It is pretty close to 0. It is estimated as the number of people in the NFL divided by the total population of the world!
The probability density function of a random variable can be either chosen from a group of widely used probability density functions (e.g.: normal, uniform, exponential), based on theoretical arguments, or estimated from the data (if you are observing data generated by a specific density function). More material on density functions can be found by following the links below.
8 were defective while (75-8=67) weren't. So the estimated probability that a flyer won't be defective is 67/75
Well, that's not much of a question. Perhaps you are asking: What is the frequency interpretation of probability? This is called the classical interpretation of probability. Given n independent and identical trials with m occurrences of of a particular outcome, then the probability of this outcome, is equal to the limit of m/n as n goes to infinity. If you are asking: How can probabilities be estimated given data, based on frequency approach? A table is constructed, with intervals, and the number of events in each interval is calculated. The number of events divided by the total number of data is the relative frequency and an estimate of probability for the particular interval.
The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.
It is 55/150 = 11/30
One way of finding the probability is to carry out an experiment repeatedly. Then the estimated experimental probability is the proportion of the total number of repeated trials in which the desired outcome occurs.Suppose, for example you have a loaded die and want to find the probability of rolling a six. You roll it again and again keeping a count of the total number of rolls (n) and the number of rolls which resulted in a six, x. The estimated experimental probability of rolling a six is x/n.
About a 74% estimated probability of green,
If it lands on a six 140 times then the estimated probability of a six is 140/400 = 0.35
I doubt it. The probability of being hit by a meteorite in your lifetime is estimated at about 1 in 700,000.
It is pretty close to 0. It is estimated as the number of people in the NBA divided by the total population of the world!
It means that the probability is calculated (or more precisely, estimated) based on experiment. For example, if a certain event occurs 70 times in 1000 tries, you can estimate the probability to be approximately 7%.
Experimental or empirical probability is estimated from repeated trials of an experiment. However, instead of actually carrying out the experiment a very large number of times, it may be possible to simulate them.
It is pretty close to 0. It is estimated as the number of people in the NFL divided by the total population of the world!
There cannot be such a value since the total area, being a probability, is 1.
There are two main ways: One is to calculate the theoretical probability. You will need to develop a model for the experiment and then use the laws of science and mathematics to determine the probability of the event (subject to the model's assumptions). A major alternative is the empirical or experimental method. This requires performing the trial many times. The probability of the event is estimated by the proportion of the total number of trials which result in the outcome of interest occurring.
The probability density function of a random variable can be either chosen from a group of widely used probability density functions (e.g.: normal, uniform, exponential), based on theoretical arguments, or estimated from the data (if you are observing data generated by a specific density function). More material on density functions can be found by following the links below.