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A polynomial of degree zero is a constant term

The grouping method of factoring can still be used when only some of the terms share a common factor A True B False

The sum or difference of p and q is the of the x-term in the trinomial

A number a power of a variable or a product of the two is a monomial while a polynomial is the of monomials

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Q: For a symmetric distribution of data the mean is equal to which other quantity?
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What is symmetrical normal distribution?

The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.

Is symmetrical a characteristic of a normal distribution?

The Normal ditribution is symmetric but so are other distributions.

Is the uniform probability distribution is symmetric about the mode?

Yes, the uniform probability distribution is symmetric about the mode. Draw the sketch of the uniform probability distribution. If we say that the distribution is uniform, then we obtain the same constant for the continuous variable. * * * * * The uniform probability distribution is one in which the probability is the same throughout its domain, as stated above. By definition, then, there can be no value (or sub-domain) for which the probability is greater than elsewhere. In other words, a uniform probability distribution has no mode. The mode does not exist. The distribution cannot, therefore, be symmetric about something that does not exist.

What is symmetric distribution?

It is a probability distribution in which the probability of the random variable being in any interval on one side of the mean (expected value) is the same as for the equivalent interval on the other side of the mean.

Do liters and meters equal the same?

If one is a distance, and the other is a quantity, no. They do not equal the same...

What is the difference between saying that one quantity is proportional to another and saying it is equal to another?

When one quantity is proportional to another, it indicates that one quantity is dependent on the other by a factor and increases/decreases with the other quantity. When the two quantities are equal, the output of both the quantities is said to be the same.

How to identify the the shape of probability distribution?

You cannot. There are hundreds of different distributions. The shapes of the distributions depend on their parameters so that the same distribution can be symmetric when the parameters have some specific value, but is highly skewed - in either direction - for other values.

How does the standard normal distribution differ from the t-distribution?

The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.

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!

What is a nonzero number?

A quantity which does not equal zero is said to be nonzero.

Can you give me a symmetric figure?

Yo, anything that's sides are the same as the other, yo got a symmetric figure. example: a circle

Why is population distribution not uniform all over Nepal?

In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of symmetric probability distributions such that for each member of the family, all intervals of the same length on the distribution's support are equally probable. The support is defined by the two parameters, a and b, which are its minimum and maximum values. The distribution is often abbreviated U(a,b). It is the maximum entropy probability distribution for a random variate X under no constraint other than that it is contained in the distribution's support

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