GLOSSARY

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one or more parameters. Combining the samples for this purpose is usually called pooling. Population standard deviation. See standard deviation. Population variance. See variance. Population. Any nite or in nite collection of individual units or objects. Power. The power of a statistical test is the probability that the test rejects the null hypothesis when the null hypothesis is indeed false. Thus the power is equal to one minus the probability of type II error. Prediction. The process of determining the value of one or more statistical quantities at some future point in time. In a regression model, predicting the response y for some speci ed set of regressors or predictor variables also leads to a predicted value, although there may be no temporal element to the problem. Prediction interval. The interval between a set of upper and lower limits associated with a predicted value designed to show on a probability basis the range of error associated with the prediction. Predictor variable(s). The independent or regressor variables in a regression model. Probability density function. A function used to calculate probabilities and to specify the probability distribution of a continuous random variable. Probability distribution. For a sample space, a description of the set of possible outcomes along with a method to determine probabilities. For a random variable, a probability distribution is a description of the range along with a method to determine probabilities. Probability mass function. A function that provides probabilities for the values in the range of a discrete random variable. Probability. A numerical measure between 0 and 1 assigned to events in a sample space. Higher numbers indicate the event is more likely to occur. See axioms of probability. Process capability ratio. A ratio that relates the width of the product speci cation limits to measures of process performance. Used to quantify the capability of the process to produce product within speci cations. See process capability, process capability study, PCR and PCRk. Process capability study. A study that collects data to estimate process capability. See process capability, process capability ratio, PCR and PCRk.

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Process capability. The capability of a process to produce product within specification limits. See process capability ratio, process capability study, PCR, and PCRk. P-Value. The exact signi cance level of a statistical test; that is, the probability of obtaining a value of the test statistic that is at least as extreme as that observed when the null hypothesis is true. Qualitative (data). Data derived from nonnumeric attributes, such as sex, ethnic origin or nationality, or other classi cation variable. Quality control. Systems and procedures used by an organization to assure that the outputs from processes satisfy customers. Quantiles. The set of n 1 values of a variable that partition it into a number n of equal proportions. For example, n 1 3 values partition data into four quantiles with the central value usually called the median and the lower and upper values usually called the lower and upper quartiles, respectively. Quantitative (data). Data in the form of numerical measurements or counts. Quartile(s). The three values of a variable that partition it into four equal parts. The central value is usually called the median and the lower and upper values are usually called the lower and upper quartiles, respectively. Also see Quantiles. R2. A quantity used in regression models to measure the proportion of total variability in the response accounted for by the model. Computationally, R2 SSRegression SSTotal, and large values of R2 (near unity) are considered good. However, it is possible to have large values of R2 and find that the model is unsatisfactory. R2 is also called the coef cient of determination (or the coefficient of multiple determination in multiple regression). Random. Nondeterministic, occurring purely by chance, or independent of the occurrence of other events. Random effects model. In an analysis of variance context, this refers to a model that involves only random factors. Random error. An error (usually a term in a statistical model) that behaves as if it were drawn at random from a particular probability distribution. Random experiment. An experiment that can result in different outcomes, even though it is repeated in the same manner each time.

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