Computing aspects of power for multiple regression
Computing aspects of power for multiple regression
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DOI：
10.3758/bf03206551
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发表时间：
2004-11-01
期刊：
影响因子：
--
通讯作者：
Myers, L
中科院分区：
文献类型：
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作者：
Dunlap, WP;Xin, X;Myers, L
在多元回归研究中，经验法则的力量比比皆是。大多数此类规则规定了必要的样本量，但它们仅基于预测变量的数量，通常忽略了准确计算功效所需的其他关键因素。其他在多元回归中使用幂的指南通常使用近似而非精确的方程来计算潜在的分布;需要复杂的预备计算;需要用表格表示格式进行插值;只能在Mathmartica或SAS等软件下运行，这些软件可能无法立即提供给用户;或者作为幂计算包的一部分出售给用户。相比之下，我们在此提供的程序可以立即免费下载，在Windows下运行，是交互式的，自我解释的，灵活的，以适应用户自己的回归问题，并作为单精度计算通常允许的准确性。
Rules of thumb for power in multiple regression research abound. Most such rules dictate the necessary sample size, but they are based only upon the number of predictor variables, usually ignoring other critical factors necessary to compute power accurately. Other guides to power in multiple regression typically use approximate rather than precise equations for the underlying distribution; entail complex preparatory computations; require interpolation with tabular presentation formats; run only under software such as Mathmatica or SAS that may not be immediately available to the user; or are sold to the user as parts of power computation packages. In contrast, the program we offer herein is immediately downloadable at no charge, runs under Windows, is interactive, self-explanatory, flexible to fit the user's own regression problems, and is as accurate as single precision computation ordinarily permits.