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Suppose that we take a sample of size n from each of k populations with the same normal distribution N(μ, σ 2) and suppose that ¯ is the smallest of these sample means and ¯ is the largest of these sample means, and suppose S 2 is the pooled sample variance from these samples. Then the following random variable has a Studentized range ...
For example, a ranged loop like for x = 1 to 10 can be implemented as iteration through a generator, as in Python's for x in range(1, 10). Further, break can be implemented as sending finish to the generator and then using continue in the loop.
The scope of test cases usually rely on the software tester involved, who uses experience and intuition to determine what situations commonly cause software failure, or may cause errors to appear. [2] Typical errors include divide by zero, null pointers, or invalid parameters. [3]
The end-loop marker specifies the name of the index variable, which must correspond to the name of the index variable at the start of the for-loop. Some languages (PL/I, Fortran 95, and later) allow a statement label at the start of a for-loop that can be matched by the compiler against the same text on the corresponding end-loop statement.
The else clause in the above example is linked to the for statement, and not the inner if statement. Both Python's for and while loops support such an else clause, which is executed only if early exit of the loop has not occurred. Some languages support breaking out of nested loops; in theory circles, these are called multi-level breaks.
The example discussed by Duncan in his 1955 paper is of a comparison of many means (i.e. 100), when one is interested only in two-mean and three-mean comparisons, and general p-mean comparisons (deciding whether there is some difference between p-means) are of no special interest (if p is 15 or more for example). Duncan's multiple range test is ...
A language that supports the statement construct typically has rules for one or more of the following aspects: Statement terminator – marks the end of a statement; Statement separator – demarcates the boundary between two statements; need needed for the last statement; Line continuation – escapes a newline to continue a statement on the ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]