An experimenter wished to study the effects of two types of incentives (factor A) on a person' to solve problems. The researcher also wanted to study two types of problems (factor B)-absti concrete problems. Twelve persons were randomly selected and assigned in equal numbers to incentive groups. The order of the two types of problems was then randomized independently person. The problem-solving ability scores follow (the higher the score, the greater the ability problems). 1. Problem Type Abstract (k= 1) Incentive Concrete Stimulus Subject (k= 2) i =1 i = 2 i= 3 i = 4 18 10 14 17 8 12 15 19 18 12 14 i=6 20 i = 1 i = 2 i = 3 16 19 22 20 24 35 32 37 33 39 32 j = 2 i=4 i=5 i = 6 21 Number of Observations Read 24 Number of Observations Used 24 The GLM Procedure Dependent Variable: score Source DF Sum of Squares Mean Square F Value Pr >F Model 13 1792.541667 137.887821 40.46 <.0001 Error 10 34.083333 3.408333 Corrected Total 23 1826.625000 Source DF Type I SS Mean Square F Value Pr >F Incentive 1 975.3750000 975.3750000 286.17 <.0001 subject(incentive) 10 148.7500000 14.8750000 4.36 0.0145 Problem type 1 513.3750000 513.3750000 150.62 <.0001 incentive*problem type 1 155.0416667 155.0416667 45.49 <.0001 Find the values for SSA, SSB, SSAB, SSS(A), SSB.S(A), and SSTO.
An experimenter wished to study the effects of two types of incentives (factor A) on a person' to solve problems. The researcher also wanted to study two types of problems (factor B)-absti concrete problems. Twelve persons were randomly selected and assigned in equal numbers to incentive groups. The order of the two types of problems was then randomized independently person. The problem-solving ability scores follow (the higher the score, the greater the ability problems). 1. Problem Type Abstract (k= 1) Incentive Concrete Stimulus Subject (k= 2) i =1 i = 2 i= 3 i = 4 18 10 14 17 8 12 15 19 18 12 14 i=6 20 i = 1 i = 2 i = 3 16 19 22 20 24 35 32 37 33 39 32 j = 2 i=4 i=5 i = 6 21 Number of Observations Read 24 Number of Observations Used 24 The GLM Procedure Dependent Variable: score Source DF Sum of Squares Mean Square F Value Pr >F Model 13 1792.541667 137.887821 40.46 <.0001 Error 10 34.083333 3.408333 Corrected Total 23 1826.625000 Source DF Type I SS Mean Square F Value Pr >F Incentive 1 975.3750000 975.3750000 286.17 <.0001 subject(incentive) 10 148.7500000 14.8750000 4.36 0.0145 Problem type 1 513.3750000 513.3750000 150.62 <.0001 incentive*problem type 1 155.0416667 155.0416667 45.49 <.0001 Find the values for SSA, SSB, SSAB, SSS(A), SSB.S(A), and SSTO.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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statistics ANOVA
Transcribed Image Text:1. An experimenter wished to study the effects of two types of incentives (factor A) on a person's ability
to solve problems. The researcher also wanted to study two types of problems (factor B)-abstract and
concrete problems. Twelve persons were randomly selected and assigned in equal numbers to the two
incentive groups. The order of the two types of problems was then randomized independently for each
person. The problem-solving ability scores follow (the higher the score, the greater the ability to solve
problems).
Problem Type
Incentive
Abstract
Concrete
Stimulus
Subject
(k= 1)
(k= 2)
i=1
i = 2
i= 3
i =4
10
18
19
18
12
14
17
8
i=5
12
14
i= 6
15
20
16
19
i=1
35
i = 2
i = 3
32
j = 2
22
37
i=4
j = 5
i = 6
20
24
33
39
32
21
Number of Observations Read 24
Number of Observations Used 24
The GLM Procedure
Dependent Variable: score
Source
DF Sum of Squares Mean Square F Value Pr>F
Model
13
1792.541667
137.887821
40.46 <.0001
Error
10
34.083333
3.408333
Corrected Total 23
1826.625000
Source
DF
Type I SS Mean Square F Value Pr>F
Incentive
1 975.3750000 975.3750000
286.17 <.0001
subject(incentive)
10 148.7500000
14.8750000
4.36 0.0145
Problem type
1 513.3750000
513.3750000
150.62 <.0001
incentive*problem type
1 155.0416667
155.0416667
45.49 <.0001
Find the values for SSA, SSB, SSAB, SSS(A), SSB.S(A), and SSTO.
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