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相关概念视频

Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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用于单个案例实验设计的一般最小平方转换:介绍R包 lmeSCED.

Chendong Li1, Eunkyeng Baek2, Wen Luo1

  • 1Department of Educational Psychology, Texas A&M University, 718E Harrington Tower, College Station, TX, 77843-4225, USA.

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概括
此摘要是机器生成的。

可以改进单个案例实验设计 (SCED) 的多层次模型 (MLM). 一个新的R包,lmeSCED,解决了自相关性和小样本大小,提供了准确的固定效应推断和随机效应差异组件.

关键词:
自动相关性 自动相关性一个案例的实验设计.小样本调整小样本调整

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科学领域:

  • 行为科学和社会科学.
  • 统计建模 统计建模
  • 心理学 心理学 心理学

背景情况:

  • 单个案例实验设计 (SCED) 产生重复观察,适用于多层模型 (MLM).
  • SCED数据经常表现出自相关性和小样本大小,导致有偏见的标准错误和固定效应的膨胀的I型错误率.
  • 现有的统计软件在同时解决这些SCED数据挑战方面存在局限性.

研究的目的:

  • 评估一个两步统计方法,将自相关性和小样本的Satterthwaite调整相结合的一般最小方程 (GLS) 转换.
  • 为了实现这些方法,与随机效应的新型测试一起,在一个名为lmeSCED的用户友好的R包中实现.
  • 用蒙特卡洛模拟来评估这种方法的性能,并证明其实际应用.

主要方法:

  • 一种两步方法,涉及GLS转换来处理AR(1) 余量和Satterthwaite对固定效应推断的调整.
  • 开发 lmeSCED R 软件包,包括边界校正的限制性概率测试和随机效应的参数启动.
  • 蒙特卡洛模拟研究评估参数恢复和I型错误率.

主要成果:

  • 将MLM应用于GLS转换的SCED数据导致了无偏差的参数恢复.
  • 提出的方法保持了I型错误率在名义水平.
  • lmeSCED包有效地处理SCED数据中的自相关性和小样本大小.

结论:

  • 评估的两步方法为使用多层模型分析SCED数据提供了统计学上合理的方法.
  • lmeSCED R包为研究人员提供了一个实用和有效的工具,提高了SCED数据分析的可靠性.
  • 这项工作解决了SCED数据现有统计方法的关键局限性,为更准确的研究结果铺平了道路.