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4: Correlation and Regression

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    44356
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    • 4.1: Correlation
      Correlation as a means of measuring the relationship between variables. Subsections cover how to predict correlation from scatterplots of data, and how to perform a hypothesis test to determine if there is a statistically significant correlation between the independent and the dependent variables.
    • 4.2: Scatterplots
      A scatterplot shows the relationship between two quantitative variables measured on the same individuals. The predictor variable is labeled on the horizontal or x -axis and  the response variable is labeled on the vertical or y -axis.
    • 4.3: Simple Linear Regression
      A linear regression is a straight line that describes how the values of a response variable \(y\) change as the predictor variable \(x\) changes. It should only be performed if you observe visually that there is a linear pattern in the scatterplot and that there is a statistically significant correlation between the independent and dependent variables. Subsections discuss residuals and the coefficient of determination, how to use the least squares equation for prediction, and how to identify out
    • 4.4: Residuals
    • 4.5: Coefficient of Determination
    • 4.6: Chapter 4 Formulas
    • 4.7: Chapter 4 Exercises


    This page titled 4: Correlation and Regression is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Rachel Webb via source content that was edited to the style and standards of the LibreTexts platform.

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