Skip to main content
Statistics LibreTexts

5.1.2: Two-Factor Factorial - Greenhouse Example (Minitab)

  • Page ID
    33635
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    For Minitab, we also need to convert the data to a stacked format (Lesson 4 2 way Stacked Dataset). Once we do this, we will need to use a different set of commands to generate the ANOVA. We use...

    Stat > ANOVA > General Linear Model > Fit General Linear Model

    and get the following dialog box:

    General Linear Model pop-up window with "resp" in the Responses window, and "fert species" in the Factors window.
    Figure \(\PageIndex{1}\): General Linear Model pop-up window.

    Click on Model…, hold down the shift key and highlight both factors. Then click on the Add box to add the interaction to the model.

    General Linear Model: Model pop-up window with both "fert" and "species" selected in the Factors and covariates window, the value "2" selected in the interactions through order dropdown, and "fert*species" selected in the Terms in the model window.
    Figure \(\PageIndex{2}\): General Linear Model: Model pop-up window.

    These commands will produce the ANOVA results below which are similar to the output generated by SAS (shown in the previous section).

    Analysis of Variance

    Source DF Adj SS Adj MS F-value P-value
    fert 3 745.44 248.479 73.10 0.000
    species 1 236.74 236.741 69.65 0.000
    fert*species 3 50.58 16.861 4.96 0.005
    Error 40 135.97 3.399    
    Total 47 1168.73      

    Following the ANOVA run, you can generate the mean comparisons by

    Stat > ANOVA > General Linear Model > Comparisons

    Then specify the fert*species interaction term for the comparisons by checking the box.

    Comparisons pop-up window with "resp" selected in the Response dropdown, "Pairwise" selected in the Type of comparison dropdown, "Tukey" selected as the method and "fert*species" selected as the terms for comparison.
    Figure \(\PageIndex{3}\): Comparisons pop-up window.

    Then choose Graphs to get the following dialog box, where "Interval plot for difference of means" should be checked.

    Comparisons: Graphs pop-up window, with "Interval plot for differences of means" checked.
    Figure \(\PageIndex{4}\): Comparisons: Graphs pop-up window.

    The outputs are shown below.

    Grouping Information Using the Tukey Method and 95% Confidence

    fert species N Mean Grouping
    f3 SppB 6 37.0667 A
    f1 SppB 6 31.6167 B
    f2 SppB 6 30.0500 B
    f3 SppA 6 29.2000 B C
    f1 SppA 6 28.6000 B C
    f2 SppA 6 25.8667 C D
    control SppB 6 23.7000 D E
    control SppA 6 21.0000 E

    Means that do not share a letter are significantly different.

    Minitab Tukey Simultaneous 95% confidence intervals graph of differences of means for resp.
    Figure \(\PageIndex{5}\): Tukey simultaneous 95% confidence intervals.

    This page titled 5.1.2: Two-Factor Factorial - Greenhouse Example (Minitab) is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Penn State's Department of Statistics.

    • Was this article helpful?