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There are probably many probability distributions that have just one parameter. The most important one for statistical analysis is probably the Student t distribution.
This probability distribution is fully described by a single parameter which is often called "degrees of freedom". The parameter describes the scale of the distribution, and not the location, since the Student t distribution is always centered at zero (unlike the normal distribution, which has a scale parameter, the variance, and a location parameter, the mean).
Another example of a distribution that is described with a single parameter is the exponential distribution. Unlike the Student t distribution, it is a distribution that takes only positive values.
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What is a confidence interval?
Answers.com says it is: A statistical range with a specified probability that a given parameter lies within the range. I think that means, just how confident you are that your statistical analysis is correct.
What is the relationship between the Z-score for Normal Distribution and Continuous Probability?
There is no real relationship. Probabilities for the Normal distribution are extremely difficult to work out. The z-score is a method used to convert any Normal distribution into the Standard Normal distribution so that its probabilities can be looked up in tables easily. There are infinitely many types of continuous probability distributions and the Normal is just one of them.
Differences between probability samplingand non-probability sampling?
the difference is just that non-probability sampling does not involve random selection, but probability sampling does.
How can the Poisson distribution be applied to a continuous frequency distribution?
Let L(t) be the instantaneous average rate of occurrences per unit time, at time t. So, for the ordinary Poisson distribution with parameter L, we just have L(t)=L for all t.Let I be the integral of L(t) dt over a certain time interval [0,T], say.Then, assuming that L(t) is continuous, or maybe just Riemann integrable, the total number of occurrences during [0,T] simply follows a Poisson distribution with parameter I. This is the simple answer one might expect.To prove this (SKETCH: further estimates are needed to make this really rigorous): divide [0,T] into many small intervals [tj, tj+1). In each interval, the number of occurrences is approximately Poisson with parameter L(tj)(tj+1-tj).The occurrences in each small interval are all independent of each other; hence the total number in [0,T], which is the sum of all these, follows a Poisson distribution with parameter the sum of L(tj)(tj+1-tj).As you make the maximum size of the intervals shrink to zero, this sum tends towards I, the Riemann integral of L(t)dt over [0,T], as required.
What is the probability that they you will get two heads when you toss two coins?
If you toss them enough times, the probability is 1. For just one toss the probability is 1/4.
Can you use the formulas for a probability distribution to calculate parameters for a binomial probability distribution or can you just use the given formulas for a binomial probability distribution?
This depends on what information you have. If you know the success probability and the total number of observations, you can use the given formulas. Most of the time, this is the case. If you have data or experience which allow you to estimate the parameters, it may sometimes happen that you work like this. This mostly happens when n is very large and p very small which results in an approximation with the Poisson distribution.
How are your results similar to the distribution of electron of an atom?
The results of an atom's electron distribution are similar to our calculations in that both involve the probability of finding a particular entity (electron or result) in a specific state. Just as the electron cloud represents the likelihood of finding an electron in a particular location, our results show the likelihood of obtaining a specific outcome in our experiment. Both concepts involve probability distributions to describe possible states or outcomes.
What is a confidence interval?
Answers.com says it is: A statistical range with a specified probability that a given parameter lies within the range. I think that means, just how confident you are that your statistical analysis is correct.
What is the relationship between the Z-score for Normal Distribution and Continuous Probability?
There is no real relationship. Probabilities for the Normal distribution are extremely difficult to work out. The z-score is a method used to convert any Normal distribution into the Standard Normal distribution so that its probabilities can be looked up in tables easily. There are infinitely many types of continuous probability distributions and the Normal is just one of them.
What probability value would be needed to complete the following probability distribution?
You should be given p(x) values such as 0.09 0.19 0.14 0.29 you just add these values to get 0.71 subtract 0.71 from 1 to get 0.29 is the answer
Write three dimensional Schrodinger wave equation for an electron. what is the physical significance of its wave funtion2?
The Wave function (psi) is just used as an identifier that the particle exhibits wave nature. Actually the square of the wave fn (psi2 ) - the probability amplitude- is the real significant parameter. The probability amplitude gives the maximum probability of observing the particle in a given region in space.
Is the total area within any continuous probability distribution is equal to 1.00?
Yes, but not just continuous prob distribs. It applies to discontinous or discrete distributions as well.
How do you put bootstrap parameter in dota?
simple just type warcraft.exe in browser.In bootsrap parameter type -normal done.
Coefficient of discharge is a dimensionless parameter?
It is a dimensionless parameter since its just a ratio between two quantities of same unit.
How do you put game boostrap parameter in garena?
simple just type warcraft.exe in browser.In bootsrap parameter type -normal done.
What does it mean normal distribution?
The Normal distribution is a probability distribution of the exponential family. It is a symmetric distribution which is defined by just two parameters: its mean and variance (or standard deviation. It is one of the most commonly occurring distributions for continuous variables. Also, under suitable conditions, other distributions can be approximated by the Normal. Unfortunately, these approximations are often used even if the required conditions are not met!
In an atom the electrons can be found in the?
Orbitals. Electrons are negative charges that move around the (positive) core. They do so in so called orbitals, which describe the chance that an electron is at a certain position. They cannot be said to really rotate in the classical sense, such as the moon does around the earth; it's just a probability distribution. This is the field of quantum mechanics if you want to study in more detail.
