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Independent Risk Factors In Management

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Independent Risk Factors In Management Question: Discuss about the Factors of Independent Risk Factors in Management.   Answer: 1: The table showcases the relationship between risk of incurring cancer and exposure to magnetic and electric fields of varying power use conditions; as mandated clearly from the provided information. Clearly, the or(odds ratio) for the first table shows an unmistakeably increasing trend from low power use conditions if we kept increasing the strength of the magnetic fields. However, a p-value of 0.14 shows relatively low significance and that the trend lies far below the requisite 95 % interval to prove the null hypothesis. (that a trend exists in the observations) As with the high power use conditions for exposure with magnetic fields, the odds ratio doesn’t show any clear trend with relatively low significance due to a high p-value. (0.43) We can safely reject null hypothesis in this case. With high power use condition with Electric field, odds ratio between number of diagnosed and control cases doesn’t give any particular trend with p-value on the higher side. Thus trend is not detectable. To summarize: Low power use conditions in magnetic fields gives perceivable trend in the odds ratio between number of cancer and controlled cases with a low significance. High power use conditions in both magnetic and electric fields fail to show discernible effect on cancer diagnosis to control ratio. 2. Based on the figure 1, a sample size of 200 was randomized and allocated to usual medical care and exercise program groups and the effect of each on clinical depression was studied with no significant difference posted. The one flaw in the methodology of sample selection was elimination on the basis of the PHQ test I which around 95% of the sample was eliminated due to a score of less than 10. Such elimination might have biased the sample under inspection since many of the invitees may not have attached due importance or noticed the same. Such huge elimination have significant scope of biasing the entire data set. Potential confounders from the table are the variables of smoking and drinking since both seemed to have highly negative correlation with both the groups while past history of depression diagnosis seemed to be positively correlated with both the groups. (about 80%)   The 0.68 value of odds ratio tells us the odds that 26 weeks will show more than 50% reduction in SIGMA scores 0.68 times for the exercise intervention group as compared to the medical care group. It further tells that odds ratio for the same will be within 0.36 to 1.28 with 95% confidence or with probability 0.95 in a normal distribution. The confidence interval of odds-ratio for each rows in table 2 shows too much width of the spectrum which implies that the effect of depression on the exercise group and basic medical control group are not very significantly different. Since the effect of exercise doesn’t show a distinctly higher odds ratio of responding positively to depression with sufficient confidence, it can be concluded that exercise form of treatment didn’t help that much in curing clinical depression. It portrays the sentiment of the entire study.

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