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Experiments of the Mind最新文献

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Practicing Experimental Tasks 练习实验任务
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.15
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引用次数: 0
Sensing the World 感知世界
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.9
Philip B. Morgan
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引用次数: 0
ACKNOWLEDGMENTS 致谢
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.5
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引用次数: 0
REFERENCES CITED 参考文献引用
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.20
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引用次数: 0
Doing This Ethnography 做人种学
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.8
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引用次数: 0
Experimenting Scientifically 实验科学
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.10
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引用次数: 0
Normalizing Data 规范数据
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.11
H. Abdi
We often want to compare scores or sets of scores obtained on different scales. For example, how do we compare a score of 85 in a cooking contest with a score of 100 on an I.Q. test? In order to do so, we need to “eliminate” the unit of measurement, this operation is called to normalize the data. There are two main types of normalization. The first first type of normalization originates from linear algebra and treats the data as a vector in a multidimensional space. In this context, to normalize the data is to transform the data vector into a new vector whose norm (i.e., length) is equal to one. The second type of normalization originates from statistics, and eliminates the unit of measurement by transforming the data into new scores with a mean of 0 and a standard deviation of 1. These transformed scores are known as Z-scores.
我们经常想比较在不同尺度上获得的分数或分数集。例如,我们如何比较烹饪比赛的85分和智商测试的100分?为了做到这一点,我们需要“消除”度量单位,这个操作被称为规范化数据。有两种主要的规范化类型。第一类归一化源自线性代数,并将数据视为多维空间中的向量。在这种情况下,对数据进行规范化就是将数据向量转换为一个新的向量,其范数(即长度)等于1。第二种归一化源于统计学,通过将数据转换为均值为0、标准差为1的新分数,消除了度量单位。这些转换后的分数被称为z分数。
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引用次数: 14
Entering Social and Digital Media 进入社交和数字媒体领域
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.18
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引用次数: 0
Envisaging “Productive Thinking” 设想“高效思维”
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.16
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引用次数: 0
Gazing Technologically 盯着技术
Pub Date : 2022-01-25 DOI: 10.2307/j.ctv1n1bs5c.14
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引用次数: 0
期刊
Experiments of the Mind
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