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Template question. The student is asked to perform a two factor ANOVA to test the null hypotheses that the measurement does not depend on each of the factors, and that there is no interaction between the factors.
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England schools
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England university
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Scotland schools
Taxonomy: mathcentre
Taxonomy: Kind of activity
Taxonomy: Context
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said | Ready to use | 8 years, 9 months ago |
History
Christian Lawson-Perfect 8 years, 9 months ago
Gave some feedback: Ready to use
James Eustace 9 years, 3 months ago
Created this as a copy of Two factor ANOVA.Name | Status | Author | Last Modified | |
---|---|---|---|---|
Two factor ANOVA | Ready to use | Christian Lawson-Perfect | 01/06/2016 11:08 | |
José's copy of Two factor ANOVA | Ready to use | Jos Klenner | 02/12/2020 15:42 | |
Julie's copy of Two factor ANOVA | Ready to use | Julie Crowley | 26/07/2016 12:53 | |
Ricardo's copy of Two factor ANOVA | Ready to use | Ricardo Monge | 01/06/2016 09:47 | |
James's copy of Two factor ANOVA | Ready to use | James Eustace | 01/06/2016 09:47 | |
YJ's copy of Two factor ANOVA | Ready to use | YJ Kim | 01/06/2016 09:47 | |
Andrew's copy of Two factor ANOVA | Ready to use | Andrew Dunbar | 01/06/2016 09:47 | |
Christoph's copy of Two factor ANOVA | Ready to use | Christoph Tiedtke | 01/06/2016 09:47 | |
Two factor ANOVA - SES | Has some problems | Chris Graham | 10/12/2020 00:31 | |
Two factor ANOVA - Mock Test - SES2003 | Needs to be tested | Chris Graham | 25/02/2019 11:02 | |
Two factor ANOVA | draft | Xiaodan Leng | 11/07/2019 05:48 | |
Two factor ANOVA | draft | Xiaodan Leng | 11/07/2019 06:27 |
There are 4 other versions that do you not have access to.
Name | Type | Generated Value |
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measurement_name | string |
GSR
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factor1_name | string |
personality
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factor1_levels | list |
[ "normal", "antisocial" ]
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factor2_name | string |
stimulus
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factor2_levels | list |
[ "baseline", "stress" ]
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mu1 | number |
22.5
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||||
mu2_diff | number |
4.8
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||||
mu3 | number |
22.5
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||||
mu4_diff | number |
1.4
|
||||
sig1 | number |
2.8
|
||||
sig2 | number |
1.6
|
||||
num_samples | integer |
10
|
Name | Type | Generated Value |
---|
r1 | list |
List of 10 items
|
||||
r2 | list |
List of 10 items
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||||
r3 | list |
List of 10 items
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||||
r4 | list |
List of 10 items
|
Name | Type | Generated Value |
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n | integer |
40
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||||
f1btss | number |
42.03
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||||
f1vr | number |
0.09
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||||
f2btss | number |
198.03
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||||
f2vr | number |
0.41
|
||||
ivr | number |
-35.44
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||||
rss | number |
17526.30
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||||
mrs | number |
486.84
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||||
mean1 | number |
22.30
|
||||
mean2 | number |
17.00
|
||||
mean3 | number |
23.50
|
||||
mean4 | number |
19.90
|
Name | Type | Generated Value |
---|
w1 | list |
[ 0, 0, 0, 0, 1 ]
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||||
w2 | list |
[ 0, 0, 0, 0, 1 ]
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||||
w0 | list |
[ 0, 0, 0, 0, 1 ]
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f1l2 | list |
List of 20 items
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f1l1 | list |
List of 20 items
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f1ssq | list |
[ 7961, 9648 ]
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tol | number |
0.001
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btss | number |
-17015.52
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tss | number |
510.78
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||||
f1t | list |
[ 393, 434 ]
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w | list |
Nested 3×5 list
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f2ssq | list |
[ 10642, 6967 ]
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f2t | list |
[ 458, 369 ]
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ssq | list |
[ 5055, 2906, 5587, 4061 ]
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vvr | list |
[ 0.09, 0.41, -35.44 ]
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f1ss | number |
17609
|
||||
interactionss | number |
-17255.58
|
||||
tsqovern | list |
[ 22.3, 17, 23.5, 19.9 ]
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||||
mu4 | number |
21.1
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||||
g | number |
827
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||||
mu2 | number |
17.7
|
||||
ss | number |
17609
|
||||
f1tsqovern | list |
[ 7722.45, 9417.8 ]
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||||
f1rss | number |
468.75
|
||||
f2l1 | list |
List of 20 items
|
||||
f2l2 | list |
List of 20 items
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||||
stovern | number |
82.7
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||||
t | list |
[ 223, 170, 235, 199 ]
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||||
f1stovern | number |
17140.25
|
||||
f2ss | number |
17609
|
||||
f2stovern | number |
17296.25
|
Generated value: string
- Statement
- Advice
- "Part b)" → "Unnamed gap" - Choice text
- "Part b)" → "Unnamed gap" - Choice text
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- Part b) Gap-fill
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Ask the student a question, and give any hints about how they should answer this part.
Now complete the following two factor ANOVA table from this data. Input the $\mathit{SS}$, $\mathit{MS}$ and $\mathit{VR}$ data to 2 decimal places.
Source | df | SS | MS | VR |
---|---|---|---|---|
{capitalise(factor1_name)} | ||||
{capitalise(factor2_name)} | ||||
Interaction | ||||
Residual | - | |||
Total | - | - |
The Calculations
- Residual Calculations. When you are calculating $\mathit{TSS}$ and $\mathit{BTSS}$, round them both to 2 decimal places, then calculate $\mathit{RSS}$ by taking away these rounded values. The $\mathit{RMS}$ value is then obtained by dividing this $\mathit{RSS}$ value by the residual degrees of freedom.
- For {factor1_name}, {factor2_name} and Interaction, calculate the estimations of their variances to 2 decimal places as well. These values go in the $\mathit{MS}$ column.
- The $\mathit{VR}$ values are obtained by dividing the first three values in the $\mathit{MS}$ column by the $\mathit{RMS}$ value. Enter the $\mathit{VR}$ values to 2 decimal places in the last column.
Also input the mean values of the factors at their various levels:
$\overline{x_i}$ | |
---|---|
{capitalise(factor1_levels[0])}, {factor2_levels[0]} | |
{capitalise(factor1_levels[0])}, {factor2_levels[1]} | |
{capitalise(factor1_levels[1])}, {factor2_levels[0]} | |
{capitalise(factor1_levels[1])}, {factor2_levels[1]} |
Use this tab to check that this question works as expected.
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