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Attributes are closely related to variables. A variable is a logical set of attributes. [1] Variables can "vary" – for example, be high or low. [1] How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high"). [1] (For example see: Binary option)
A single sampling plan for attributes is a statistical method by which the lot is accepted or rejected on the basis of one sample. [4] Suppose that we have a lot of sizes M {\displaystyle M} ; a random sample of size N < M {\displaystyle N<M} is selected from the lot; and an acceptance number B {\displaystyle B} is determined.
A variables sampling plan can be designed so that the OC curve passes through two points (AQL,) and (LQL,). AQL and LQL are the Acceptable quality limit and the limiting quality level respectively. α {\displaystyle \alpha } and β {\displaystyle \beta } are the producer and consumer's risks.
The variables upon which the population is stratified are strongly correlated with the desired dependent variable. Advantages over other sampling methods. Focuses on important subpopulations and ignores irrelevant ones. Allows use of different sampling techniques for different subpopulations. Improves the accuracy/efficiency of estimation.
Common tools and techniques of measurement system analysis include: calibration studies, fixed effect ANOVA, components of variance, attribute gage study, gage R&R, [1] ANOVA gage R&R, and destructive testing analysis. The tool selected is usually determined by characteristics of the measurement system itself.
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
The pps sampling results in a fixed sample size n (as opposed to Poisson sampling which is similar but results in a random sample size with expectancy of n). When selecting items with replacement the selection procedure is to just draw one item at a time (like getting n draws from a multinomial distribution with N elements, each with their own ...
Use variable-width control limits [2]: 280 Each observation plots against its own control limits: ¯ ¯ (¯), where n i is the size of the sample that produced the ith observation on the p-chart Use control limits based on an average sample size [2]: 282