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Understanding correlation is crucial in statistics as it measures how two variables move together. A correlation coefficient, denoted as 'r', quantifies this relationship's strength and direction, ranging from -1 to +1. Positive values indicate a direct relationship, while negative values suggest an inverse one. This concept is vital in research for identifying relationships between variables, though it does not imply causation. The calculation involves a formula considering the covariance and standard deviations of the variables.
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Correlation is a statistical measure that describes the extent to which two variables fluctuate together
Positive Correlation
A positive correlation indicates that as one variable increases, the other tends to increase as well
Negative Correlation
A negative correlation means that as one variable increases, the other tends to decrease
Correlation does not establish causation; it merely suggests a possible association
Correlational research involves observing but not manipulating variables
Correlational research contrasts with experimental research, which can determine causation by introducing controlled changes to the variables
Correlational research allows researchers to identify and quantify the strength of relationships between variables
The correlation coefficient, symbolized by "r," quantifies the degree and direction of a linear relationship between two variables
The value of the correlation coefficient ranges from -1 to +1, with +1 indicating a perfect positive linear correlation, -1 indicating a perfect negative linear correlation, and 0 signifying no linear correlation
The correlation coefficient measures the strength and direction of the linear relationship, while the p-value assesses the probability that the observed correlation occurred by chance
The Pearson correlation coefficient is calculated using a formula that involves the number of pairs of scores, the sum across all pairs, and the individual scores on the two variables
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Definition of correlation
Statistical measure of how two variables move together.
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Positive vs negative correlation
Positive: variables increase together. Negative: one increases, other decreases.
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Correlational vs experimental research
Correlational: observes variables without manipulation. Experimental: manipulates variables to establish causation.
