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相关概念视频

Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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Correlations02:20

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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解释相关系数的增益概率方法:一个教程教程

David Trafimow1

  • 1Department of Psychology, New Mexico State University.

Psychological methods
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概括
此摘要是机器生成的。

这项研究引入了一种用于解释相关系数的新方法,避免因二分化变量而导致的数据丢失. 新方法估计了概率的优点和缺点,提高了理论的具体性.

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科学领域:

  • 心理学 心理学 心理学
  • 统计 统计 统计 统计

背景情况:

  • 解释相关系数是复杂的,现有的方法如确定系数和二项式效应大小显示有局限性.
  • 确定系数 (r2) 量化了解释的方差,但可能会被误解.
  • 双项效果大小显示 (BESD) 需要二分化连续变量,导致信息丢失.

研究的目的:

  • 提出一种新的,基于教程的方法来解释相关系数.
  • 估计相关系数所暗示的概率优势和缺点.
  • 引入增益概率图作为一种新的解释工具.

主要方法:

  • 拟议的方法估计了相关系数的概率 (缺点).
  • 它涉及构建增益概率图.
  • 最重要的是,它避免了连续依赖变量的二分化,从而保留了信息.

主要成果:

  • 新程序为相关系数提供了第三种解释方法.
  • 它不涉及连续变量的二分化,从而防止信息丢失.
  • 该方法可以更轻松地对相关系数进行细微的比较,从而提高理论的具体性.

结论:

  • 介绍了一种新的,保存信息的方法来解释相关系数.
  • 这种方法通过允许微妙的比较来增强理论的具体性.
  • 增益概率图为理解相关关系的概率影响提供了有价值的工具.