5.2.2: Nested Model in Minitab
- Page ID
- 33640
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)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.
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.
Then choose Graphs to get the following dialog box, where "Interval plot for difference of means" should be checked.
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.
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.