distributionnerovnomerné

Distributionnerovnomerné (also known as non-uniform distribution) is a statistical concept that describes a dataset where the values are not evenly distributed across the range. Unlike uniform distribution, where each value has an equal chance of occurring, non-uniform distribution assigns different probabilities to different values.

In non-uniform distribution, some values may be more frequent than others, leading to a skewed or irregular pattern. This type of distribution is common in real-world scenarios, such as income distribution, where a few individuals earn significantly more than the majority. It can also be observed in natural phenomena, like the distribution of rainfall or the size of particles in a sample.

There are several types of non-uniform distributions, including:

1. Binomial distribution: Describes the number of successes in a fixed number of independent trials, with a constant probability of success.

2. Poisson distribution: Models the number of events occurring within a fixed interval of time or space, given a constant average rate.

3. Exponential distribution: Represents the time between events in a Poisson process, where events occur continuously and independently at a constant average rate.

4. Normal distribution: A symmetric bell-shaped curve, characterized by its mean and standard deviation, where most values cluster around the mean.

Non-uniform distributions are essential in statistics and probability theory, as they help model and understand real-world phenomena more accurately. However, they require more complex mathematical tools for analysis compared to uniform distributions.