# Statistics and Probability distribution               In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.

- Probability distribution

Numerical descriptors include mean and standard deviation for continuous data (like income), while frequency and percentage are more useful in terms of describing categorical data (like education).

- Statistics 9 related topics ## Probability theory

Branch of mathematics concerned with probability.

Branch of mathematics concerned with probability.  Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes (which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion).

As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of data. ## Sampling (statistics)    In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population.

In this case, the 'population' Jagger wanted to investigate was the overall behaviour of the wheel (i.e. the probability distribution of its results over infinitely many trials), while his 'sample' was formed from observed results from that wheel. ## Statistical dispersion In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. ## Standard deviation      In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values.

The standard deviation of a random variable, sample, statistical population, data set, or probability distribution is the square root of its variance. ## Variance In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its population mean or sample mean.

The variance is the square of the standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by. ## Mean  There are several kinds of mean in mathematics, especially in statistics.

The mean of a probability distribution is the long-run arithmetic average value of a random variable having that distribution. ## Pearson correlation coefficient    In statistics, the Pearson correlation coefficient (PCC, pronounced ) ― also known as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), the bivariate correlation, or colloquially simply as the correlation coefficient ― is a measure of linear correlation between two sets of data.

The population Pearson correlation coefficient is defined in terms of moments, and therefore exists for any bivariate probability distribution for which the population covariance is defined and the marginal population variances are defined and are non-zero. ## Chi-squared distribution In probability theory and statistics, the chi-squared distribution (also chi-square or χ 2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.

The chi-squared distribution is a special case of the gamma distribution and is one of the most widely used probability distributions in inferential statistics, notably in hypothesis testing and in construction of confidence intervals. ## Binomial distribution

{{Probability distribution

{{Probability distribution   In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p).