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Probability
The probability of a certain event is a number expressing the likelihood that a specific event will occur, expressed as the ratio of the number of actual occurrences to the number of possible occurrences. In mathematics, it is a measure of how often an event will happen and is the basis of statistics.
14,643 Questions
How do you find the probability of something?
Sometimes you can make a practical assumption about the probability of something occurring by considering all the possible outcomes. For example, a coin only has two sides so the probability of it landing on heads (assuming that the coin is "fair") is 1/2. Similarly, a die only has six sides so the probability of it landing on a 4 is 1/6. At other times it may not be possible to make any assumptions about the possible outcomes. In those situations you may have to estimate the probability by measuring how many "successes" you get as a proportion of how many "trials". Say, for example, you want to estimate the probability of a yellow truck going down the road. You could sit by the side of the road for a day and measure the number of yellow trucks that pass by (successes) as a proportion of all traffic (trials).
What is the value of the standard error of the sample mean?
The sample standard deviation (s) divided by the square root of the number of observations in the sample (n).
Yes, the range gives you an idea of the S.D. Assuming that the largest and smallest data points are not "outliers," a set of data with a wide range will have a greater S.D. than a set with a narrow one.
random sample
What is a standardized unit that tells how far away each measurement is from the mean?
Standard deviation is a measure of the spread of data around the mean. The standardized value or z-score, tells how many standard deviations the measurement is away from the mean, and in which direction.
z score = (observation - mean) / standard deviation
Standard deviation is the unit measurement. This tells what the value a decimal is.
What is the probability of an event occurring to the probability that it will not occur?
The probability that an event will occur plus the probability that it will not occur equals 1.
What word describes the probability of an event occurring to the probability that it will not?
compliment- it's a word for the probability minus one, so it something has a .6 probability, the probability(compliment) it woun't occur is .4 The answer for the Statistic Crossword Puzzle is "odds"
Measurements that fall beyond three standard deviations from the mean?
I believe outliers is the best answer to this question. The previous answer was average, which is the mean.
It is a simple random sample.
How many standard deviations a measurement is away from the mean?
The answer depends on the individual measurement in question as well as the mean and standard deviation of the data set.
What tells how many standard deviations a measurement is away from the mean?
the Z score, or standard score.
What are measurements called that fall beyond three standard deviations from the mean?
Measurements. Just because a particular result lies far from the mean doesn't make it any different. If it's noticeably far from the "crowd" of all the other measurements, it can be called an outlier. An outlier isn't necessarily bad, just different. It should be examined in detail to see if there's something odd about it, but not discarded out of hand.
This tells how many standard deviations a measurement is away from the mean?
Z-Score tells how many standard deviations a measurement is away from the mean.
% chance to occur + % chance to not occur = 100% Expressed in decimals, 40% chance to occur + 60% chance to not occur = 100%. .40 + .60 = 1.00 More detailed answer: === === Probability of event occurrence versus failure to occur First, probabilities are expressed as decimals or fractions with values between zero and one, with zero representing no possibility of an event's occurrence, and one representing the certainty of occurrence. For example, the probability of flipping a heads with one flip of a fair coin is 0.5 or 1/2. The probability of rolling a snake-eye with one roll of one fair die is 0.167 or 1/6. The probability of pulling the Ace of spades out of regular deck of shuffled cards (without the jokers) is 0.0192 or 1/52. The probability of pulling a heart -- any heart -- out of the same deck (assuming the Ace of spades was put back in) is 0.25 or 13/52. Remember, a probability must always be between 0 and 1. If you ever do a probability calculation and get a result greater than one, you screwed up. Second, the probability of any event's failure to occur is one minus the probability of the event's occurrence. So, if the probability of rolling a snake-eye with one fair die is 0.167, then the probability of NOT rolling a snake-eye is 1 - 0.167 = 0.833. (Or 1 - 1/6 = 5/6.) The probability of NOT drawing a heart from a deck of 52 cards is 1 - 0.25 = 0.75. (Or 1 - 1/4 = 3/4). == ==
The short answer is "random sample," but that, unfortunately, is neither specific nor complete. It is not specific because there are forms of random sampling where selection probabilities are not constant. It is not complete because there are many different ways to conduct random sampling with equal selection probabilities.
"Simple random sampling" occurs when you can perform a process that, for all practical purposes, behaves like writing down the identifier of each population member on a piece of paper, putting all the pieces into a box, mixing them thoroughly, and pulling out a few of them one by one (without replacing them in the box). Nowadays we use a computer to do this job, because it's faster and more reliable (it is notoriously difficult to mix pieces of paper perfectly randomly). The computer needs a complete list of all the population members: this is called a <i>sampling frame</i>.
Here is an example of random sampling that is not simple but still selects every population member with equal probability. Suppose you want to sample half the students in a classroom of 30. Ask them to line up. Flip a fair coin: if it's heads, pick the first, third, ..., 29th in line. If tails, pick the second, fourth, ..., 30th. Any individual student has a 50% chance of being part of the sample, so each student has an equal probability of being included. However, if you lined up the students boy-girl-boy-girl, etc., the samples themselves wouldn't look very random: they will either be mostly boys or mostly girls. It's still random though, because it's determined by the flip of a coin.
The example highlights a subtle but important property of a random sample: in many cases, you want the selection of population members to be <b>independent</b>. This means the probability of selecting one member is not affected by which other members are selected. In simple random sampling, independence holds; in the second example (a form of <i>gridded sampling</i>), there is complete dependence: no student can be chosen along with either of their neighbors in line, for instance.
Simple random sampling is ideal for many purposes but often cannot be carried out in practice because it is not feasible (you might not be able to construct a sampling frame) or costs too much. Often, more complicated procedures, such as <i>hierarchical sampling</i>, are carried out to overcome these limitations. (An example of hierarchical sampling is when an epidemiologist selects a city at random, then selects households at random within the city, then selects children at random within each household to study. Doing it this way can require much less travel than selecting children at random from all over the state.) These procedures might or might not select population members with equal probability. Usually the selection is not independent, either. When the probabilities are unequal, they can be figured out and used as <i>weights</i> in statistical analysis of the data. Results can also be adjusted for lack of independence.
A good, readable, non-technical introduction to sampling and simple random samples is the textbook <i>Statistics</i> by Freedman, Pisani, and Purves. Any edition is fine. Steven Thompson's book <i>Sampling</i> discusses dozens of different sampling procedures and explains the theory behind each one.
How is probability used in business?
Probability is used throughout business to evaluate financial and decision-making risks. Every decision made by management carries some chance for failure, so probabiity analysis is conducted formally ("math") and informally (i.e. "I hope"). Math is the preferred method but requires some advanced training, like college courses. For everyone else theres "I hope I guess right"
What is the probability that a randomly chosen number is not divisible by 2 3 or 6?
The probability that a randomly chosen [counting] number is not divisible by 2 is (1-1/2) or 0.5. One out of two numbers is divisible by two, so 1-1/2 are not divisible by two.
The probability that a randomly chosen [counting] number is not divisible by 3 is (1-1/3) = 2/3.
Similarly, the probability that a randomly chosen [counting] number is not divisible by N is (1-1/N).
The probability that a random number is not divisible by any of 2, 3 or 6 can be reduced to whether it is divisible by 2 or 3 (since any number divisible by 6 can definitely be divided by both and so it is irrelevant). This probability depends on the range of numbers available. For example, if the range is all whole numbers from 0 to 10 inclusive, the probability is 3/11, because only the integers 1, 5, and 7 in this range are not divisible by 2, 3, or 6. If the range is shortened, say just from 0 to 1, the probability is 1/2.
Usually questions of this sort invite you to contemplate what happens as the sampling range gets bigger and bigger. For a very large range (consisting of all integers between two values), about half the numbers are divisible by two and half are not. Of those that are not, only about one third are divisible by 3; the other two-thirds are not. That leaves 2/3 * 1/2 = 1/3 of them all. As already remarked, a number not divisible by two and not divisible by three cannot be divisible by six, so we're done: the limiting probability equals 1/3. (This argument can be made rigorous by showing that the probability differs from 1/3 by an amount that is bounded by the reciprocal of the length of the range from which you are sampling. As the length grows arbitrarily large, its reciprocal goes to zero.)
This is an example of the use of the inclusion-exclusion formula, which relates the probabilities of four events A, B, (AandB), and (AorB). It goes like this:
P(AorB) = P(A) + P(B) - P(AandB)
In this example, A is the event "divisible by 2", and B is the event "divisible by 3".
Soo it goinqq to bee 4/52 soo i guess you simply that intill you get the riqht answer
a friend on the farm= mike howe
itchin' and scratchin'= amos keto
What is the probability of the spinner landing on Phil?
The answer depends on the characteristics of the spinner.
The answer depends on the characteristics of the spinner.
The answer depends on the characteristics of the spinner.
The answer depends on the characteristics of the spinner.
Answer D- A higher sample size gives more accurate results- APEX LEARNING
