A uniform distribution is not considered unimodal because it has a constant probability density across its range, meaning there are no peaks or modes. In a unimodal distribution, there is one clear peak where the values cluster, while in a uniform distribution, all values within the specified range are equally likely. Therefore, it lacks a single mode.
It may be or may not be; however a normal distribution is unimodal.
No, the normal distribution is strictly unimodal.
Your distribution is unimodal and symmetrical.
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
Please consider the probability density function graphs for the beta distribution, given in the link. For alpha=beta=2, the density is unimodal, which is to say, it has a single maximum. In contrast, for alpha=beta=0.5, the density is bimodal; it has two maxima.
It may be or may not be; however a normal distribution is unimodal.
No they are not the same in a unimodal symmetrical distribution and they will never be
No, it is in general not true - for example for uniform distribution on [0,1] every number in the interval is a mode, but the mean is 1/2. The correct answer would be that a symmetric unimodal distribution has one mode equal to the mean (but may have modes elsewhere).
Yes it is.
No, the normal distribution is strictly unimodal.
No. Normal distribution is a special case of distribution.
The median and mode.
Your distribution is unimodal and symmetrical.
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
If the distribution is not symmetric, the mean will be different from the median. A negatively skewed distribution will have a mean hat is smaller than the median, provided it is unimodal.
Please consider the probability density function graphs for the beta distribution, given in the link. For alpha=beta=2, the density is unimodal, which is to say, it has a single maximum. In contrast, for alpha=beta=0.5, the density is bimodal; it has two maxima.
No, a bimodal distribution is characterized by having two distinct modes, or peaks, in its probability distribution. This differs from a unimodal distribution, which has only one mode. Bimodal distributions can indicate the presence of two different underlying processes or populations within the data.