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5.2.2: Nested Model in Minitab

  • Page ID
    33640
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    In Minitab, for the following (Nested Example Data):

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

    Enter the factors 'Region' and 'City' in the Factors box, then click on Random/Nest...Here is where we specify the nested effect of City in Region.

    Minitab General Linear Model pop-up window, with 'Ex_hours' in the Responses window and "Region" and "City" in the Factors window.
    Figure \(\PageIndex{1}\): General Linear Model pop-up window.
    General Linear Model: Random Nest pop-up window, with "Region" entered in the City window and the Fixed factor type selected for both Region and City.
    Figure \(\PageIndex{2}\): Random Nest pop-up window.

    The output is shown below.

    Factor Information

    General Linear Model: response versus School, Instructor

    Factor Type Levels Values
    Region Fixed 2 1,2
    City(Region) Fixed 6 Atlanta(1), Chicago(1), SanFran(1), Atlanta(2), Chicago(2), SanFran(2)

    Analysis of Variance

    Source DF Adj SS Adj MS F P
    Region 1 108.00 108.000 15.43 0.008
    City(Region) 4 616.00 154.000 22.00 0.001
    Error 6 42.00 7.000    
    Total 11 766.00      

    Model Summary

    S R-sq R-sq(adj) R-sq(pred)
    2.64575 94.52% 89.95% 78.07%

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

    Stat > ANOVA > General Linear Model > Comparisons

    Then specify "Region" and "City(Region)" for the comparisons by checking the boxes.

    Comparisons pop-up window with "Ex_hours" selected in the Response dropdown, "Pairwise" selected in the Type of comparison dropdown, "Tukey" selected for the Method and "Region" and "City(Region)" selected in the "choose terms for comparisons" section.
    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.

    Checking the box for "Interval plot for differences of means" in the Comparisons: Graphs pop-up window.
    Figure \(\PageIndex{4}\): Comparisons: Graphs pop-up window.

    The outputs are as follows.

    Comparison for Ex_hours

    Tukey Pairwise Comparisons: Region

    Grouping Information Using Tukey Method and 95% Confidence

    Region N Mean Grouping
    1 6 18 A
    2 6 12 B

    Means that do not share a letter are significantly different.

    Minitab Tukey Simultaneous 95% CIs Differences of Means for Ex_Hours graph
    Figure \(\PageIndex{5}\): Tukey simultaneous 95% CIs differences of means graph for Ex_hours, by Region.

    Tukey Pairwise Comparisons: (City)Region

    Grouping Information Using Tukey Method and 95% Confidence

    City(Region) N Mean Grouping
    Atlanta (1) 2 27.0 A
    Chicago(2) 2 20.0 A B
    SanFran(1) 2 18.5 A B C
    Atlanta(2) 2 12.5 B C D
    Chicago(1) 2 8.5 C D
    SanFran(2) 2 3.5 D

    Means that do not share a letter are significantly different.

    Minitab Tukey Simultaneous 95% CIs Differences of Means for Ex_hours graph
    Figure \(\PageIndex{6}\): Tukey simultaneous 95% CIs differences of means graph for Ex_hours, by City(Region).

    This page titled 5.2.2: Nested Model in 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.

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