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Q–Q plot for first opening/final closing dates of Washington State Route 20, versus a normal distribution. [5] Outliers are visible in the upper right corner. A Q–Q plot is a plot of the quantiles of two distributions against each other, or a plot based on estimates of the quantiles.
For any population probability distribution on finitely many values, and generally for any probability distribution with a mean and variance, it is the case that +, where Q(p) is the value of the p-quantile for 0 < p < 1 (or equivalently is the k-th q-quantile for p = k/q), where μ is the distribution's arithmetic mean, and where σ is the ...
Quantile functions are used in both statistical applications and Monte Carlo methods. The quantile function is one way of prescribing a probability distribution, and it is an alternative to the probability density function (pdf) or probability mass function, the cumulative distribution function (cdf) and the characteristic function.
In particular, the quantile is 1.96; therefore a normal random variable will lie outside the interval in only 5% of cases. The following table gives the quantile z p {\textstyle z_{p}} such that X {\textstyle X} will lie in the range μ ± z p σ {\textstyle \mu \pm z_{p}\sigma } with a specified probability p {\textstyle p} .
In statistics, a standard normal table, also called the unit normal table or Z table, [1] is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution.
A quantile-based credible interval, which is computed by taking the inter-quantile interval [, +] for some predefined [,]. For instance, the median credible interval (MCI) of probability γ {\displaystyle \gamma } is the interval where the probability of being below the interval is as likely as being above it, that is to say the interval [ q ...
and Φ −1 is the standard normal quantile function. If the data are consistent with a sample from a normal distribution, the points should lie close to a straight line. As a reference, a straight line can be fit to the points. The further the points vary from this line, the greater the indication of departure from normality.
Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value. A boxplot is a standardized way of displaying the dataset based on the five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.