pres-sure at admission, SYS, in the ICU study described in Section 1.6 presents a particularly challenging example. Consider the vari-able vital status (STA) as the outcome
Consider the ICU data described in Section 1.6.1 and use as the outcome variable vital status (STA) and CPR prior to ICU admission (CPR) as a covariate. (a) Demonstrate that the value of the log-odds ratio obtained from the cross-classification of STA by CPR is identical to the estimated slope
Use the Prostate Cancer data described in Section 1.6.3 and consider the multiple logistic regression model of capsule penetration (CAPSULE),on AGE, RACE, results of the digital rectal exam (DPROS and DCAPS), prostate specific antigen (PSA), Gleason score (GLEASON) and tumor volume (VOL). (a) The
In the Prostate Cancer Study described in Section 1.6.3, one variable thought to be particularly predictive of capsule penetration is the prostate specific antigen level, PSA. Repeat steps (a) – (g) and (j) of Exercise 1 using CAPSULE as the outcome variable and PSA as the covariate. For
In the Low Birth Weight Study described in Section 1.6.2, one variable that physicians felt was important to control for was the weight of the mother at the last menstrual period, LWT. Repeat steps (a) – (g) of Exercise 1, but for Exercise 3(c) use intervals [80, 99], [100, 109], [110, 114],
Use the ICU Study and repeat Exercises 1(a), 1(b), 1(d), 1(e) and 1(g) using the variable "type of admission," TYP, as the covariate.
In the ICU data described in Section 1.6.1 the primary outcome variable is vital status at hospital discharge, STA. Clinicians associated with the study felt that a key determinant of survival was the patient's age at admission, AGE.(a) Write down the equation for the logistic regression model of
Pick, and discuss, two components of the global sourcing development model presented in this chapter.
Refer back to the logistical activities listed in Chapter 1; pick two that you’re interested in and research how they have been influenced by the Internet. Are you surprised by your findings? Why or why not?
Discuss four possible functions that might be performed by international freight forwarders.
What is a freight bill? Why should each freight bill be audited?
Assume that the cost to HDT of borrowing money is 12 percent per year. Because the buyer will pay for trucks as they are delivered, would it be advantageous for HDT to pay overtime to speed up production, ship the trucks as they are finished via the Port of Baltimore, and collect its payment
Of the 10 materials handling principles discusses in the chapter, which two are most surprising to you? Why?
What information is needed to design a protective package properly?
What are some potential nonstorage space needs that might impact warehousing design?
How can taxes and incentives impact facility location decisions?
What are maquiladoras? Why do maquiladoras continue to be popular?
Discuss how the effectiveness and efficiency of order processing can be improved without large expenditures.
How can supply chain finance help procurement drive value for its firm?
What are the components of the global sourcing development model presented in this chapter?
1. When comparing performance during the first five months of 2017 with performance in 2016, which warehouse shows the most improvement?2. When comparing performance during the first five months of 2017 with performance in 2016, which warehouse shows the poorest change in performance?3. The year
Identify some of the key considerations for a logistics manager who is designing and implementing a logistics measurement system in his or her organization.
What are the steps for developing an effective logistics scorecard?
How do you measure gross margin return on inventory (GMROI)?
Most managers believe that although it is possible to connect logistics decisions to costs, the connection to revenue enhancement is difficult to impossible. Provide an example of how logistics could improve sales.
What are the three primary areas where the Sarbanes-Oxley Act (SOX) has implications for logistics managers?
Discuss some ways that inventory can be reduced on a firm’s balance sheet.
What are the three key components of the statement of cash flows?
1: Should the team take into account any other costs or benefits from implementing the WMS? If so, what are they?2: Should the team take into account any other costs or benefits from implementing the TMS? If so, what are they?3: What are the advantages and disadvantages of implementing both
What is the Internet of things (IoT)? How can it potentially affect logistics management?
What are some of the advances in telecommunications technology that have occurred since this book was first published? How do these advances help logistics managers?
Discuss five activities that might be part of a company’s logistics department.
Distinguish between materials management and physical distribution.
What are some ways in which technology has impacted logistics management?

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Contemporary Logistics 12th edition Paul R. Murphy Jr., A. Michael Knemeyer - Solutions

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