Skip to main content
Statistics LibreTexts

3.3: Introduction to Assessing the Fit of a Line

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)

    What you’ll learn to do: Use residuals, standard error, and r2 to assess the fit of a linear model.

    This graph shows a sample relationship between height and weight of people as a residual plot.Graphing the regression line with the scatterplot gives a visual depiction of how well the regression line fits the data. To further hone in on assessing the fit of our regression line to the data, in this section we present:

    • Residual plots.
    • The correlation coefficient r gives us a numerical way to measure this fit.
    • Interpreting the square of the correlation coefficient r2 .
    • Interpreting the standard error se.

    Contributors and Attributions

    CC licensed content, Shared previously

    3.3: Introduction to Assessing the Fit of a Line is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?