Hi. I'm the moderator of the spreadsheet with the statistics for PolarPanda's Blog Theory Post. If you could send me your work, I would really appreciate it - I've been looking for new ways to analyze the blog, and I'd like to know your methods for a potential Calculation E. Don't really care if you send it to me here or in a dm. Just show me your work. Thx.
I started with the average number of newcomers a week (though I didn't use the slightly adjusted average, that took into account the number of trailers shown this week, though I should have done) and timesed it by the number of weeks remaining, as Polar Panda did. From there I divided the expected number of unshowcased fighters by the number of blog updates between now and release (I calculated 55, I may have made a mistake). I did round the expected value up to 35 to make my life easier. But I'm going to need to come back to how I used these to work out the odds of a character trailer on each individual day. I used a binomial distribution, which is a statistical method of working out the probability of an event occurring a certain number of times.
n=total number of trials (in this case 55 days)
p=the probability of an event occurring.
X-B(n,p) which I wrote in the initial workings (I did substitute in n and p) is the mathematical notation of a Binomial Distribution. One of the rules or effects of a Binomial Distribution is that np=E(x) meaning expected value which is 35. So 35 is equal to 55 times p and therefore p is 35 over 55, which simplifies to 7/11. So from here I needed to use a hypothesis test. This is a mathematical method that statisticians use to test if the probability of something occurring (or the rate of occurance) has changed. I chose to use this as the whole principle of the blog theory is that there cannot be less that 6 newcomers without the rate of character trailers being decreased, but this is incorrect. I have explained the level of significance above. I chose 5%, I could (maybe should) have chosen 1% but i went with 5%, granted choosing 1% would only allow the possibility of no new newcomers to be possible. Ho and H1 are uneccesary, in retrospect I should have ignored them. What I found was the critical region, which in this context is the highest number of newcomers that could not occur without a change in the average number of newcomers per week. The critical region is found by the highest whole number (Binomial only works with whole or discrete values) for which the probability of an event occurring less than this number of times is less than the level of significance. I will post the exact numbers tomorrow afternoon (British Time) as I don't have access to my equipment. I need a certain kind of scientific calculator to work it out which I don't have access to now. The formula for an event occurring an exact number of times (x) is:
P(X=x)=(nCx)(p to the power of x)((1-p)To the power of (n-x))
(C is a function that I work out with a calculator. I can work this one without but there is no reason to do this so I don't. I can't really explain what it means. If you want I'll post the formula tomorrow as well.)
Anyway to find the cumulative distribution I would need use this formula up to 34 times and total them which would take me days to do accurately, hence why i use this specific calculator which can do all these things instantly.
Hope I was of help. Let me know if I can do anything else.