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The overall grade for the class is then typically weighted so that the final grade represents a stated proportion of different types of work. For example, daily homework may be counted as 50% of the final grade, chapter quizzes may count for 20%, the comprehensive final exam may count for 20%, [1] and a major project may count for the remaining ...
The mean for the morning class is 80 and the mean of the afternoon class is 90. The unweighted mean of the two means is 85. However, this does not account for the difference in number of students in each class (20 versus 30); hence the value of 85 does not reflect the average student grade (independent of class).
Unweighted, or "elementary", price indices only compare prices of a single type of good between two periods. They do not make any use of quantities or expenditure weights. They are called "elementary" because they are often used at the lower levels of aggregation for more comprehensive price indices. [2]
Weighted means are commonly used in statistics to compensate for the presence of bias.For a quantity measured multiple independent times with variance, the best estimate of the signal is obtained by averaging all the measurements with weight = /, and the resulting variance is smaller than each of the independent measurements = /.
The second form above illustrates that the logarithm of the geometric mean is the weighted arithmetic mean of the logarithms of the individual values. If all the weights are equal, the weighted geometric mean simplifies to the ordinary unweighted geometric mean. [1]
Grading in education is the application of standardized measurements to evaluate different levels of student achievement in a course. Grades can be expressed as letters (usually A to F), as a range (for example, 1 to 6), percentages, or as numbers out of a possible total (often out of 100).
UPGMA (unweighted pair group method with arithmetic mean) is a simple agglomerative (bottom-up) hierarchical clustering method. It also has a weighted variant, WPGMA , and they are generally attributed to Sokal and Michener .
In weighted least squares, the definition is often written in matrix notation as =, where r is the vector of residuals, and W is the weight matrix, the inverse of the input (diagonal) covariance matrix of observations.
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