Probability

The probability of rolling a sum of 5 with two die is 4 over 36, or 2 over 18, or 1 over 9.

With 3 fair dice, pipped 1 through 6, there are 10 permutations that add up to 6. They are 114, 123, 132, 142, 213, 222, 231, 312, 321, and 411. The total number of permutations of the three dice is 63 or 216. The probability, then, of rolling a sum of 6 on 3 dice is 10 in 216, or 5 in 108, or about 0.0463.

63000 of 1000

The probability is 25/216 = 0.1157, approx.

approximately 1 in 20

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My calculation

for the first card 13 out of 52 = I in 4

Now there are only 12 heart cards remaining among 51 cards.

for the second 12 out of 51 = or 4 out of 17. so for both (1/4) * (4/17) = 1/17

and so on 11 out of 50 and 10 out of 49.

Multiplication gives us approx. 1 out of 379.

The above is for 4 cards of a suite and not for colour.

for the first 26 out of 52 = I in 2

Now there are only 25 red cards remaining among 51 cards.

for the second 25 out of 51 so for both (1/2) * (25/51) = 25/102

and so on 24 out of 50 and 23 out of 49.

Multiplication gives us approx. 1 out of approx. 18

2 6-sided? 0%

2 10-sided? 7%

2 20-sided? 3.25%

It is 1/3.

Answer If twins run in your family then it's a good chance that you too will have twins but it doesn't mean that the first baby will have a twin.

to reproduce seeds to plat another flower

First of all, there's the sample size. If you don't have a big enough sample, you can't give a very reliable answer. Of course, with limited resources it might not be possible to collect a large sample. There's a tradeoff between sample size and reliability.

Also, there are aspects of statistics which are countrintuitive and tend to confuse people. For instance, Simpson's Paradox. We all know that lots of sunlight is good for crops. But if you do a survey of crop yield and compare it to weather records, you might actually find that the sunnier it is, the less the crops grow. This is different from the problem of small sample size outlined above. I'm not talking about a few freak years with lots of pests throwing the statistics out. The problem is that when it's sunny, it generally isn't raining. So the years with lots of sunshine have lower crop yields because they have less rain. It is possible to correct for this, by looking at records of both sunshine and rainfall. Basically, if you're looking for a direct relationship between two variables, you need to think of all the other variables that might have an effect and take those into account. But I think this increases the sample size needed, if you overdo it.

Another fallacy is to assume that correlation implies causality. For instance, you could do a survey and discover that poorer neighbourhoods have more crime. You might conclude that living in such a place makes someone a criminal. But this might not be the case. It might be that wealth affects both crime and place of residence, i.e. poor people tend to live in certain places (because they can't afford to live anywhere else), and poor people often turn to crime (to feed their families). Or there might be some other variable we haven't thought of yet.

Actually, statistics is an exact science. It is the application of the results that leads to problems.

Perhaps the most commonly encountered limitation of statistics is the misunderstanding that a statistical measure can be used as a measure of the accuracy of a measurement.

Statistics, in general, provide very little information on the intrinsic accuracy of a measurement. Statistics can only provide an estimate of the minimal error that might be in the measurement. The actual error can be much greater than the minimal (statistical) error.

Another way to put it is that statistics measure the variability of a measurment, not the accuracy of a measurment.

It is 1/221.

Assume that the standard deck is completely shuffled in a completely unbiased way.

The probability of drawing the first ace is 4/52, since there are 4 aces in a standard deck.

The probability of drawing the second ace is 3/51, since there are three aces remaining and 51 cards from which to choose.

12/52 X 51 equals 12/2652, which equals 1/221.

Prob(Having a heart) = 1 - Prob(No hearts)

= 1 - (39/52)*(38/51)*(37/50)*(36/49)*(35/48)*(34/47)*(33/46)*(32/45)

= 1 - 2480637519360/30342338208000

= 1 - 0.0818 = 0.9182

1/6 or 1 in 6 :)

you need to know more about the paper cup. what is its shape? is it weighted more on a side or bottom? when you are computing the chances are you including your knowledge of the outcome of the previous 365 times you tossed the cup? how are you tossing it? how high are you tossing it? etc.

clearly there is a lot of unanswered questions before we can give a good answer.

Yes, and is equal to 1. This is true for normal distribution using any mean and variance.

You will roll 600 times, of course!

i think it's 6x7=42 any1 else think that's right ;)

See related link.

2/3 left

ND measures the number of outlets per total number of outlets from the specific market, while WD measures how much the outlet sells of the total volume.

Assuming the returns are nomally distributed, the probability is 0.1575.

There are 40 cards that are NOT jacks, queens, or kings, so the probability of drawing one of these cards is 40/52 = 10/13 = 0.769

There are 36 total ways two dice can be rolled. 4 of those give you a 9. The probability is 4/36=1/9

1 in 2652. The odds of drawing one red king are 1 in 52. The odds of drawing the second are 1 in 51. Multiply 52 by 51, and you get 2652.

1/676

there's 4 suits in one deck of cards.

if j =11, queen =12, and king =13.

that means there will be 6 even numbers card in one suit, and times that by 4, which mean 24. theres 24 cards are even in a regular deck of cards.