(NI)C784 MATH 1100 (Appplied Healthcare Statistics) Comprehensive OA Exam Q & S 2024.
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Grado
Nursing
Institución
Western Governors University
(NI)C784 MATH 1100 (Appplied Healthcare Statistics) Comprehensive OA Exam Q & S 2024.(NI)C784 MATH 1100 (Appplied Healthcare Statistics) Comprehensive OA Exam Q & S 2024.(NI)C784 MATH 1100 (Appplied Healthcare Statistics) Comprehensive OA Exam Q & S 2024.
,1. Which statistical test is most appropriate for comparing
the means of three or more independent groups?
- A) t-test
- B) Chi-square test
- C) ANOVA
- D) Mann-Whitney U test
- Answer: C) ANOVA
- Rationale: ANOVA (Analysis of Variance) is used to
compare the means of three or more independent groups to
determine if there are any statistically significant differences
between the means.
2. In survival analysis, what is the main purpose of a
Kaplan-Meier curve?
- A) Compare nominal data
- B) Estimate a population's survival function
- C) Measure correlation between two variables
- D) Assess the variability of a dataset
- Answer: B) Estimate a population's survival function
- Rationale: Kaplan-Meier curves are used to estimate
the survival function from lifetime data, allowing comparison
of survival rates over time.
, - Rationale: The mean is sensitive to extreme values
(outliers), while the median and mode are more robust in
their presence.
4. What does a p-value of 0.03 indicate in a hypothesis
test?
- A) The null hypothesis is true
- B) There is strong evidence against the null hypothesis
- C) The alternative hypothesis is false
- D) The sample size is too small
- Answer: B) There is strong evidence against the null
hypothesis
- Rationale: A p-value of 0.03 indicates that there is a 3%
chance of observing the data, or something more extreme,
if the null hypothesis is true, thus providing evidence
against it.
5. In healthcare informatics, which method is used to
handle missing data points within a dataset?
- A) Listwise deletion
- B) Imputation
- C) Data smoothing
- D) Standardization
- Answer: B) Imputation
- Rationale: Imputation involves replacing missing data
with substituted values, allowing for comprehensive
analyses without discarding any part of the dataset.
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