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In statistical quality control, the p-chart is a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size, n.
^ = = sample proportion, unless specified otherwise = hypothesized population proportion = proportion 1 = proportion 2 ... Template: List of statistics symbols.
In statistics a population proportion, generally denoted by or the Greek letter, [1] is a parameter that describes a percentage value associated with a population.A census can be conducted to determine the actual value of a population parameter, but often a census is not practical due to its costs and time consumption.
Represents one categorical variable which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area), is proportional to the quantity it represents. For example, as shown in the graph to the right, the proportion of English native speakers worldwide; Line ...
A "parameter" is to a population as a "statistic" is to a sample; that is to say, a parameter describes the true value calculated from the full population (such as the population mean), whereas a statistic is an estimated measurement of the parameter based on a sample (such as the sample mean). Thus a "statistical parameter" can be more ...
A chart map represents each geographic feature with a statistical chart, often a pie chart or bar chart, which can include a number of variables. Each chart is usually drawn proportionally to a total, making it a multivariate symbol. Chernoff faces have occasionally been used in maps since the 1970s, generally in an experimental situation.
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr or 3 σ, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean ...