I'm not trained in mathematics, but I took a course about using statistics in understanding hotel data. One step of the process requires us to calculate the mean rate of a collection of stay records. However, the instructor first calculated a frequency table of all unique rates, and then determined the mean. He then determines the standard deviation and uses these two values to eliminate outliers.
MY question is whether it is correct to do it this way, or should the mean be calculated from the entire dataset? For example, the real world dataset I'm trying to apply this against actually has an inordinately large number of values at 299 and 329. So I if calculate the mean from just a list of the unique values without considering the frequency, I get a much lower mean than if I do it from the total set.
From my VERY basic understanding of statistics, it could be that the example dataset was normally distributed while my real-world one is quite negatively skewed, so maybe that makes the difference?
Any guidance or direction to resource material would be great. Thank-you, AF