Probability

Probability is the likelihood that something will occur. If you subtract it from 1, we get the likelihood (or probability) that it will not occur.

If a coin is tossed and rolls heads 6 times, the (empirical) probability of obtaining a head is 6/10 or .6.

1-.6 =.4 is the empirical probability (or likelihood) of not getting a head.

B

As U KNOW THERE ARE 13 CARDS OF HEART IN A DECK AND THERE IS A TOTAL OF 52 CARDS . sO EVERYTIME U TAKE A HEART IT AFFECTS THE TOTAL NO OF CARDS . so THE ANSWER WOULD BE (13*12*11*10*9*8)/(52*51*50*49*48) = 8.42890. app.

I tried my best to explian sorry about the mistakes and i hope it would have helped u

The chances of drowning in a bath tub depend on several things, most obvious of which would be your level of fatigue and/or the water level of a drawn bath - If said person is tired and fall asleep, they may eventually slide under the water level, although, in turn, this would wake said person, and through instinct they would rise above the water. Unless someone was fully unconcious, it isn't likely that someone could drown.

I'm not sure on the actual percentages. It firstly depends on if your father is an identical or non-identical twin. (Maternal or fraternal) Non-identical (fraternal) twins are the hereditary type, not the identical ones. Identical twins are simply a hiccup with the cell division during fetal growth, whereas non-identical twins are due to the hereditary tendency of the woman releasing more than one egg at a time. The gene for non-identical twins is directly passed from fathers to daughters, and the chance of having twins usually skips a generation too (like baldness in men!). So, if your father is a twin (a non-identical twin) and you are his daughter, it's possible that you may also have twins, but it's far more likely that your daughter will. My father is a twin (his brother died at birth and it's not known if he was maternal or fraternal unfortunately) and I only have 1 child (a daughter). She has a very high chance of having twins if dad was fraternal.

1 and 2 are the only two numbers less than 3 so the odds of rolling a number less than 3 is 2/6 or 1/3. Now the odds of heads is 1/2. We use the multiplication rule and we have

1/2x 1/3=1/6 so the odd are 1 in 6

sam. theorm

50% boy 50% girl or 1/2

the same probability as me shaging your ma tonight gigity

A 6-sided die has 6 possible outcomes. If each of the sides has a different number, one through six, then the probability of getting any one number is 1/6.

An operation is commutative if it can be done front to back or back to front like addition.

2+3=5 and 3+2=5

but division is not

6/3=2 but 3/6= .5

It depends on what size die you use, what its labels are and how many rolls you make. For example using a standard six-sided die and one roll, the probability of no sixes is 5/6 or ~0.83; the probability of no sixes with 25 rolls is less than 0.01 or 1%. If you used a standard d3 (three-sided die) then the probability will always be 1 or 100%, since rolling a six is impossible; but if every side has '6' on it the probability is 0, since every roll must be a 6.

This is a binomial probability distribution with the following criteria: Number of trials = 6, number of successes = 2, probability of success = 1/6 or 0.16667. Because of the probability value not being a typical number, there is not a table to go to look the answer up. The math is tedious. So, we go to a binomial probability calculator and enter in the aforementioned data to obtain the answer of 0.201.

A simple continuous distribution can take any value between two other values whereas a discrete distribution cannot.

individual outcome of an experiment

Convert to Std Normal Distribution and calculate (or use java script) area under curve for probability (see related links). Z55= (55-55)/6 = 0 and Z65 = (65-55)/6 = 1.667. We need area under curve for Z from 0 to 1.667. From javascript = use between 0 & 1.667, obtain .4522 or 45.22% probability. From Table; 0.9525-0.5 = .4525 = 45.25% (use above; more accurate but this IS close).

Yes. By definition. A normal distribution has a bell-shaped density curve described by its mean and standard deviation. The density curve is symmetrical(i.e., an exact reflection of form on opposite sides of a dividing line), and centered about (divided by) its mean, with its spread (width) determined by its standard deviation. Additionally, the mean, median, and mode of the distribution are equal and located at the peak (i.e., height of the curve).

With two flips of a coin you can get two heads, two tails, a head and a tail, or a tail and a head. There are a total of four different possible outcomes, and three of them have at least one head. That's 3 out of 4, or 3/4ths. It's also 0.75 which is the probability of getting at least one head with two flips of a coin. Note that as we use the term probability here, it is zero (no chance it can happen at all), or one (it must happen), or something in between. A probability appears in the form of a fraction or decimal, and has no units attached to it.

The probability of correct true & false question is 1/2 and the probability correct multiple choice (four answer) question is 1/4. We want the probability of correct, correct, and correct. Therefore the probability all 3 questions correct is 1/2 * 1/2 * 1/4 = 1/16.

To find the solution, describe the events that will allow the same color draws which are WW or BB or YY. Since replacement is not stated, without replacement is assumed. So, we need probability of WW, BB, and YY (added together) which are (2/12*1/11) + (4/12*3/11) + (6/12*5/11) or (2/132 + 12/132 + 30/132) or 44/132 or1/3.

The probability of a flipped coin landing heads or tails will always be 50% either way, no matter how many times you flip it.

Convert to Standard Normal Distribution the values of 1.22 & 8.78. Z = (8.78-5)/1.41 = 2.68; and Z = (1.22-5)/1.41 = -2.68. Find the area between 2.68 & -2.68 from Table. Area @ 2.68 = .9963; Area @ -2.68 = .0037. Take the difference in the areas and that is the solution. 0.9963 - 0.0037 = .9926 or 99.26% of the scores fall between 1.22 & 8.78.

The standard deviation is used in the numerator of the margin of error calculation. As the standard deviation increases, the margin of error increases; therefore the confidence interval width increases. So, the confidence interval gets wider.

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