Probability

If we are certain of what we know then we become solid and sure on the actions that we do....a knowledge that is founded and based on certainty is not a thithered knowledge. Hence a knowledge that is bot back up with certainty makes man's decison ad action unstable and not even credible

The probability is 25/216 = 0.1157, approx.

It is 1/3.

1 + 5

5 + 1

2 + 4

4 + 2

3 + 3

5 ways

2 in 3 percent

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

The probability of rolling doubles with 2 dice is 1 in 6, or about 0.1667. The probability of doing that 100 times in a row is 0.1667100 or about 1.531x10-78.

A statistician, or anyone who wants to ensure that, for a population that can be divided into a set of strata, the sample is forced to contain the same proportion from each stratum.

2 6-sided? 0%

2 10-sided? 7%

2 20-sided? 3.25%

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.

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.

See related link.

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

You will roll 600 times, of course!

2/3 left

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

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.

it might be 395,652,150,468,457,125,279,456,000,000 chanch to roll a prime number when a monkey appeares at the door

26 of them.

Because there are only 2 outcomes for the flip of a coin, for 5 flips you just need to take (1/2)5, which equals 1/32.

This implies there are 32 different outcomes for the case of tossing a coin 5 times. From these 32 outcomes 5 have exactly 4 heads: THHHH, HTHHH, HHTHH, HHHTH, and HHHHT. So the probability of getting exactly 4 heads when you toss a coin 5 times is:

P(4H,!T) = 5/32 = 0.15625 ≈ 15.6%

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

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

It dependes on the genes of the parent if you have no information of the genes of the parent I cannot tell you