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Tutor profile: Timothy C.

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Timothy C.
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Questions

Subject: SAS

TutorMe
Question:

You have a two data sets and you want to combine the data sets into one data set. However, you are not sure whether to merge the data sets or set the data sets on top of each other. What is the rationale for each and provide an example code.

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Timothy C.
Answer:

If you have two data sets and you want to combine them and each data set has different variables (i.e., you want to add one set of variables to the other data set), you want to merge the data sets. You want to bring one set of variables over to the other data set. You want to merge by an unique identifier (e.g., ID). An example code would be: data <data name>; merge <set 1> <set 2>; by <unique identifier>; run; It is always important to sort each data set by the unique identifier before merging the data. If you just want to add cases together but the data sets have the same variables, then you just want to 'set' the data sets together. An example code would be: data <data name>; set <set 1> <set 2>; run; This code will set data set 1 on top of data set 2.

Subject: Public Health

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Question:

What is the difference between incidence and prevalence?

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Timothy C.
Answer:

Incidence is defined as the number of new cases in a population at risk of disease. Prevalence is defined as the number of existing cases in a population.

Subject: Statistics

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Question:

A researcher conducts a study to examine the relationship between medication adherence and HIV viral suppression. The null hypothesis is that there is no relationship between medication adherence and viral suppression; the alternative hypothesis is that there is a relationship between medication adherence and viral suppression. The researcher ends up committing a type II error. What did the researcher conclude?

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Timothy C.
Answer:

The researcher concluded that there is no relationship between medication adherence and viral suppression, even though there is a relationship. A type II error is defined as the probability of failing to reject the null hypothesis given that the null hypothesis is false. In this case the researcher failed to reject the null hypothesis (i.e., there is no relationship) even though it is false.

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