{"id":37719,"date":"2025-01-29T20:03:03","date_gmt":"2025-01-29T10:03:03","guid":{"rendered":"https:\/\/myassignmenthelp.info\/assignments\/?p=37719"},"modified":"2025-01-29T20:03:04","modified_gmt":"2025-01-29T10:03:04","slug":"principles-of-biostatistics-2349447","status":"publish","type":"post","link":"https:\/\/myassignmenthelp.info\/assignments\/principles-of-biostatistics-2349447\/","title":{"rendered":"Principles of Biostatistics-2349447"},"content":{"rendered":"
<\/p>\n\n\n\n
For each true-false statement: Replace the small response line with a T or F (capital, black, bold).<\/strong><\/p>\n\n\n\n
Part I True-False Statements: Fill in T<\/strong> or F<\/strong> (4 points each)<\/p>\n\n\n\n\n
In a regression involving age and serum cholesterol level, the \u201cleast squares\u201d or \u201cbest fit\u201d line calculated from the sample data estimates the age-adjusted mean serum cholesterol level in the population. _F__<\/strong><\/u><\/strong><\/li>\n<\/ol>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
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R2<\/sup>, the coefficient of determination, decreases when a variable is removed from a regression model. _T__<\/strong><\/u><\/strong><\/li>\n<\/ul>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
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The following data are from a follow-up study investigating a possible relationship between elevated ferritin levels (>200 \u00b5g\/l) and ischemic stroke:<\/li>\n<\/ul>\n\n\n\n
The appropriate procedure for evaluating the significance of such data is the binomial test. _F__<\/strong><\/u><\/strong><\/p>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
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The maximum likelihood method is used to estimate parameters in logistic regression?\u00a0_T__<\/strong><\/u><\/strong><\/li>\n<\/ul>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
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In a logistic regression model with 2 predictor variables (risk factors A and B) and no interaction term, the estimated relative risk (RR) for subjects exposed to both risk factor A (adjusted RR estimate = 2.0) and risk factor B (adjusted RR estimate = 1.5) is 3.0.\u00a0_F__<\/strong><\/u><\/strong><\/li>\n<\/ul>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
Part II Brief Responses (4 points each)<\/p>\n\n\n\n
For questions 1 – 5: Replace the small response line with your letter choice (capital, black, bold)<\/strong> or fill-in response (black, bold).<\/strong><\/p>\n\n\n\n\n
Part 1: For a fixed sample size: as the number of independent variables in a regression model increases, the power of the regression decreases<\/strong><\/u><\/strong>.<\/li>\n<\/ol>\n\n\n\n
Part 2: When a potential confounding variable is found not to affect the \u03b2-coefficient estimate for the variable of interest in a regression model and is therefore removed from the model, the coefficient of determination (R2<\/sup>) decreases<\/strong><\/u><\/strong><\/p>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
a) increases<\/p>\n\n\n\n
b) <\/strong><\/u><\/strong>decreases<\/strong><\/u><\/strong><\/p>\n\n\n\n
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An F-test for homogeneity of variances (i.e. Bartlett’s test)<\/strong><\/u><\/strong>\u00a0must be performed prior to conducting an analysis of covariance (ANCOVA).<\/li>\n<\/ul>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n
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Part 1: Under what circumstances are chi-square tests biased? all of the above<\/strong><\/u><\/strong><\/li>\n<\/ul>\n\n\n\n
XXX<\/strong><\/strong><\/p>\n\n\n\n\n
if any expected value is less than 1.0 or > 20% of the expected values are less than 5.0<\/li>\n<\/ol>\n\n\n\n
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small sample size<\/li>\n<\/ul>\n\n\n\n
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when there is 1 degree of freedom<\/li>\n<\/ul>\n\n\n\n
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all of the above<\/strong><\/u><\/strong><\/li>\n<\/ul>\n\n\n\n
Part 2: What is the best statistical test to apply when dealing with simple case-control data (single 2 x 2 table)? _Fisher exact test__<\/strong><\/u><\/strong><\/strong><\/u><\/strong><\/p>\n\n\n\n
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