Here's the population:

A - 224,000,000 (72.5%)

B - 39,000,000 (12.6%)

C - 46,000,000 (14.9%)

TOTAL population - 309,000,000

In that population, members have a certain attribute X, as follows:

A - 232,000 (59.5%)

B - 147,000 (37.7%)

C - 11,000 ( 2.8%)

TOTAL with attribute X - 390,000

1. Since B having attribute X is disproportionately overrepresented compared to their representation in the total population, is it correct that one should expect a randomly selected member B to be more likely to have that attribute than member A?

2. What is the probability that a member of the population is both A and has the attribute X?

3. What is the probability that a member of the population is both B and has the attribute X?

4. If one randomly selected member of the population is A, what is the probability that member has attribute X?

5. If one randomly selected member of the population is B, what is the probability that member has attribute X?

6. If two members of the population are randomly selected, one A and one B, what is the probability that member A has attribute X?

6. If two members of the population are randomly selected, one A and one B, what is the probability that member B has attribute X? ]]>

P(X >=2 lambda) <= 1/lambda

The way I thought to interpret this theorem was to determine what percentage of the data is clustered near the mean when faced with a non-normal distribution like a Poisson distribution that determines the probability of a given number of events within a fixed interval. This is where I get tripped up when attempting to lay out the proof with a symbol (lambda) as opposed to a number. Any assistance would be greatly appreciated. Thanks. ]]>

Tennis match. Player A to hold serve is 85%, Player B to hold serve is 80%.

What is the probability that player A will win 6-0?

I have converted the percentage into fractions so to hold serve the odds are 3/17 and to break serve the odds are 4/1, is this correct. if we then multiply through the 3 holds and 3 breaks i get 1728/4913 which equates to 73.98%. This seems high, have i gone wrong here? ]]>