Please copy and paste all the SAS code, log, and output, fill out the table.
About the project: the objective is to determine whether there is any difference between students who took this course this semester online compared to in-class.
EXCEL datasets EPI626 Online and EPI626 InClass contains selected data collected from students enrolled in this course. Please use the data to fill out the table below.
Table to be filled out*:
General Grading of Final Assignment (required steps).
Student Characteristics
All
Online
In Class
p-value
N = ()
N = ()
N = ()
Age
Gender
Female
Male
Degree program
MPH
MSPH
Other
Number of Languages
1
2
3 or more
Hold Breath for 45 seconds or longer
Yes
No
*For continuous variables, report Mean (SD) or Median (IQR) as appropriate and specify which one you are reporting in the table.For categorical variables, report n and % of total N for each column (All, Online, In Class at top of table)
If you use Import Wizard, please be sure to request and save relevant SAS program
Do NOT modify data in excel sheets by hand prior to import!
Combine data sets
Make sure that in the combined data set, there is a variable which allows you to classify a student as enrolled in the online vs in class course.
Preparing and cleaning combined data set
Please check variable names (and types!)
Please use Dec 31 of this year as the reference date for age calculations for each respective group.
Saving the cleaned data set as a permanent data set
Please exclude anyone from the final cleaned data set if the person has a missing value for one or more of the final variables.
Producing descriptive statistics (cover all characteristic)
Producing p-values (cover all characteristic)
Final report. The final report should address the following:
Describe the process you went through to clean/recode the data. Essentially, your report should allow someone else to replicate your work, based on information provided in the report, and come to the same findings/conclusions.
For each variable:
How many observations have missing values
How many observations have implausible values (e.g., age of 101)
How many observations have inconsistencies to record the same value (e.g., f and F for female)
What did you do to handle the above situations and why?
What is the impact of the approaches you used to clean the data (e.g., observation excluded from analysis)?
What are the attributes of the cleaned data set that you saved permanently?
How many total included observations?
Number of observations having missing values for one or more variables
Distribution of each variable
What procedures did you use to obtain the descriptive statistics and the p-values?
Why?
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