Probability

Generally, in an investment analysis, one is interested in determining if the risk is justified by the expected return and if the expected return is competitive to other uses of funds. Several approaches are used in risk analyses, which range from very simple approaches to more complex ones. First there is a simple qualitative approach, where one lists elements that could reduce profit (lower product price, higher costs, higher investments, delays from suppliers) and calculates expected decline in revenue. This is called the worse case scenario. Probability theory really doesn't play a role in this approach. Second, one can associate probabilities to factors (i.e. low, medium and high product price could be associated with 25, 50 and 25% probabilities) and a decision tree (risk analysis diagram) is constructed to provide the full range of combinations of events/factors, their probability of occurrence and outcome. These can be ranged and statistics, such as the mean or median outcome, and the high and low outcomes, can be identified. The third option is to associate factors with a distribution, and run Monte-Carlo simulation. Common distributions used are the uniform, triangular and normal distribution. Many programs exist to run Monte-Carlo models and summarize results. I've included a related link on the use of simulation in finance.

Theoretically, zero. The finite probability can only exist between two points. However, if we are simulating a continuous distribution with a random number generator, all values are truncated to a finite number of digits depending on the register size. If we consider a uniform random number generator, with a 7 digit accuracy, then matching a single value is 1/10,000,000, pretty close to 0.

50%

it was created in 1964 and it was greattt bahhht :P peace out my niggwars

We use three coins (quarter, nickel, dime) each are flipped only once. We get 8 possible outcomes (or four outcomes as an alternative).

The probability of drawing a spade from a standard deck of 52 cards is 13 in 52, or 1 in 4, or 0.25.

you will land on 1 or 6 5/6 times

that all depends on which facts you acknowledge as a priori state for this situation.

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There are 13 clubs in a deck of 52 cards. The probability of drawing 1 club from a deck of 52 is 13/52 or 1/4.

.820=82.0%

there are 2 jocker but in card it has 3jocker

About 48% or if you want to be more accurate the actual answer was 47.826%

We'll assume standard six-sided dice.

The first die rolls... well, whatever it rolls. It doesn't really matter what the specific number is.

The second die can come up any of six different ways. One of those ways is with the same number as the first, the other five are different.

So the chances of rolling two dice and getting the same number on both is 1/6.

A probability can never be greater than 1.

Four lizards, is one possible answer.

There are 12 months to choose from There are 7 months with 31 days in them.

The probability of choosing a 31-day month is 7/12.

There are 30 students total. 10 of them have brown hair. 10/30 equals 1/3. Answer: 1/3

Probability of drawing a heart: 1/4

Probability of drawing a club: 1/4

Probability of drawing a heart or a club: 1/4 + 1/4 = 2/4 = 1/2

There are 676,000 ways to make the license plates.

The probability is determined by the binomial distribution. We consider p = probability of defect, q = probability of not defect, n = sample size, and x= number of defects in sample, in this case x=2. We calculate the probability as P(X = x) = n!/[(n-x)! x!] pxqn-x If sample size = 10 and p = 0.1 then: P(x= 2) = 10!/(8!x2!)(0.1)2(0.9)8 = 0.1937 You can find more about the binomial distribution under Wikipedia. It is important also to note the assumptions when using this distribution. It must be a random sample and the probability of defects is known.

1/2 or 50%

Children's genders are not independent and therefore there is no simple answer.

However, if you do assume that their genders are independent then the probability is 1/3.

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