Correlation

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What is correlation in statistics?

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Correlation is a statistical measure that describes the extent to which two variables change together. It indicates the strength and direction of a linear relationship between the variables.

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What are the types of correlation?

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The main types of correlation are positive correlation, negative correlation, and no correlation. Positive correlation indicates that as one variable increases, the other also increases. Negative correlation indicates that as one variable increases, the other decreases. No correlation indicates no relationship between the variables.

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What does a positive correlation indicate?

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A positive correlation indicates that as one variable increases, the other variable also tends to increase.

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What does a negative correlation indicate?

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A negative correlation indicates that as one variable increases, the other variable tends to decrease.

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What does 'no correlation' mean?

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'No correlation' means that there is no discernible relationship between the changes in two variables.

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How is correlation measured?

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Correlation is typically measured using the Pearson correlation coefficient, which ranges from -1 to +1. A value of +1 indicates perfect positive correlation, -1 indicates perfect negative correlation, and 0 indicates no correlation.

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What is the range of the Pearson correlation coefficient?

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The range of the Pearson correlation coefficient is from -1 to +1.

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What is the difference between correlation and causation?

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Correlation indicates a relationship between two variables, while causation indicates that one variable directly affects the other. Correlation does not imply causation.

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What is an example of a positive correlation?

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An example of a positive correlation is the relationship between height and weight; generally, as height increases, weight tends to increase as well.

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What is an example of a negative correlation?

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An example of a negative correlation is the relationship between the amount of time spent studying and the number of errors made on a test; as study time increases, errors tend to decrease.