Skip to main content
Statistics LibreTexts

12.3, 12.5 The Regression Equation and Prediction

  • Page ID
    36529
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    Section 12.3, 12.5 The Regression Equation and Prediction

    Learning Objective:

    In this section, you will:

    • Calculate, interpret, and appropriately apply the regression line between two sets of data

    If a correlation exists between two variables, then a regression line (or regression equation) can be used to make predictions about those variables.

    The equation: 𝒚̂ = 𝒂 + 𝒃𝒙 describes the relationship between x and y. b is the slope of the line, and a is the y-intercept.

    You can find the regression line using the same F:LinRegTTest that we use to find r.

    Example 1a: A random sample of ten professional athletes produced the following data where x is the number of endorsements the player has and y is the amount of money made (in millions of dollars).

    x

    0

    3

    2

    1

    5

    5

    4

    3

    0

    4

    y

    2

    8

    7

    3

    13

    12

    9

    9

    3

    10

    Draw a scatter plot of the data and find the regression line and graph it on the same axes.

    Predictions: If we find that there is a linear correlation between the two variables, we can use the regression line to predict y-values for given x-values. If there is no linear correlation between the two variables, then the best prediction for y is the mean value, 𝒚̅

    Example 1b: What is the best estimate for a y-value corresponding to an x-value of 6?

    1

    Example 2: Concerns about global warming have led to studies of the relationship between global temperatures and the concentration of carbon dioxide. Listed below are the concentrations of carbon dioxide and temperatures for different years. Find the regression line. What is the best prediction for the global temperature when the carbon dioxide concentration is 320?

    Carbon Dioxide

    314

    317

    320

    326

    331

    339

    346

    354

    361

    369

    Temperature

    13.9

    14.0

    13.9

    14.1

    14.0

    14.3

    14.1

    14.5

    14.5

    14.4

    1. Calculator work
    2. Find a, b
    3. Find the regression line
    1. Find Critical Value and compare with r and state conclusion about linear correlation
    1. What is the best prediction for the global temperature when the carbon dioxide concentration is 320?

    Example 3: Listed below are brain sizes (in cm3) and IQ scores of subjects. Find the regression line. If a person has a brain size of 1020 cm3, find the best predicted IQ score for that person.

    Brain

    Size

    965

    1029

    1030

    1285

    1049

    1077

    1037

    1068

    1176

    1105

    1029

    1030

    IQ

    90

    85

    86

    102

    103

    97

    124

    125

    102

    114

    86

    87

    1. Calculator work
    2. Find a, b
    3. Find the regression line
    1. Find Critical Value and compare with r and state conclusion about linear correlation

    2

    1. If a person has a brain size of 1020 cm3, find the best predicted IQ score for that person.

    For more information and examples see online textbook OpenStax Introductory Statistics pages 685-691, 696-697.


    12.3, 12.5 The Regression Equation and Prediction is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?