Search results
Results from the WOW.Com Content Network
In statistics, a variables sampling plan is an acceptance sampling technique. Plans for variables are intended for quality characteristics that are measured on a continuous scale. This plan requires the knowledge of the statistical model (e.g. normal distribution ).
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.
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.
Proportionate allocation uses a sampling fraction in each of the strata that are proportional to that of the total population. For instance, if the population consists of n total individuals, m of which are male and f female (and where m + f = n), then the relative size of the two samples (x 1 = m/n males, x 2 = f/n females) should reflect this proportion.
The sampling of variables is done with the aid of the mapping sentence technique (see Section 1); and inferences from the sample of observed variables to the entire content universe are made with respect to correspondences between conceptual classifications (of attribute-variables or of population-members) and partitions of empirical geometric ...
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
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]