Five numbers define a double sampling plan: n1, n2, C1, C2, and c3. The plan is imple- mented in the following way: One draws an initial sample of size n1 and determines the number of defectives in the sample. If the number of defectives in the sample is less than or equal to c1, the lot is accepted. If the number of defectives in the sample is larger than c2, the lot is rejected. However, if the number of defectives is larger than c1 but less than or equal to c2, another sample of size n, is drawn. If the number of defec- tives in the combined samples is less than or equal to c3, the lot is accepted. If not, the lot is rejected. Most double sampling plans assume that c3 = c2. We will make that assumption as well from this point on. A double sampling plan is obviously more difficult to construct and more difficult to implement than a single sampling plan. However, it does have some advantages over single plans. First, a double sampling plan may give similar levels of the consumer's and the producer's risks but require less sampling in the long run than a single plan. Also, there is the psychological advantage in double sampling plans of providing a sec- ond chance before rejecting a lot.

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.