So I'm playing a videogame that shows you some statistics at the end of a mission, one of those stats is damage taken, and it comes as a percentage. Suppose it is a squad of 2 players, and both take 0 damage. Should the final stats show 50% for both of them, or 0%?

50% would make the percentage work, and it would be statistically correct, but it would also imply that damage was taken, which isn't the case

0% feels right, but it would also be the only case in which these stats do not add up to 100%

Hey everyone! I’m sharing last year’s final exam for Statistics 1 at my business school (Tunis Business School).

Context for our program:

This is a required intro-level course for all business administration freshmen (sem 2). None of us are majoring in statistics—the first two years are a broad common core covering calculus, linear algebra, micro/macroeconomics, accounting, stats, etc.

We take Statistics 2 in our sophomore year.

I’m curious to get your perspective:
How does the difficulty level of this paper compare to introductory business stats courses at your university?

Does this look fair for a general business core, or is it on the tougher/easier side?

I have cgpa of 3/4 in bsc maths

I am going to apply at

Texas A &m

Uni of georgia

Kentucky state

Should i try any more unis?

Also what are the chances i get in?

Question for statisticians , would it be a good choice to pursue ms stats in 2027 given the job market?

When building a calibration curve for a process that is linear over most of the observed range, how to determine the best choice between the following options?

(A) increasing the number of values tested, to get more x axis points;

(B) increasing the number of replicates of each measurement –less x points, but more precise estimate of y for each

For example, if an experimental setup lets me run 12 measurements for a linear calibration curve, is it better to run 4 values in triplicate? Or 6 in duplicates?