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  2. Statistics - Wikipedia

    en.wikipedia.org/wiki/Statistics

    A typical statistics course covers descriptive statistics, probability, binomial and normal distributions, test of hypotheses and confidence intervals, linear regression, and correlation. [68] Modern fundamental statistical courses for undergraduate students focus on correct test selection, results interpretation, and use of free statistics ...

  3. Glossary of probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Glossary_of_probability...

    Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...

  4. L-estimator - Wikipedia

    en.wikipedia.org/wiki/L-estimator

    L-estimators can also be used as statistics in their own right – for example, the median is a measure of location, and the IQR is a measure of dispersion. In these cases, the sample statistics can act as estimators of their own expected value; for example, the sample median is an estimator of the population median.

  5. Probability - Wikipedia

    en.wikipedia.org/wiki/Probability

    A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%).

  6. Law of total probability - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_probability

    In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct events , hence the name.

  7. Statistical dispersion - Wikipedia

    en.wikipedia.org/wiki/Statistical_dispersion

    In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.