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Interpreting Correlation and Regression

Interpreting Correlation and Regression. Section 4.2. Correlation and Regression. Describe only linear relationship. Strongly influenced by extremes in data. Always plot data first. Extrapolation – Use of regression line or curve outside the values of the domain of explanatory variable.

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Interpreting Correlation and Regression

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  1. Interpreting Correlation and Regression Section 4.2

  2. Correlation and Regression • Describe only linear relationship. • Strongly influenced by extremes in data. • Always plot data first. • Extrapolation – Use of regression line or curve outside the values of the domain of explanatory variable.

  3. Averaged Data • Correlations based on averages, not actual data, are usually too high. • Smooths out data. • Does not allow for scatter among individuals.

  4. Lurking Variables • Variables that influence the two studied variables but are not in the study. • Can falsely suggest a strong relationship. • Can hide a relationship. • Ex: Herbal tea in nursing homes/Ice cream and drowning.

  5. Association does not imply Causation • Strong associations ≠ cause/effect relation. • Causation – Change in x causes change in y. • Common Response – Both x and y respond to changes in some unobserved variable. • Confounding – The effect on y of x is mixed up with effects on y of other variables.

  6. Lurking Variables

  7. Experiments • Best way to get good evidence that x causes y. • Only x is changed, while lurking variables are controlled.

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