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Lecture 7

Lecture 7 (week 9)

14 Pages
165 Views

Department
Quantitative Methods
Course Code
QMS 202
Professor
Clare Chua

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3/10/2011
1
Lecture 10
Chi-Square Tests
Hypothesis Test
Two Population
Hypothesis Test
More than
Two Populations
Sample
Independent Sample
dependent
c MEANSc Proportions
Check the
conditions
If yes
Levene’s test
Equal Variance unequal Variance
STOP
Chi-square
Test
Non-parametric test
One-Way ANOVA
Post-hoc
(SPSS output)
ANOVA with SPSS
To perform ANOVA
www.notesolution.com
3/10/2011
2
Select “Post Hoc”
to perform “Multiple Comparisons-
Tukey Procedure
SelectTukey
Click continue
ClickOK” ANOVA
Breaking Strength
Sumof
Mean
SPSS OUTPUT
Sum
of
Squares df
Mean
Square FSig.
Between
Groups 329.389 2 164.694 11.366 .000
Within
Groups 478.167 33 14.490
Total807.556 35
www.notesolution.com
3/10/2011
3
Multiple Comparisons
Dependent Variable: Breaking Strength
Tukey HSD
(I) Machines (J) Machines
Mean
Differen
ce (I-J)
Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
1.00 2.00 7.333(*) 1.554 .000 3.52 11.15
3.00 4.583
(
*
)
1.554 .016 .77 8.40
()
2.00 1.00 -
7.333(*) 1.554 .000 -11.15 -3.52
3.00 -2.750 1.554 .195 -6.56 1.06
3.00 1.00 -
4.583(*) 1.554 .016 -8.40 -.77
2.00 2.750 1.554 .195 -1.06 6.56
* The mean difference is significant at the .05 level.
Breaking Strength
Tukey HSD
Machines N
Subset for alpha = .05
12
2.00 12 105.75
3.00 12 108.50
1.00 12 113.08
Sig. .195 1.000
Means for groups in homogeneous subsets are displayed.
a Uses Harmonic Mean Sample Size = 12.000.
Multiple Comparisons
Dependent Variable: Breaking Strength
Tukey HSD
(I) Machines (J) Machines
Mean
Differen
ce (I-J)
Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
1.00 2.00 7.333(*) 1.554 .000 3.52 11.15
3.00 4.583
(
*
)
1.554 .016 .77 8.40
How do we change the
Labelling?
()
2.00 1.00 -
7.333(*) 1.554 .000 -11.15 -3.52
3.00 -2.750 1.554 .195 -6.56 1.06
3.00 1.00 -
4.583(*) 1.554 .016 -8.40 -.77
2.00 2.750 1.554 .195 -1.06 6.56
* The mean difference is significant at the .05 level.
Selectvalues”
Go to variable view
Hypothesis Test
Two Population
Hypothesis Test
More than
Two Populations
Sample
Independent Sample
dependent
c MEANSc Proportions
Check the
conditions
If yes
Levene’s test
Equal Variance unequal Variance
STOP
Chi-square
Test
Non-parametric test
One-Way ANOVA
Post-hoc
(SPSS output)
Chi-Square Tests
A. Goodness-of-fit Tests
B. Contingency Analysis
1. 2 x 2 Contingency table
2. 2 x c contingency table: Chi-Square Test
for the Differences Among More Than
Two Proportions.
3. r x c contingency table: Chi-Square Test
of Independence
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Description
3102011 Hypothesis Test Hypothesis Test More than Two Population Two Populations c MEANS c Proportions Sample Independent Sample Check the dependent conditions Lecture 10 Levenes test If yes Equal Variance unequal Variance Chi-square Test Chi-Square Tests STOP One-Way ANOVA Non-parametric test Post-hoc (SPSS output) ANOVA with SPSS To perform ANOVA 1 www.notesolution.com 3102011 Select Tukey Select Post Hoc Click continue to perform Multiple Comparisons- Tukey Procedure SPSS OUTPUT ANOVA Click OK Breaking Strength Sum off Meean Squares df Square F Sig. Between Groups 329.389 2 164.694 11.366 .000 Within Groups 478.167 33 14.490 Total 807.556 35 2 www.notesolution.com
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