### Table 2. CMI Practice Results

"... In PAGE 5: ... After the problems were remedied, efforts to establish consistent charges and capture reimbursement were implemented within the hospital programs already linked to the MARS repository. Reports have demonstrated a significant increase in revenue generated for DSME services ( Table2 ). Efforts are now underway to track reimbursement for every UPMC program.... In PAGE 5: ... Results Integrating a multi-faceted approach to improving diabetes care, including all elements of the Chronic Care Model, has been shown to result in the best outcomes. Implementation of the model resulted in significant improvement in provider practices ( Table2 ) and patient outcomes within the CMI practices as compared to national data (Table 3). Laboratory results of 15,687 patients were collected in the database, which represents 198 PCPs in the laboratory tracking component of the initiative.... ..."

### Table 2. CMI Practice Results

"... In PAGE 3: ... After the problems were reme- died, efforts to establish consistent charges and capture reimbursement were implemented within the hospital pro- grams already linked to the MARS repository. Reports have demonstrated a sig- nificant increase in revenue generated for DSME services ( Table2 ). Efforts are now underway to track reimburse- ment for every UPMC program.... In PAGE 3: ... Results Integrating a multi-faceted approach to improving diabetes care, including all elements of the CCM, has been shown to result in the best outcomes. Implementation of the model resulted in significant improvement in provider practices ( Table2 ) and patient out- comes within the CMI practices as compared to national data (Table 3). Laboratory results of 15,687 patients were collected in the database, which represents 198 PCPs in the laboratory tracking component of the initiative.... ..."

### Table 1. Practical architectural models

### Table 4. Statistics for a partial list of the skills, students and the overall model. Intercept for skill is the initial difficulty level for each skill. Slope is the learning rate. Avg Practice Opportunties is the average amount of practice per skill across all students. Initial Probabltiy is the estimated probability of getting a problem correct in the first opportunity to use a skill accross all students. Avg Probability and Final Probability are the success probability to use a skill at the average amount of opportunities and the last opportunity, respectively.

2006

Cited by 5

### Table 4. Statistics for a partial list of the skills, students and the overall model. Intercept for skill is the initial difficulty level for each skill. Slope is the learning rate. Avg Practice Opportunties is the average amount of practice per skill across all students. Initial Probabltiy is the estimated probability of getting a problem correct in the first opportunity to use a skill accross all students. Avg Probability and Final Probability are the success probability to use a skill at the average amount of opportunities and the last opportunity, respectively.

2006

Cited by 5

### Table 2: Linear model estimation

2006

"... In PAGE 9: ...Computational experience (MIPLIB instances) 3 OUR METHOD Table2 compares the size of the measurement tree obtained by the linear model with the actual number of nodes in T. The last column shows the ratio between the two.... ..."

Cited by 2

### Table 1: Overview of the practice results

"... In PAGE 38: ... The results on the transfer test are summarized in Table 1. Table1 : Summary of the test data. Supportive information Before During Procedural information M SD M SD Equivalent test tasks (max.... In PAGE 53: ...lock was calculated. Results are shown in figure 2. An overall mean score was calculated for the number of times the participants consulted the apos;before apos; information block during all practice problems (see table 1). Table1 : Summary of the mean revisiting behavior data. Supportive information Before During Procedural information M SD n M SD n Before 5.... In PAGE 69: ... Results Practice problems For an overview of the practice results see Table 1. Table1 : Overview of the mean practice results. Information presentation format Variable Split-source Integrated M SD N M SD N Practice scores (max.... In PAGE 80: ... During the physics lesson the participant behavior on the computer and the time spent on each problem was logged. Results Practice problems See Table1... ..."

### Table 3: Deployment key practices

2006

"... In PAGE 4: ... By ap- plying the CCU model onto the eight presented cases, Tables 1, 2, and 3 provide us with a number of observations. Even though some of the cases reported that up to 15% of their deployments failed at the customer side, Table3 shows that soft- ware vendors do not implement all practices of the deployment process. The most commonly reported causes for deployment prob- lems are faulty configurations, incompatible updates, and customi- sations.... ..."

Cited by 2

### Table 9 reports results of these score tests for both subperiods. We face serious prob- lems in the rst subperiod. Here, autocorrelation and ARCH e ects seem to be present. Speci cation tests for the Markov structure raise some doubt about the two state model. However, the tests indicate no such misspeci cations in the second subperiod. One reason might be that the impact of the crash in October 1987 and the period of market distur- bances afterwards induce the need to correct for autocorrelation or to introduce a third state picking up the particular ARCH e ects around the crash. We refrained from doing so, since the second subperiod does not show similar e ects. Nevertheless, in practical ap- plications one should pay attention to the speci cation problems which arise using MSW models.

"... In PAGE 14: ... Table9 : Score tests for dynamic speci cation Finally, we tested whether the imposed zero mean restrictions cause a signi cant loss in t measured in terms of the likelihood. This is also based on scores.... ..."

### Table 9 reports results of these score tests for both subperiods. We face serious prob- lems in the rst subperiod. Here, autocorrelation and ARCH e ects seem to be present. Speci cation tests for the Markov structure raise some doubt about the two state model. However, the tests indicate no such misspeci cations in the second subperiod. One reason might be that the impact of the crash in October 1987 and the period of market distur- bances afterwards induce the need to correct for autocorrelation or to introduce a third state picking up the particular ARCH e ects around the crash. We refrained from doing so, since the second subperiod does not show similar e ects. Nevertheless, in practical ap- plications one should pay attention to the speci cation problems which arise using MSW models.

"... In PAGE 14: ... Table9 : Score tests for dynamic speci cation Finally, we tested whether the imposed zero mean restrictions cause a signi cant loss in t measured in terms of the likelihood. This is also based on scores.... ..."