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Section 4.2

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

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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 drowning.

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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.

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Lurking Variables

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

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Chapter 4: More on Two- Variable Data. Correlation and Regression Describe only linear relationships Are not resistant One influential observation.

Chapter 4: More on Two- Variable Data. Correlation and Regression Describe only linear relationships Are not resistant One influential observation.

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