Which of the following numbers, when rounded off to the nearest thousand, becomes 7 541 000? A 7 530 798 C 7 540 618 ( B 7 531 300 D 7 541 503 5. When 690 203 is broken down according to its digit values, it becomes A 690 000 + 20 + 3 B 690 000 + 200 + 3 C 600 000 + 9 000 + 200 + 3 D 600 000 + 90 000 + 200 + 3 ( 6. Which of the following numbers is the nearest to 1 million? A 989 799 C 1 000 100 ( B 997 899 D 1 100 000 7.
Data Table #1 Trial Mass of Stopper Hanging Mass Radius T1 T2 T3 Revolutions 1 0.014kg 0.05 kg 0.595m 12.93s 13.10s 14.6s 20 2 0.014kg 0.1 kg 0.595m 11.53s 11.78s 10.8s 20 3 0.014kg 0.15 kg 0.595m 8.59s 9.37s 9.35s 20 4 0.014kg 0.2 kg 0.595m 8.0s 8.03s 7.72s 20 5 0.014kg 0.1 kg 0.765m 12.97s 13.81 ------- 20 6 0.014kg 0.1 kg 0.50m 10.78s 9.72s ------- 20 7 0.014kg 0.1 kg 0.60m 10.0s 10.25s ------- 20 *For the last 3 trials, we only had time to take 2 time intervals. IV. Analysis and Interpretation 1. Organizing Data- Calculate the weight of the hanging mass for each trial. This weight is the force that maintains circular motion, Fc.
Since 2.9134>1.2816, 1.6449 and 2.3263, I rejected H0 and accepted Ha at 1 = 0.10, 0.05, and 0.01 and concluded that the mean rating exceeds 42. Since 2.9134<3.0902, I rejected H0 at a = 0.001, and fail to conclude that the mean rating exceeds 42. c. Using the information in part b, calculate the p-value and use it to test H0 versus Ha at each of at = 10, .05, .01 and .001. Upper tail p-value for z = 2.9134 is 00018. Since 0.0018<0.10, 0.65, 0.01, I rejected H0 and accepted Ha at a = 0.10, 0.05, and 0.01 and concluded that the mean rating exceeds 42. Since 0.0018>0.001, I failed to reject H0 @ a = 0.001 and failed to conclude that the mean rating exceeds 42. d. How much evidence is there that the mean composite
Finally I will discuss any weaknesses inherent in the healthcare accreditation process. I will use course provided material and personal research to make my case. Hospital Licensure, Certification and Accreditation Hospital accreditation is not the same as licensure or certification. Licensure is required to operate as a hospital and overseen by state government officials. Certification affords hospitals to participate in federally funded Medicare and Medicaid programs.
Analysis: VHA’s NSQIP Program VHA’s NSQIP Program The VHA’s approach to the NSQIP program was to determine, through measurement strategies, the potential for complications and outcomes of the multitudes of surgical programs in their system. Risk adjusted outcomes were used to monitor and improve surgical components for their VA patients. They included information and data from 87,078 surgical cases related to 30-day mortality and morbidity rate outcomes. Comparative studies between all of their surgical sites were conducted resulting in benchmark standards using “risk adjusted models that were developed for general, vascular, orthopedic, urological, thoracic, cardiac and other surgery specialties. Cardiac surgery models were based on previous methods published” (Khuri et al., 1998) as well.
Other users might include lawyers, employers, law enforcement and researchers. Government licensing agencies make policies that have been determined from the analysis of aggregate data gathered from medical records, in federal and state databases. Institutional users such as hospitals or clinics, depend on the Data Quality Management Model for adequacy and appropriateness of care determined by medical review organizations and the effectiveness of healthcare services reimbursement guidelines enabling the coding and billing departments to receive payments for their services. Research organizations use data to aid in experimental patient care and keep
1. Thermal runway reactions An exothermic reaction can lead to thermal runaway, which begins when the heat produced by the reaction exceeds the heat removed. The surplus heat raises the temperature of the reaction mass, which causes the rate of reaction to increase. This in turn accelerates the rate of heat production. An approximate rule of thumb suggests that reaction rate - and hence the rate of heat generation - doubles with every 10°C rise in temperature.
Line Outage Case A Case C Line Outage Case A Case C Min. Eigenvalue Min. Eigenvalue Min. Eigenvalue Min. Eigenvalue 1 0.4871 Not converged 22 0.5031 0.4621 2 0.5075 0.4595 23 0.5166 0.4753 3 0.5127 0.4703 24 0.5154 0.4732 4 0.5080 0.4601 25 0.4932 0.4506 5 0.5130 0.4610 26 0.5181 0.4768 6 0.5119 0.4650 27 0.4934 0.4506 7 0.5141 0.4722 28 0.5089 0.4683 8 0.5132 0.4736 29 0.5075 0.4673 9 0.5155 0.4823 30 0.4604 0.4199 10 0.4924 0.4474 31 0.4341 0.3945 11 0.5055 0.4653 32 0.4927 0.4509 12 0.5013 0.4603 33 0.3440 0.3132 13 Not converged Not converged 34 Not converged Not converged 14 0.4236 0.3766 35 0.5148 0.4711 15 0.5013 0.4518 36 0.2005 0.1236 16 Not converged Not converged 37 0.3734 0.3257 17 0.5088 0.4675 38 0.4171 0.3576 18 0.4656 0.4245 39 0.5123 0.4676 19 0.5031 0.4627 40 0.4994 0.4579 20 0.5152 0.4740 41 0.4594 0.4186 21 0.5122
LEARNING ACTIVITY 1 WORKSHEET Title of study: Development and Validation of the Nursing Severity Index A New Method for Measuring Severity of Illness Using Nursing Diagnosis Purpose of study: The Purpose of this study was to develop and validate the Nursing Severity Index, a new method used to measure the admission severity of illness of hospital patients using nursing diagnoses, which categorize biologic, functional, cognitive, and psychosocial abnormalities. Statistical methods and analysis used in study: First, the association between individual nursing diagnoses and in-hospital patient mortality was determined using Fisher's exact test. Second the relationship between in hospital-mortality and the number of admission nursing diagnoses was examined using the chi-square test, modified for linear trend. In this and subsequent analyses, the number of diagnoses included both the presence of diagnoses that were directly associated with mortality and the absence of the five diagnoses that were inversely associated with mortality. Third, diagnoses that were independently related to in-hospital mortality were identified; these diagnoses form the Nursing Severity Index.