Study guides

☆☆

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

Write your answer...

Submit

Still have questions?

Continue Learning about Other Math

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!

Symmetric means having similarity in shape, size, and relative position of corresponding parts. It refers to a thing wherein one side is a mirror image or reflection of the other.

A linear relationship

Feedback in general is the process in which changing one quantity changes a second quantity, and the change in the second quantity in turn changes the first.Positive feedback amplifies the change in the first quantity while negative feedback reduces it.....

The normal distribution can have any real number as mean and any positive number as variance. The mean of the standard normal distribution is 0 and its variance is 1.

Related questions

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

The Normal ditribution is symmetric but so are other distributions.

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.

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.

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

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.

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.

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.

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!

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

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

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

People also asked