SAS Results for Problem 2Factor (IV) in ANOVA 2 levels (1 or.docx

SAS Results for Problem 2:
Factor (IV) in ANOVA:
2 levels (1 or 2)
The ANOVA Procedure
Class Level Information
Class Levels Values
PERLEVEL 2 1 2
Number of Observations Read 80
Number of Observations Used 80
1st Dependent Variable (DV) analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV1 for two IV
levels
Dependent Variable: EFFORT
Sum of
Source DF Squares Mean Square F
Value Pr > F
Model 1 22.8703938 22.8703938
15.74 0.0002
Error 78 113.3171062 1.4527834
Corrected Total 79 136.1875000
R-Square Coeff Var Root MSE EFFORT
Mean

0.167933 28.78363 1.205315
4.187500
Source DF Anova SS Mean Square F
Value Pr > F
PERLEVEL 1 22.87039385 22.87039385
15.74 0.0002
2nd DV analyzed
The ANOVA Procedure
levels
Dependent Variable: UND
Sum of
Value Pr > F
Model 1 9.3002113 9.3002113 3.92
0.0513
Error 78 185.1872887 2.3741960
R-Square Coeff Var Root MSE UND
Mean
0.047819 36.36207 1.540843
4.237500
Value Pr > F
PERLEVEL 1 9.30021129 9.30021129
3.92 0.0513

Note: Good strategy is conduct univariate ANOVA F-tests at a
small α (e.g., α = .01 or .02)
The ANOVA Procedure
levels
3rd DV analyzed
Dependent Variable: QUALITY
Sum of
Value Pr > F
Model 1 7.6597701 7.6597701 5.45
0.0221
Error 78 109.5402299 1.4043619
R-Square Coeff Var Root MSE
QUALITY Mean
0.065356 28.90385 1.185058
4.100000
Value Pr > F
PERLEVEL 1 7.65977011 7.65977011
5.45 0.0221
4th DV analyzed
The ANOVA Procedure
levels

Dependent Variable: LUCK
Sum of
Value Pr > F
Model 1 10.5261663 10.5261663
3.64 0.0599
Error 78 225.2738337 2.8881261
R-Square Coeff Var Root MSE LUCK
Mean
0.044640 37.35052 1.699449
4.550000
Value Pr > F
PERLEVEL 1 10.52616633 10.52616633
3.64 0.0599
5th DV analyzed
The ANOVA Procedure
levels
Dependent Variable: DIFF
Sum of
Value Pr > F
Model 1 4.3206981 4.3206981 2.50
0.1180

Error 78 134.8668019 1.7290616
R-Square Coeff Var Root MSE DIFF
Mean
0.031042 39.69624 1.314938
3.312500
Value Pr > F
PERLEVEL 1 4.32069811 4.32069811
2.50 0.1180
Multivariate Analysis of Variance
p-values for MANOVA test of Ho: no mean vector differences
between the 2 PERLEEVEL groups
MANOVA Test Criteria and Exact F Statistics
for the Hypothesis of No Overall PERLEVEL
Effect
H = Anova SSCP Matrix for PERLEVEL E = Error
SSCP Matrix
S=1 M=1.5 N=36
Statistic Value F Value Num DF Den DF
Pr > F
Wilks' Lambda 0.67500180 7.13 5 74
<.0001
Pillai's Trace 0.32499820 7.13 5 74
<.0001
Hotelling-Lawley Trace 0.48147752 7.13 5
74 <.0001

Roy's Greatest Root 0.48147752 7.13 5 74
<.0001
Levene's Test for Homogeneity of EFFORT
Variance
Sum of Mean
Source DF Squares Square F Value
Pr > F
PERLEVEL 1 1.0414 1.0414 0.30
0.5832
Error 78 267.5 3.4300
p-value for testing equality of variances in DV for 2
PERLEVEL groups
(Fail to reject Ho implies equality of variances)
Levene's Test for Homogeneity of UND
Variance
DV analyzed
Sum of Mean
Pr > F
PERLEVEL 1 12.2948 12.2948 1.51
0.2235
Error 78 636.9 8.1650
Levene's Test for Homogeneity of QUALITY
Variance
Sum of Mean
Pr > F

PERLEVEL 1 11.3703 11.3703 3.51
0.0646
Error 78 252.3 3.2350
Levene's Test for Homogeneity of LUCK
Variance
Sum of Mean
Pr > F
PERLEVEL 1 8.0512 8.0512 0.63
0.4285
Error 78 991.5 12.7112
Levene's Test for Homogeneity of DIFF
Variance
Sum of Mean
Pr > F
PERLEVEL 1 3.4087 3.4087 0.89
0.3477
Error 78 297.9 3.8188
The ANOVA Procedure
1st DV analyzed
Bonferroni (Dunn) t Tests for EFFORT
NOTE: This test controls the Type I experimentwise
error rate.
Alpha 0.05
Error Degrees of Freedom 78
Error Mean Square 1.452783

Critical Value of t 1.99085
Minimum Significant Difference 0.5581
Harmonic Mean of Cell Sizes 36.975
NOTE: Cell sizes are not equal.
Means with the same letter are not significantly
different.
Ranking of DV means (means with different letters are
significantly different)
Bon Grouping Mean N PERLEVEL
A 4.8966 29 2
B 3.7843 51 1
Bonferroni (Dunn) t Tests for UND
error rate.
2nd DV analyzed
Alpha 0.05

different.
Ranking of DV means (means with the same letter are not
significantly different)
A 4.6897 29 2
A
A 3.9804 51 1
Bonferroni (Dunn) t Tests for QUALITY
3rd DV analyzed
error rate.
Alpha 0.05
different.
A 4.3333 51 1

B 3.6897 29 2
4th DV analyzed
Bonferroni (Dunn) t Tests for LUCK
error rate.
Alpha 0.05
different.
A 4.8235 51 1
A
A 4.0690 29 2
Bonferroni (Dunn) t Tests for DIFF
5th DV analyzed

error rate.
Alpha 0.05
different.
A 3.6207 29 2
A
A 3.1373 51 1
SOPHOMORE - PRACTICING
A - level
Written Communication
B - level
C - level

D - level
F - level
Using language (grammar, syntax,
mechanics) with almost no errors, the
work is very well organized and very
clearly communicates meaning while
using good, relevant, and compelling
content and sources to illustrate good
understanding of the subject. The work
well addresses the context, audience,
and purpose of the assignment.
mechanics) with minimal errors, the
work is well organized and very clearly
communicates meaning while using
appropriate, relevant, and compelling
content and sources to illustrate good
understanding of the subject. The work
addresses the context, audience, and
purpose of the assignment.
mechanics) that may have some
errors, but overall the work is mostly
organized and clearly communicates
meaning while using appropriate and
relevant content and sources to
illustrate general understanding of the

subject. The work begins to address
the context, audience and purpose of
the assignment.
mechanics) that has many errors and
some vague areas, the work is not
organized and does not communicate
clear meaning. Content and sources
are not very appropriate nor relevant
to illustrate general understanding of
the subject. The work does not
satisfactorily address the context,
audience and purpose of the
assignment.
mechanics) that has too many errors
and vague areas, the work is poorly
organized and fails to communicate a
clear meaning. Content and sources
are neither appropriate nor relevant
to illustrate any understanding of the
subject. The work fails to address the
context, audience and purpose of the
assignment.
A - level
Critical Thinking
B - level
Critical Thinking

C - level
Critical Thinking
D - level
Critical Thinking
F - level
Critical Thinking
Issue/problem to be considered
critically is very clear. Ideas are taken
from quality sources with very good
interpretation/evaluation and
questioning. Distinguishes between
key assumptions and contexts,
addresses multiple viewpoints, and
clearly integrates own and others’
points with very sound reasoning.
critically is clear. Ideas are taken from
quality sources with good
interpretation/evaluation and
questioning. Generally distinguishes
between key assumptions and
contexts, addresses some multiple
viewpoints, and generally integrates
own and others’ points with sound
reasoning.
critically is generally clear. Ideas are
taken from quality sources with
adequate interpretation/ evaluation

and adequate questioning. Identifies
key assumptions and contexts, begins
to address multiple viewpoints, and
begins to integrate own and others’
points with basically sound reasoning.
critically is only minimally clear. Ideas
are taken from quality sources with
minimal interpretation/ evaluation
and questioning. The work does not
identify key assumptions and contexts
nor does it address multiple
viewpoints. The approach does
not integrate own and others’ points
and the reasoning is somewhat faulty.
critically is not clear and/or very
ambiguous. Ideas are taken from poor
or sources with virtually no
interpretation/ evaluation and
questioning. The work does not
identify key any assumptions and
contexts nor does it address multiple
viewpoints. The approach does
not integrate own and others’ points
and the reasoning is faulty.
A - level
Information Literacy
B - level

C - level
D - level
F - level
Information literacy skills are
demonstrated through at least TWO
evaluative annotations for books or
substantial articles (preferably peer
reviewed) found through the Otis
Library databases.
Annotations must include:
1. author credentials
2. a description of the type of source
(audience)
3. a discussion about
purpose/bias/point of view
4. a discussion of currency of the
source
5. an explanation about why the source
is relevant to the project/paper.
A works cited page is required and
must include at least 5 QUALITY
sources that are incorporated into
paper/project.
Information literacy skills are

Library databases.
(audience)
3. a discussion about
purpose/bias/point of view
is relevant to the project/paper.
paper/project.
Information literacy competency is
Library databases.
(audience)
is relevant to the project/paper

paper/project.
Any of the following:
- inferior or missing annotations
- author credentials missing
- type of source/audience missing
- relevance is missing
- works cited page missing
- fewer than 3 QUALITY sources
- too many free web sources or low-
quality, inadequate or inferior sources
are used
- sources not incorporated in paper
_______________________________
A QUALITY source is not necessarily
the first or easiest found. Should be
peer-reviewed or substantial material
that considers multiple points of view
and relies on evidence, logic, and
research to create a context. It has at
least one named author and is up to
date.
Any of the following:
- inferior or no annotations
- no works cited page
- no QUALITY sources
- only free web sources or low-quality,
inadequate or inferior sources are used
- sources not incorporated in paper
Signature Assignment

“In the long history of humankind (and animal kind, too) those
who learned to collaborate and
improvise most effectively have prevailed.”
Charles Darwin, The Origin of Species
For your Signature Assignment, you’ll be applying improv
techniques to a particular problem.
The problem can be personal, political, environmental, social,
or a variety of the above.
Diagnose the problem and decide how various improvisational
skills can be used to better the
situation. This 1500-word essay will be part narrative
description, part research, and part
analysis.
Example problems to diagnose:
How could a particular struggle in your art or design practice be
helped by the philosophy of
‘yes, and…’?
How can establishing the ‘who, what, and where?’ help abate
the proliferation of fake news?
How can ‘facing the facts’ foster more community on campus?
In activist groups?
Research: While this essay will have a narrative component,
detailing your own experiences

with improv, this is also a research paper. You will need to
include four sources and two
annotations. If you decide to write about a topic from your
personal life, be sure you understand
the greater thematic elements at play so you can find relevant
sources and include multiple
points of view.
For instance, if you are writing about your perfectionism in
studio, you can research how
psychology, philosophy or world religions have grappled with
this common problem. You could
also research specific topics in your major. How do improv
techniques align or conflict with your
discipline?
Those of you choosing a political or social issue will perhaps
have an easier time coming up
with research topics, but remember that you must include your
own experiences with
improvisation and explain how they apply, or could be applied,
to this issue. You may also need
to write how improvisation does not serve your paper topic.
Below is a review of some of the concepts* we practiced this
term:
Yes, and...

Giving gifts
Accepting reality/Facing the Facts
Establishing the Who, What, & Where
The 'If blank is true, then what else is true' pattern
Labeling broad and playing specific
Listening instead of planning
Taking risks
Surrendering your own ideas for the group
Clunky and clear is better than clever and vague
Taking care of each other
Status in relationships
*These are not all of the techniques we explored. If you think of
another improv technique and
want to integrate it into your essay – please do!
Assignment Requirements:
Minimum of 6 pages/1500 words.
Use at least 4 quality sources and integrate these sources into
your final paper.

Write 2 annotations, as outlined in the Source Worksheet
<doc>.
Rough Draft for Peer Review.
2nd Draft emailed to Instructor ([email protected])
Students will post their final draft on their Learning Portfolio
for this class and "submit" the final
draft as "Signature Assignment". There must be an element of
document design - proper
formatting and integration of media.
Please see the Signature Assignment Rubric <pdf>
Final Pointers:
-If appropriate, feel free to use the articles we read in class as
sources for your research.
-Be sure you chose a particular issue, struggle, or experience.
Don’t generalize
For example:
Being a product designer is like saying ‘yes, and...’ to life =
general
On my internship, I was what we call ‘blocking’ in improv - not
participating, disagreeing, and
looking at the clock. Then, I said yes to my colleague’s
suggestion. I was irritated at first, but it
turns out his wood and earthy designs for the flatware were
much more aligned with the
aesthetic the client was looking for. = specific
SAS Results for Problem 3:

SAS output for Stepwise Discriminant Analysis (only useful
when trying to select best subset of IVs from a long list of
potential IVs)
STEPWISE DISCRIMINANT ANALYSIS
The STEPDISC Procedure
The Method for Selecting Variables is STEPWISE
Total Sample Size 569 Variable(s) in the
Analysis 14
Class Levels 2 Variable(s) Will Be Included
0
Significance Level to Enter
0.15
Significance Level to Stay
0.15
DV
(2 levels)
Variable
ADOPT Name Frequency Weight
Proportion
0 _0 468 468.0000 0.822496
p-values: Ho tested is no difference between means of IV for 2
Adopt status levels, using all other IVs as covariates
1 _1 101 101.0000 0.177504

IVs selected by stepwise routine
The STEPDISC Procedure
Stepwise Selection Summary
Number Partial
Step In Entered Removed Label R-
Square F Value Pr > F
1 1 NUMIT 0.0747
45.77 <.0001
2 2 REVDUM 0.0306
17.84 <.0001
3 3 CHLEADER My company is obligated to do as
0.0324 18.91 <.0001
4 4 P2HDUM 0.0310
18.04 <.0001
5 5 WMLDUM 0.0162
9.26 0.0025
6 6 P2LDUM 0.0129
7.34 0.0069
7 7 QUALITY Product quality
0.0076 4.28 0.0390
8 8 SERVICE My firm feels my channel or supply
0.0055 3.08 0.0796
9 9 FIRMDUM 0.0050
2.81 0.0942
10 10 WMHDUM 0.0054
3.04 0.0820
11 9 WMLDUM 0.0033
1.83 0.1763
12 10 BRHDUM 0.0056
3.16 0.0759
13 11 WMLDUM 0.0041
2.32 0.1285

Note: With 14 potential IVs, Step 1 conducts 14 ANCOVAs,
Step 2 conducts 13 ANCOVAs, Step 3 conducts 12 ANCOVAs,
etc. At each step, IV with smallest p-value is selected
DA function includes the 1st 7 IVs selected by Stepwise DA
SAS output for Discriminant Analysis (assuming MV normal
distribution)
NORMAL DISCRIMINANT ANALYSIS
The DISCRIM Procedure
Total Sample Size 626 DF Total
625
Variables 7 DF Within Classes 624
Classes 2 DF Between Classes 1
Prior probabilities used
DV
(2 levels)
Variable Prior
ADOPT Name Frequency Weight Proportion
Probability
0 _0 512 512.0000 0.817891
0.817891
1 _1 114 114.0000 0.182109
0.182109

Test of Homogeneity of Within Covariance
Matrices
Notation: K = Number of Groups
P = Number of Variables
N = Total Number of Observations - Number
of Groups
N(i) = Number of Observations in the i'th Group
- 1
__ N(i)/2
|| |Within SS Matrix(i)|
V = -----------------------------------
N/2
|Pooled SS Matrix|
_ _ 2
| 1 1 | 2P + 3P - 1
RHO = 1.0 - | SUM ----- - --- | -------------
|_ N(i) N _| 6(P+1)(K-1)
DF = .5(K-1)P(P+1)
_ _
| PN/2 |
| N V |
p-value for testing Ho: equal Var-Cov matrix
Under the null hypothesis: -2 RHO ln | ---------------
--- |
| __ PN(i)/2 |
|_ || N(i) _|
SAS uses Linear DA if equal Var-Cov matrix; uses Quadratic
DA if unequal (i.e., if reject Ho at alpha=.10)

is distributed approximately as Chi-Square(DF).
Chi-Square DF Pr > ChiSq
234.291358 28 <.0001
Since the Chi-Square value is significant at the 0.1
level, the within
covariance matrices will be used in the discriminant
function.
Reference: Morrison, D.F. (1976) Multivariate
Statistical Methods p252.
p-values for MANOVA test of Ho: no mean vector differences
between the 2 ADOPT groups
Multivariate Statistics and Exact F Statistics
S=1 M=2.5 N=308
Statistic Value F Value Num DF Den
DF Pr > F
Wilks' Lambda 0.80289363 21.67 7
618 <.0001
Pillai's Trace 0.19710637 21.67 7 618
<.0001
Hotelling-Lawley Trace 0.24549500 21.67 7
618 <.0001
Roy's Greatest Root 0.24549500 21.67 7
618 <.0001

Classification Summary for Calibration Data:
WORK.RFID
Resubstitution Summary using Quadratic
Discriminant Function
Number of Observations and Percent Classified into
ADOPT
From ADOPT 0 1 Total
0 400 112 512
Summary table of Hit Rates when using all data to estimate DA
function
78.13 21.88 100.00
1 37 77 114
32.46 67.54 100.00
Total 437 189 626
69.81 30.19 100.00
Priors 0.81789 0.18211
Error Count Estimates for ADOPT
0 1 Total
Rate 0.2188 0.3246 0.2380
Priors 0.8179 0.1821

Classification Summary for Calibration Data:
WORK.RFID
Cross-validation Summary using Quadratic
Discriminant Function
Hit Rate for ADOPT=0
Number of Observations and Percent Classified into
ADOPT
From ADOPT 0 1 Total
0 398 114 512
Summary table of Hit Rates when using jackknife method to
estimate DA function
77.73 22.27 100.00
1 50 64 114
43.86 56.14 100.00
Total 448 178 626
71.57 28.43 100.00
Priors 0.81789 0.18211
Hit Rate for ADOPT=1

Error Count Estimates for ADOPT
0 1 Total
Rate 0.2227 0.4386 0.2620
Priors 0.8179 0.1821
Overall hit rate = 1 - .262 = .738
SAS output Stepwise Logistic Regression
STEPWISE LOGISTIC REGRESSION
The LOGISTIC Procedure
Model Information
Data Set WORK.RFIDSUB
Response Variable ADOPT
Number of Response Levels 2
Model binary logit
DV (2 levels):
1=Adopt RFID
0=No adoption
Response Profile
Ordered Total
Value ADOPT Frequency
1 0 468
2 1 101

Probability modeled is ADOPT=1.
NOTE: 147 observations were deleted due to missing values.
Stepwise Selection Procedure
SAS creates dummy variables for QL IVs
Design
Class Value Variables
WLAN HI-USE 1 0
LO-USE 0 1
NO-USE -1 -1
WMS HI-USE 1 0
LO-USE 0 1
NO-USE -1 -1
BAR HI-USE 1 0
LO-USE 0 1
P2LS HI-USE 1 0
LO-USE 0 1
NO-USE -1 -1
REVENUE HIGH 1
LOW -1
FIRMTYPE DOM 1
INT -1

p-values for Ho: beta associated with IV = 0
STEPWISE LOGISTIC REGRESSION
IVs selected by the stepwise routine
Summary of Stepwise Selection
Effect Number Score Wald
Variable
Step Entered Removed DF In Chi-Square Chi-Square
Pr > ChiSq Label
1 NUMIT 1 1 42.4998 <.0001
2 REVENUE 1 2 20.5906 <.0001
3 CHLEADER 1 3 17.1828 <.0001
4 P2LS 2 4 18.1073 0.0001
5 WMS 2 5 12.3397 0.0021
Type 3 Analysis of Effects
Note: With 14 potential IVs, Step 1 conducts 14 chi-sq tests
(one for each IV), Step 2 conducts 13 chi-sq tests, Step 3
conducts 12 chi-sq tests, etc. At each step, IV with smallest p-
value is selected
Wald
Effect DF Chi-Square Pr > ChiSq
CHLEADER 1 16.5384 <.0001
NUMIT 1 7.2375 0.0071
WMS 2 11.3212 0.0035
P2LS 2 19.0501 <.0001
REVENUE 1 23.4229 <.0001
Analysis of Maximum Likelihood Estimates

Standard Wald
Parameter DF Estimate Error Chi-Square
Pr > ChiSq
Intercept 1 -5.6041 0.7805 51.5490
<.0001
CHLEADER 1 0.3095 0.0761 16.5384
<.0001
NUMIT 1 0.3620 0.1346 7.2375
0.0071
WMS HI-USE 1 0.8305 0.2512 10.9344
0.0009
WMS LO-USE 1 -0.6511 0.3810 2.9205
0.0875
P2LS HI-USE 1 -1.2804 0.3046 17.6678
<.0001
P2LS LO-USE 1 0.9976 0.2706 13.5920
0.0002
REVENUE HIGH 1 0.8824 0.1823
23.4229 <.0001
SAS output for fit of Logistic Regression model with only main
effects of IVs selected by stepwise
MAIN EFFECTS LOGISTIC REGRESSION
Model Information
Data Set WORK.RFID

Model binary logit
Response Profile
Ordered Total
π = P(Adopt RFID) is probability modeled in logistic regression
equation
DV (2 levels):
1=Adopt RFID
0=No adoption
1 0 512
2 1 114
Rsq statistic used to assess fit:
Values near 1 are excellent fit, values near 0 are poor fit
(Subjective decision)
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept

Intercept and
Criterion Only Covariates
AIC 596.169 465.647
SC 600.608 501.162
-2 Log L 594.169 449.647
p-value for overall model chi-sq test of Ho: all betas in model
= 0
(Reject Ho implies a statistically useful model)
R-Square 0.2062 Max-rescaled R-Square
0.3363
Number of IVs in model is 7 -- the IVs selected by the stepwise
routine
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 144.5219 7 <.0001
Score 123.3886 7 <.0001
Wald 86.0149 7 <.0001
MAIN EFFECTS LOGISTIC REGRESSION
p-values for testing each IV in model, Ho: beta associated
with IV = 0
(Reject Ho implies IV is a statistically useful predictor)
Standard Wald

Parameter DF Estimate Error Chi-Square Pr
> ChiSq
Intercept 1 -6.5899 0.7008 88.4291
<.0001
Independent variables (IV) in model
CHLEADER 1 0.3148 0.0728 18.6977
<.0001
NUMIT 1 0.4359 0.1296 11.3081
0.0008
REVDUM 1 1.7115 0.3389 25.5101
<.0001
P2HDUM 1 -1.6445 0.4420 13.8436
0.0002
P2LDUM 1 0.6271 0.3881 2.6102
0.1062
WMHDUM 1 0.9445 0.5089 3.4439
0.0635
WMLDUM 1 -0.3953 0.6878 0.3303
0.5655
95% Confidence Interval for odds ratio (OR) estimate of each
IV in model
Odds Ratio Estimates
Odds ratio (OR) estimates for each IV in model
Point 95% Wald
Effect Estimate Confidence Limits
CHLEADER 1.370 1.188 1.580
NUMIT 1.546 1.199 1.994
REVDUM 5.537 2.850 10.758
OR values > 1 imply odds increase as IV increases;
OR values < 1 imply odds decrease as IV increases

P2HDUM 0.193 0.081 0.459
Odds of Adopt RFID increase 1.55 times (i.e., by 55%) for each
unit increase in NUMIT
P2LDUM 1.872 0.875 4.006
WMHDUM 2.572 0.948 6.973
WMLDUM 0.673 0.175 2.593
Association of Predicted Probabilities and Observed
Responses
Percent Concordant 82.6 Somers' D 0.667
Percent Discordant 16.0 Gamma 0.676
Summary table of predictions using jackknife method
Percent Tied 1.4 Tau-a 0.199
Pairs 58368 c 0.833
Level used for making predictions (e.g., .5). If predicted prob.
of adopting RFID (π) > .5 then predict company will adopt. If
predicted π < .5, then predict company will not adopt.
Classification Table
Correct Incorrect Percentages
Prob Non- Non- Sensi- Speci- False
False
Level Event Event Event Event Correct tivity ficity
POS NEG
For a given prob. level:
Sensitivity is Hit Rate for Adopters;
Specificity is Hit Rate for Non-adopters
Select prob. level based on maximizing these hit rates
0.100 111 285 227 3 63.3 97.4 55.7 67.2

1.0
0.200 81 370 142 33 72.0 71.1 72.3 63.7
8.2
0.300 60 437 75 54 79.4 52.6 85.4 55.6
11.0
0.400 39 464 48 75 80.4 34.2 90.6 55.2
13.9
0.500 23 487 25 91 81.5 20.2 95.1 52.1
15.7
0.600 16 496 16 98 81.8 14.0 96.9 50.0
16.5
0.700 8 507 5 106 82.3 7.0 99.0 38.5
17.3
0.800 4 512 0 110 82.4 3.5 100.0 0.0
17.7
0.900 0 512 0 114 81.8 0.0 100.0 .
18.2
SAS output for testing interactions of channel leader IV with
other IVs in Logistic Regression model
TEST CHANNEL LEADER
INTERACTIONS
Model Information
Data Set WORK.RFID
Model binary logit

Response Profile
Ordered Total
1 0 512
2 1 114
NOTE: 90 observations were deleted due to missing valuess.
Intercept
Intercept and
AIC 596.169 470.688
SC 600.608 532.839
-2 Log L 594.169 442.688
R-Square 0.2149 Max-rescaled R-Square
0.3507
Number of IVs in model is 13 -- the 7 IVs selected by the
stepwise routine + 6 interactions with channel leader

Score 127.2428 13 <.0001
Wald 76.3402 13 <.0001
TEST CHANNEL LEADER
INTERACTIONS
Standard Wald
> ChiSq
Intercept 1 -11.8112 2.6631 19.6704
<.0001
CHLEADER 1 1.3518 0.4700 8.2711
0.0040
NUMIT 1 0.6703 0.3899 2.9559
0.0856
REVDUM 1 2.6683 1.1137 5.7407
0.0166
P2HDUM 1 -2.4134 1.2934 3.4819
0.0620
P2LDUM 1 0.3411 1.0803 0.0997
0.7522
WMHDUM 1 4.4171 2.5652 2.9651
0.0851
WMLDUM 1 3.7808 2.9408 1.6528
0.1986
CHL_NUM 1 -0.0469 0.0812 0.3335
0.5636
CHL_REV 1 -0.2023 0.2204 0.8426
0.3587
CHL_WMH 1 -0.6879 0.4753 2.0947

0.1478
CHL_WML 1 -0.8488 0.5723 2.1997
0.1380
CHL_P2H 1 0.1676 0.2725 0.3782
0.5385
CHL_P2L 1 0.0599 0.2414 0.0617
0.8039
Point 95% Wald
These are the 7 IVs selected by stepwise
CHLEADER 3.864 1.538 9.709
NUMIT 1.955 0.910 4.197
REVDUM 14.416 1.625 127.881
P2HDUM 0.090 0.007 1.129
P2LDUM 1.407 0.169 11.687
WMHDUM 82.854 0.543 >999.999
p-value for testing the interactions (nested model test); Ho: all
interaction betas =0
(Fail to reject Ho implies interactions are not significant)
WMLDUM 43.850 0.138 >999.999
CHL_NUM 0.954 0.814 1.119
These are the 6 channel leader interactions
CHL_REV 0.817 0.530 1.258
CHL_WMH 0.503 0.198 1.276
CHL_WML 0.428 0.139 1.314
CHL_P2H 1.182 0.693 2.017
CHL_P2L 1.062 0.662 1.704
Linear Hypotheses Testing Results

Wald
Label Chi-Square DF Pr > ChiSq
CHLEADER_INTERACTION 5.7630 6
0.4503
Number of terms (interactions) tested
TEST NUMBER IT INTERACTIONS
SAS output for testing interactions of NUMIT IV with other
IVs in Logistic Regression model
Model Information
Data Set WORK.RFID
Model binary logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
1 0 512
2 1 114

Intercept
Intercept and
AIC 596.169 460.609
SC 600.608 522.760
-2 Log L 594.169 432.609
Number of IVs in model is 13 -- the 7 IVs selected by the
stepwise routine + 6 interactions with NUMIT
Score 130.3523 13 <.0001
Wald 69.9183 13 <.0001
TEST NUMBER IT INTERACTIONS

Standard Wald
> ChiSq
Intercept 1 -11.4784 2.5018 21.0504
<.0001
CHLEADER 1 0.7476 0.3019 6.1310
0.0133
NUMIT 1 1.3536 0.5507 6.0409
0.0140
REVDUM 1 5.2846 1.6370 10.4219
0.0012
P2HDUM 1 -4.4241 3.1177 2.0136
0.1559
P2LDUM 1 4.4983 2.2370 4.0436
0.0443
WMHDUM 1 1.0095 1.3328 0.5737
0.4488
WMLDUM 1 -7.4836 3.7309 4.0234
0.0449
CHL_NUM 1 -0.0858 0.0598 2.0609
0.1511
NUM_REV 1 -0.7198 0.3025 5.6619
0.0173
NUM_WMH 1 0.0565 0.3817 0.0220
0.8822
NUM_WML 1 1.3533 0.7071 3.6632
0.0556
NUM_P2H 1 0.4742 0.5318 0.7952
0.3725
NUM_P2L 1 -0.7049 0.3867 3.3223
0.0683

Point 95% Wald
CHLEADER 2.112 1.169 3.817
NUMIT 3.871 1.315 11.393
REVDUM 197.280 7.974 >999.999
P2HDUM 0.012 <0.001 5.401
P2LDUM 89.865 1.121 >999.999
WMHDUM 2.744 0.201 37.399
WMLDUM <0.001 <0.001 0.843
These are the 6 NUMIT interactions
CHL_NUM 0.918 0.816 1.032
NUM_REV 0.487 0.269 0.881
NUM_WMH 1.058 0.501 2.236
NUM_P2H 1.607 0.567 4.556
NUM_P2L 0.494 0.232 1.055
Linear Hypotheses Testing Results
p-value for testing the interactions (nested model test); Ho: all
interaction betas =0
(Fail to reject Ho implies interactions are not significant)
Wald
Label Chi-Square DF Pr > ChiSq
NUMIT_INTERACTION 14.3850 6
0.0256
Note: When conducting multiple tests better to test each at a
small α (e.g., .01)
Number of terms (interactions) tested
For each problem, submit a document that describes:

1. (1) the research question(s) of interest
2. (2) the method of analysis and why it is appropriate
3. (3) the assumptions that underlie the method
4. (4) the statistical tests to be conducted
5. (5) a discussion of results that will answer the research
question
Assume you are writing the "methods" section of a research
paper to be submitted to a professional journal.
Problem Set 2 answer
1- The research questions
Is to determine whether the means of the outcome variables
described above differ depending on performance level.
2-The method of analysis used in this case isMANOVA and why
it is appropriate Multivariate analysis of variance (MANOVA)
is simply an ANOVA with several dependent variables. That is
to say, ANOVA tests for the difference in means between two or
more groups, while MANOVA tests for the difference in two or
more vectors of means this is why we have to used here.
3- Assumptions
· Equality regarding covariance matrices
· Each sample group is taken from a normally distributed
population
· The observations are sampled independently and randomly of
each other
4- the statistical tests to be conducted
It's better to conduct Bonferroni is more flexible and easier to
compute, it has limitations. Bonferroni assumes that tests are
independent while calculating familywise error
-5-discussion of results that will answer the research question
- Discussion of results - MANOVA
Test Criteria and Exact F Statistics. for the Hypothesis of No
Overall PERLEVEL Effect
H = Anova SSCP Matrix for PERLEVEL E = Error SSCP
Matrix
S=1 M=1.5 N=36

Statistic Value F Value
Num DF Den DF Pr > F
Wilks' Lambda 0.67500180 7.13
5 74 <.0001
Pillai's Trace 0.32499820 7.13
5 74 <.0001
Hotelling-Lawley Trace 0.48147752 7.13
5 74 <.0001
Roy's Greatest Root 0.48147752 7.13 5
74 <.0001
(p-values for MANOVA test of Ho: no mean vector differences
between the 2 PERLEEVEL groups)
Levene's Test for Homogeneity of EFFORT Variance
Sum of Mean
Pr > F
PERLEVEL 1 1.0414 1.0414 0.30
0.5832
p-value for testing equality of variances in DV for 2
PERLEVEL groups
(Fail to reject Ho implies equality of variances)
- Discussion of results - ANOVA
1st Dependent Variable analyzed EFFORT
R-Square Coeff Var Root MSE EFFORT
Mean
0.167933 28.78363 1.205315
4.187500
Value Pr > F
PERLEVEL 1 22.87039385 22.87039385
15.74 0.0002

· There is a significant difference in the mean
2en Dependent Variable analyzed UND
Value Pr > F
Model 1 9.3002113 9.3002113 3.92
0.0513
Error 78 185.1872887 2.3741960
R-Square Coeff Var Root MSE UND
Mean
0.047819 36.36207 1.540843
4.237500
Value Pr > F
PERLEVEL 1 9.30021129 9.30021129
3.92 0.0513
· There is NO significant difference in the mean
3rd Dependent Variable analyzed QUALIT
Value Pr > F
Model 1 7.6597701 7.6597701 5.45
0.0221
Error 78 109.5402299 1.4043619
R-Square Coeff Var Root MSE
QUALITY Mean
0.065356 28.90385 1.185058
4.100000
Value Pr > F

PERLEVEL 1 7.65977011 7.65977011
5.45 0.0221
• There is NO significant difference in the mean
4rd Dependent Variable analyzed LUCK
Value Pr > F
Model 1 10.5261663 10.5261663
3.64 0.0599
Error 78 225.2738337 2.8881261
R-Square Coeff Var Root MSE LUCK
Mean
0.044640 37.35052 1.699449
4.550000
Value Pr > F
PERLEVEL 1 10.52616633 10.52616633
3.64 0.0599
• There is NO significant difference in the mean
5rd Dependent Variable analyzed DIFF
Value Pr > F
Model 1 4.3206981 4.3206981 2.50
0.1180
Error 78 134.8668019 1.7290616
R-Square Coeff Var Root MSE DIFF
Mean
0.031042 39.69624 1.314938
3.312500

Value Pr > F
PERLEVEL 1 4.32069811 4.32069811
2.50 0.1180
• There is NO significant difference in the mea
PROBLEMS/DATA SETS
The following problems (with associated data sets) are designed
to test your ability to determine the proper multivariate
statistical method(s) to apply in order to answer the research
question(s) of interest.
For each problem, submit a document that describes:
(1) the research question(s) of interest
(2) the method of analysis and why it is appropriate
(3) the assumptions that underlie the method
(4) the statistical tests to be conducted
(5) a discussion of results that will answer the research
question
Assume you are writing the "methods" section of a research
paper to be submitted to a professional journal.
Each of the 2 data sets described employs at least one of the
following statistical methods:
(1) Analysis of Variance
(2) Analysis of Covariance
(3) Multivariate Analysis of Variance
(4) Multivariate Analysis of Covariance
(5) Discriminant Analysis
(6) Logistic Regression

(7) Cluster Analysis
(8) Principal Components
(9) Exploratory Factor Analysis
(10) Confirmatory Factor Analysis
.
Problem 2:
Attribution theory is concerned with the cognitive processes
that individuals use to explain their own performance in
situations where causal relations are ambiguous. Empirical
evidence indicates a tendency for individuals to attribute their
own successful performance to internal factors, such as effort or
ability, while poor performance is attributed to external factors
beyond the individual's control. An experiment was conducted
to examine the causal reasoning patterns of system users at the
conclusion of a competitive, computer-based business game.
Eighty MBA students used what appeared to be different
computer models to analyze unexpected variances in
manufacturing costs. (Actually, all students utilized the same
computer model.) Upon completion, students were paid an
amount based on their overall performance: those who were
told they performed poorly relative to their peers were paid $5,
while those who were told they did well earned $20. (In
actuality, the students were randomly assigned to one of the two
performance groups.) At the time of payment, participants
completed an evaluation form upon which five outcome
variables were measured (each on a 7-point Likert scale):
Internal Outcome SAS Variable Names
EFFORT -- amount of effort expended
UND -- how well they understood the cost structure
External Outcome SAS Variable Names

QUALITY -- quality of the computer model used
LUCK -- level of good/bad luck
DIFF -- difficulty of the task itself
The main purpose of the study is to determine whether the
means of the outcome variables described above differ
depending on performance level (SAS variable defined below).
PERLEVEL = 1 if poor performance ($5),
2 if good performance ($20)
The data are saved in the ATTRIB SAS file. Several
observations are listed below.
PERLEVEL EFFORT UND QUALITY LUCK DIFF
1 4 6 5 4 3
1 3 4 6 1 1
1 3 4 4 6 3
1 3 3 5 5 5
1 4 5 5 6 1
2 7 5 4 4 4
2 5 2 1 6 6
2 5 3 3 4 5
2 4 3 1 2 4
2 4 5 4 5 4
Problem 3:
Radio-frequency identification (RFID) is the wireless use of
electromagnetic fields to track data. Some industries have
already adopted RFID technology (e.g., an RFID tag attached to
an automobile during production is used to track its progress
through the assembly line), but others have yet to adopt. This
study attempts to identify those factors that increase the
likelihood or probability of RFID adoption for supply chain

management companies.
Data were collected through a Web-based survey of managers
who are members of the Institute for Supply Management
(ISM). A total of 755 managers participated in the survey. A
list of the variables measured for each manager is provided
below (SAS variable name given first). The researchers want to
use these variables to build an algorithm which accurately
predicts whether or not a supply management firm will adopt
RFID technology.
ASDOPT -- Firm’s RFID adoption status (1=adopted, 0=not
adopted)
NUMIT -- Total number of other information technology
adoptions at firm
WLAN – Level of wireless LAN adoption (HI-USE, LO-USE, or
NO-USE)
WMS – Level of warehouse management system adoption (HI-
USE, LO-USE, or NO-USE)
BAR – Level of barcode adoption (HI-USE, LO-USE, or NO-
USE)
P2LS – Level of “pick-to-light” system adoption (HI-USE, LO-
USE, or NO-USE)
FIRMTYPE – Domestic (DOM) or International (INT) firm
REVENUE – Firm revenue status (LOW or HIGH)
CHLEADER -- “My firm is obligated to do as the
channel/supply chain leader suggests”
(7-point Likert scale where 1=strongly disagree
and 7=strongly agree)
QUALITY -- “My firm is concerned with product quality”
(7-point Likert scale where 0=never and
7=always)
SERVICE -- “My firm feels channel/supply chain leader
provides services needed”

(7-point Likert scale where 1=strongly disagree
and 7=strongly agree)
The data are saved in the RFID SAS file. Several observations
are listed below.
CHLEADER QUALITY SERVICE ADOPT WLAN WMS
BAR P2LS NUMIT REVENUE FIRMTYPE
4 1 2 1 LO-USE NO-USE LO-USE NO-USE 3
LOW DOM
6 2 4 0 HI-USE NO-USE HI-USE NO-USE 3
LOW DOM
2 1 6 0 NO-USE HI-USE HI-USE NO-USE 4
HIGH DOM
6 4 4 1 HI-USE HI-USE HI-USE NO-USE 5
HIGH INT
4 5 4 0 LO-USE HI-USE HI-USE NO-USE 4
HIGH INT

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