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Hi all, I recently got accepted to a few programs and wanted to hear any thoughts folks might have. I got accepted to NYU's 1-year MSPP, Carnegie Mellon's 2-year MSPPM-Data Analytics track, and Indiana University's 2-year MPA program. Currently, NYU is most expensive (only got 15% funding), CM is less expensive (got 50% funding), and Indiana is the cheapest (I got 40% funding but the program is very cheap). I'd like a career in public policy analysis and want to invest in data analytics skills that I've been working on in my professional experiences since graduating college. My thought is
Hello, I am interpreting some data that was calculated by someone else and initially I was confused how this person got these numbers. So I emailed the office that oversees the analysis of this information and their explanation still left me a little confused. Hopefully someone can clarify this for me. The data is from pre and post surveys. Part of the survey asks about dietary intake. So here is an example of a question asked: In the past week, how many servings of fruit did you eat per day? 0 times per day, less than 1 time per day, 1 time per day, 2 times per day, 3 time
The question is: "There is a belief among students that if they go to office hours, it will increase their midterm exam score by 5 points. Evaluate this belief by means of an appropriate t test." The data are presented as the midterm exam scores in one column (quantitative variable), of 100 students, and ‘went to office hours or not’ in another column (categorical variable, which can be recoded as a dummy variable with 0 as went and 1 as didn’t go), which describes each of the 100 students. I added two new columns, copying and pasting the scores of those people who went to of