Odds of dating someone with the same birthday

Surface integral from Griffiths, I tried to solve it by considering the parallelepiped with twice the volume of the prism by setting the integrals of x and y from 0 to 1 and z from 0 to 3.Dividing the result by two doesn't give the same answer, why is that?This also however tells us something about match probabilities within our MDM solutions, and critically it has an impact on how we view matching for Big Data, the larger the data set the it will be that false positives occur.This means that when we create probabilities for name matching it should be driven not off a fixed assessment of likelihood but on a combination of factors including the number of instances that the name appears in the source records.You could try rolling three 10-sided dice and five six-sided dice 100 times each and record the results of each roll.Calculate the mathematical probability of getting a sum higher than 18 for each combination of dice when rolling them 100 times.(This Web site can teach you how to calculate probability: Probability Central from Oracle Think Quest.) Observations and results Did about 50 percent of the groups of 23 or more people include at least two people with the same birthdays?When comparing probabilities with birthdays, it can be easier to look at the probability that people do share a birthday.

But when all 23 birthdays are compared against each other, it makes for much more than 22 comparisons. Well, the first person has 22 comparisons to make, but the second person was already compared to the first person, so there are only 21 comparisons to make.Based on the birthday paradox, how many groups would you expect to find that have two people with the same birthday? If you use a group of 366 people—the greatest number of days a year can have—the odds that two people have the same birthday are 100 percent (excluding February 29 leap year birthdays), but what do you think the odds are in a group of 60 or 75 people?• Extra: Rolling dice is a great way to investigate probability.Taking the maths behind the Birthday Paradox and adding in a few more variables, for instance assuming that 100 years will cover everyone and that ages are uniformly spread (this is clearly a false assumption but one which makes it less rather than more likely to find a match) we take the old maths of the Birthday Paradox So now the probability that anyone in a group has exactly the same birth date as another p(n) can be calculated.Lets take the 50/50 point as where we decide that this really isn’t the sort of metric we should rely on and try and understand just how big the 50/50 group is.

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