Weight management

Definition of weight?

Weight is a relative concept for a certain indicator. The weight of an indicator refers to the relative importance of the indicator in the overall evaluation.

definition weight management

Weight represents the quantitative allocation of the importance of different aspects of the evaluated object in the evaluation process, and the role of each evaluation factor in the overall evaluation is treated differently. In fact, an evaluation without emphasis is not an objective evaluation.

For example, for one thing, you give it 100 points, and your boss gives it 60 points. If it is average, it is (100 60)/2 = 80 points. But because what the boss said weighs more than you, if the boss’s weight is 2 and you are 1, then finding the average is the weighted average, and the result is (100 * 1 60 * 2)/(1 2) = 73. 3 points, obviously tilted towards your boss. If the boss’s weight is 3 and your weight is 1, the result is (100 * 1 60 * 3)/(1 3) = 70. This is the calculation of the average according to the different weights, so it is also called the weighted average.

In short, the weight is to be divided from several evaluation indicators, and the weight corresponding to a set of evaluation index systems constitutes the weight system.

Ask for advice: What does weight mean?

In statistical theory and practice, weight is the weight that indicates the importance of each evaluation index (or evaluation item), indicating the different roles played by each evaluation index in the overall. There are different types of weights, and the weights of various categories have different mathematical characteristics and economic meanings. Generally, there are the following weights. According to the different forms of weights, they can be divided into absolute number weights and relative number weights. Relative number weights, also known as specific weight, can more intuitively reflect the role of weights in evaluation. According to the way weights are formed, they can be divided into artificial weights and natural weights. Natural weights are weights obtained by changing the manifestation of statistical data and the synthesis of statistical indicators, also known as objective weights. Artificial weights are weights that reflect the importance of each indicator by subjective analysis and judgment according to the research purpose and the connotation of evaluation indicators, also known as subjective weights. According to the different divisions of the quantitative characteristics of weight formation, it can be divided into qualitative empowerment and quantitative empowerment. If the methods of qualitative empowerment and quantitative empowerment are combined in statistical comprehensive evaluation, the effect obtained is better. According to the degree of correlation between the weights and the various indicators to be evaluated, it can be divided into independent weights and related weights. Independent weight means that the weight of the evaluation index has nothing to do with the size of the index value, and independent weights are used more in comprehensive evaluation. The comprehensive evaluation model established by this weight is called the “weighted comprehensive” model. Correlation weight means that the weight of the evaluation index has a functional relationship with the value of the index. For example, when the index value of a certain evaluation reaches a certain level, the importance of the indicator decreases accordingly; or when the value of a certain evaluation index reaches another level, the importance of the indicator increases accordingly. Correlation weight is suitable for the condition that the importance of the evaluation index changes with the different values of the index. The comprehensive evaluation model established based on the correlation weight is called the “variable weight model”. For example, the “variable weight comprehensive” model is often used to evaluate environmental quality. There are many methods to determine the weight, including statistical average method, coefficient of variation method and analytic hierarchy process.

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