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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
ANOVA F-test for testing differences in mean DV2 for two IV
levels
Dependent Variable: UND
Sum of
Source DF Squares Mean Square F
Value Pr > F
Model 1 9.3002113 9.3002113 3.92
0.0513
Error 78 185.1872887 2.3741960
Corrected Total 79 194.4875000
R-Square Coeff Var Root MSE UND
Mean
0.047819 36.36207 1.540843
4.237500
Source DF Anova SS Mean Square F
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
ANOVA F-test for testing differences in mean DV3 for two IV
levels
3rd DV analyzed
Dependent Variable: QUALITY
Sum of
Source DF Squares Mean Square F
Value Pr > F
Model 1 7.6597701 7.6597701 5.45
0.0221
Error 78 109.5402299 1.4043619
Corrected Total 79 117.2000000
R-Square Coeff Var Root MSE
QUALITY Mean
0.065356 28.90385 1.185058
4.100000
Source DF Anova SS Mean Square F
Value Pr > F
PERLEVEL 1 7.65977011 7.65977011
5.45 0.0221
4th DV analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV4 for two IV
levels
Dependent Variable: LUCK
Sum of
Source DF Squares Mean Square F
Value Pr > F
Model 1 10.5261663 10.5261663
3.64 0.0599
Error 78 225.2738337 2.8881261
Corrected Total 79 235.8000000
R-Square Coeff Var Root MSE LUCK
Mean
0.044640 37.35052 1.699449
4.550000
Source DF Anova SS Mean Square F
Value Pr > F
PERLEVEL 1 10.52616633 10.52616633
3.64 0.0599
5th DV analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV3 for two IV
levels
Dependent Variable: DIFF
Sum of
Source DF Squares Mean Square F
Value Pr > F
Model 1 4.3206981 4.3206981 2.50
0.1180

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Week 7 - Linear Regression Exercises SPSS Output Simple Linear Regression SPSS Output Descriptive Statistics Mean Std. Deviation N Family income prior month, all sources $1,485.49 $950.496 378 Hours worked per week in current job 33.52 12.359 378 Correlations Family income prior month, all sources Hours worked per week in current job Pearson Correlation Family income prior month, all sources 1.000 .300 Hours worked per week in current job .300 1.000 Sig. (1-tailed) Family income prior month, all sources . .000 Hours worked per week in current job .000 . N Family income prior month, all sources 378 378 Hours worked per week in current job 378 378 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .300a .090 .088 $907.877 a. Predictors: (Constant), Hours worked per week in current job ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 3.068E7 1 3.068E7 37.226 .000a Residual 3.099E8 376 824241.002 Total 3.406E8 377 a. Predictors: (Constant), Hours worked per week in current job b. Dependent Variable: Family income prior month, all sources Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 711.651 135.155 5.265 .000 445.896 977.405 Hours worked per week in current job 23.083 3.783 .300 6.101 .000 15.644 30.523 a. Dependent Variable: Family income prior month, all sources Part II: Multiple Regression SPSS Output  This part is going to begin with an example that has been interpreted for you. Analyze the output provided and read the interpretation of the data so that you will have an understanding of what you will do for the multiple regression assignment. Descriptive Statistics Mean Std. Deviation N CES-D Score 18.5231 11.90747 156 CESD Score, Wave 1 17.6987 11.40935 156 Number types of abuse .83 1.203 156 Correlations CES-D Score CESD Score, Wave 1 Number types of abuse Pearson Correlation CES-D Score 1.000 .412 .347 CESD Score, Wave 1 .412 1.000 .187 Number types of abuse .347 .187 1.000 Sig. (1-tailed) CES-D Score . .000 .000 CESD Score, Wave 1 .000 . .010 Number types of abuse .000 .010 . N CES-D Score 156 156 156 CESD Score, Wave 1 156 156 156 Number types of abuse 156 156 156 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .412a .170 .164 10.88446 .170 31.506 1 154 .000 2 .496b .246 .236 10.41016 .076 15.352 1 153 .000 a. Predictors: (Constant), CESD Score, Wave 1 b. Predictors: (Constant), CESD Score, Wave 1, Number types of abuse ANOVAc Model Sum of Squares df Mean Square F Sig. 1 Regression 3732.507 1 3732.507 31.506 .000a Residual 18244.613 154 118.472 Total 21977.1.

Error 78 134.8668019 1.7290616
Corrected Total 79 139.1875000
R-Square Coeff Var Root MSE DIFF
Mean
0.031042 39.69624 1.314938
3.312500
Source DF Anova SS Mean Square F
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
Source DF Squares Square F Value
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
Source DF Squares Square F Value
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
Source DF Squares Square F Value
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
Source DF Squares Square F Value
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
NOTE: This test controls the Type I experimentwise
error rate.
2nd DV analyzed
Alpha 0.05
Error Degrees of Freedom 78
Error Mean Square 2.374196
Critical Value of t 1.99085
Minimum Significant Difference 0.7134
Harmonic Mean of Cell Sizes 36.975
NOTE: Cell sizes are not equal.

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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
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4. a discussion of currency of the
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5. an explanation about why the source
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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
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reviewed) found through the Otis
Library databases.
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_______________________________
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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:
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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
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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
Number of Observations Read 716
Number of Observations Used 569
DV
(2 levels)
Class Level Information
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

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ScanScan 1Scan 2Scan 3Scan 4Scan 5Scan 6Scan 7Scan 8Scan 9Scan 10Scan 11Scan 12Scan 13 Chapter 13 Global Health Challenges MANY INDIVIDUALS AND NONGOVERNMENTAL ORGANIZATIONS (NGOS) HELP FIGHT GLOBAL DISEASE. The Bill and Melinda Gates Foundation plays a key role in the war against malaria, AIDS, and other diseases. Melinda and Bill Gates met with doctors and patients at the Manhica Research Center and Hospital in an area of Mozambique heavily affected by malaria. Learning Objectives 1. 13.1Recall the causes and effects of noncommunicable diseases 2. 13.2Evaluate the role of global travel and trade in facilitating the globalization of infectious diseases 3. 13.3Outline the three developments that gave rise to the concept of human security 4. 13.4Describe the three epidemiologic transitions to better understand contemporary concerns about infectious diseases 5. 13.5Report the cause, spread, effects, and control measures of influenza and avian flu 6. 13.6Report the cause, spread, effects, and control measures of malaria 7. 13.7Recognize the causes and preventive measures of HIV 8. 13.8Report the origin, spread, effects, and control measures of SARS 9. 13.9Report the origin, spread, effects, and control measures of Ebola 10. 13.10Outline role of the WHO in preventing the spread of infectious diseases Noncommunicable diseases (NCDs) such as heart disease, cancer, diabetes, chronic respiratory disease, and mental illness in general and Alzheimer’s disease in particular are the leading causes of death and disability globally. Long associated with affluent Western standards of living, NCDs are now a global problem. While rich countries are better equipped to deal with chronic diseases, they are far more deadly in poor countries. Growing numbers of old people and the spread of middle-class lifestyles make NCDs more prevalent than infectious diseases. Globalization also contributes to the growth of NCDs by helping expand the global middle class and by promoting fast foods, sugary drinks, alcohol, smoking, processed foods, and sedentary lifestyles. A major global health threat that undermines efforts to cure diseases is the emergence of germs that are resistant to antibiotics. This is due mainly to the excessive use of antibiotics in medicine and agriculture. Infectious diseases are intertwined with numerous global issues and are inseparable from political, economic, and cultural components of globalization. Ethnic conflicts make populations vulnerable to infectious diseases. Fighting contributes to the collapse of public services, which means that many people die from what would ordinarily be treatable diseases, such as diarrhea and respiratory infections. Conflicts also create refugees, overcrowding, and unsanitary conditions, thereby creating environments conducive to the spread of infectious diseases. Environmental degradation and deforestation expose humans to a variety of infectious diseases. They also contribute to global warming and flooding,.

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
Number of Observations Read 716
DV
(2 levels)
Number of Observations Used 626
Class Level Information
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
NORMAL DISCRIMINANT ANALYSIS
The DISCRIM Procedure
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
NORMAL DISCRIMINANT ANALYSIS
The DISCRIM Procedure
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
NORMAL DISCRIMINANT ANALYSIS
The DISCRIM Procedure
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

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Scapegoating is a theory of prejudice and discrimination. Society looks at the weakest group, and places blame on that group for all ills. That group then becomes the bottom level of society. We've seen this over the past 18 months. Illegal immigrants have been blamed for many issues, in particular crime and unemployment rates. Yet, I know few in my own area who will do the jobs these folks do every day. As for crime, please see the link below for a journal article that addresses this issue. Most crimes committed by immigrants without papers are misdemeanors.  What are your  thoughts? .

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Sara Mohammed 1991 Washington St. Indiana, PA 15701 (571) 550-3232 [email protected] EDUCATION Indiana University of Pennsylvania (IUP) Expected December 2020 Bachelor of Science in Business Northern Virginia Community College (NOVA), Woodbridge, VA May 2016 English As a Second Language Volunteerism Saudi club association at Gannon University Fall 2018 SKILLS · Speak three languages (Arabic, English, and Turkish) · Knowledge with technology · Experience with Microsoft, Word, Excel, and PowerPoint · Looking for helping others always · Familiar with taking care of kids .

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The DISCRIM Procedure
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
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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
Number of Observations Read 716
Number of Observations Used 569
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
Class Level Information
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
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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
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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
The LOGISTIC Procedure
Model Information
Data Set WORK.RFID
Response Variable ADOPT
Number of Response Levels 2
Model binary logit
Number of Observations Read 716
Number of Observations Used 626
Response Profile
Ordered Total
π = P(Adopt RFID) is probability modeled in logistic regression
equation
Value ADOPT Frequency
DV (2 levels):
1=Adopt RFID
0=No adoption
1 0 512
2 1 114
Probability modeled is ADOPT=1.
NOTE: 90 observations were deleted due to missing values.
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)
Analysis of Maximum Likelihood Estimates
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
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Scanned by CamScanner Scanned by CamScanner TABLE 2.2 Connecting Knowledge of Development and Learning to Teaching Practices Principles of Child Development and Learning Developmentally Appropriate Teaching Practices Children develop holistically • Teachers plan daily activities and routines to address aesthetic, emotional, cognitive, language, physical, and social development. • Teachers integrate learning across the curriculum (e.g., mixing language, physical, and social; combining math, science, and reading). Child development follows an orderly sequence • Teachers use their knowledge of developmental sequences to gauge whether children are developing as expected, to determine reasonable expectations, and to plan next steps in the learning process. Children develop at varying rates • Teachers give children opportunities to pursue activities at their own pace. • Teachers repeat activities more than once so children can participate according to changing needs and abilities. • Teachers plan activities with multiple learning objectives to address the needs of more and less advanced learners. Children learn best when they feel safe and secure • Teachers develop nurturing relationships with children and remain with children long enough so children can easily identify a specific adult from whom to seek help, comfort, attention, and guidance. • Daily routines are predictable. Changes in routine are explained in advance so children can anticipate what will happen. • There is two-way communication between teachers and families, and families are welcome in the program. • Children have access to images, objects, and activities that reflect their home experiences. • The early childhood environment complies with all safety requirements. • Adults use positive discipline to enhance children’s self-esteem, self-control, and problem-solving abilities. • Teachers address aggression and bullying calmly, firmly, and proactively. Children are active learners • Activities, transitions, and routines respect children’s attention span, need for activity and need for social interaction. Inactive segments of the day are short. • Children participate in gross motor activities every day. Children learn through a combination of physical experience, social experience, and reflection • Adults encourage children to explore and investigate. They pose questions, offer information, and challenge children’s thinking. • Children have many chances to document and reflect on their ideas. Children learn through mastery and challenge • Practitioners simplify, maintain, or extend activities in response to children’s functioning and comprehension. Children’s learning profiles vary • Teachers present the same information in more than one modality (seeing, hearing, touching) and through different types of activities. • Children have opportunities to play on their own and with others; indoors and outdoors; with natural and manufactured materials. Chil.

Sandro Reyes .docx
Sandro Reyes                                                      .docxSandro Reyes                                                      .docx
Sandro Reyes .docx

Sandro Reyes 1 5 Human Impact on the Environment Every day, I see the harmful impacts of humans on the environment. Just 13 percent of the globe’s oceans remain unsoiled by humanity’s damaging impacts (Carrington, 2018). In the remotest poles and Pacific areas, most of the ocean has no natural marine wildlife. Pollution, huge fishing fleets, and global shipping along with climate change are all degrading the oceans. The vehicles we drive every day, industrial wastes, overpopulation, and fossil fuels, all have negative effects on the environment. Human activities are negatively affecting the environment by degrading it and sooner or later, the earth will not be able to sustain humans. Overpopulation is now an epidemic with decreased mortality rates, improved medicine, and food sustainability. We are living longer, which is increasing population. The impact of overpopulation includes environmental degradation due to cutting down of trees to create space. With less trees to filter the air, an increase in carbon dioxide levels is damaging every single organism (Interesting Engineering, 2019). Another effect of overpopulation is overdependence on fossil fuels such as coal and oil, which emit plentiful carbon oxide into the air. With increased population, humans need more space, which damage ecosystems and augment carbon dioxide emissions. Pollution is another impact of human activities on the environment. From trash, industrial wastes to carbon dioxide emissions into the air, pollutions is inevitable. Over 2.4 billion individuals have no access to sources of clean water. Human activities continue to deplete indispensable resources such as soil, water, and air. United States, for example, produces 147 million metric tons of air pollution annually (Interesting Engineering, 2019). Air quality in developing nations continues to plummet as well. This means that we are engaging in activities that are hurting the environment. Global warming is one of the greatest causes of environmental degradation contributed by human activities. Some people do not believe that global warming is real. However, that is not true, and its major contributors include carbon dioxide emissions from respiration, deforestation, and burning fossil fuels. Each year, we continue to contribute to levels of carbon dioxide globally. Current levels exceed 400 PPM, and the rise in carbon dioxide emissions are attributed to an increase in global temperatures (Interesting Engineering, 2019). The result is the melting of arctic glaciers and land ice, which will increase sea levels, and have negative effects on oceanic life. Climate change is another impact on the environment that is being caused by us. It is linked .

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
The LOGISTIC Procedure
Model Information
Data Set WORK.RFID
Response Variable ADOPT
Number of Response Levels 2
Model binary logit
Number of Observations Read 716
Number of Observations Used 626
Response Profile
Ordered Total
Value ADOPT Frequency
1 0 512
2 1 114
Probability modeled is ADOPT=1.
NOTE: 90 observations were deleted due to missing valuess.
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
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
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 151.4814 13 <.0001
Score 127.2428 13 <.0001
Wald 76.3402 13 <.0001
TEST CHANNEL LEADER
INTERACTIONS
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr
> 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

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0.1478
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0.1380
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Point 95% Wald
Effect Estimate Confidence Limits
These are the 7 IVs selected by stepwise
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Number of terms (interactions) tested
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Number of Observations Used 626
Response Profile
Ordered Total
Value ADOPT Frequency
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2 1 114
Probability modeled is ADOPT=1.
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Model Convergence Status
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Model Fit Statistics
Intercept
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Point 95% Wald
Effect Estimate Confidence Limits
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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
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Number of terms (interactions) tested
For each problem, submit a document that describes:
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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
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Problem Set 2 answer
1- The research questions
Is to determine whether the means of the outcome variables
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2-The method of analysis used in this case isMANOVA and why
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to say, ANOVA tests for the difference in means between two or
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3- Assumptions
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4- the statistical tests to be conducted
It's better to conduct Bonferroni is more flexible and easier to
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-5-discussion of results that will answer the research question
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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
Source DF Squares Square F Value
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
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
· There is a significant difference in the mean
2en Dependent Variable analyzed UND
Source DF Squares Mean Square F
Value Pr > F
Model 1 9.3002113 9.3002113 3.92
0.0513
Error 78 185.1872887 2.3741960
Corrected Total 79 194.4875000
R-Square Coeff Var Root MSE UND
Mean
0.047819 36.36207 1.540843
4.237500
Source DF Anova SS Mean Square F
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
Source DF Squares Mean Square F
Value Pr > F
Model 1 7.6597701 7.6597701 5.45
0.0221
Error 78 109.5402299 1.4043619
Corrected Total 79 117.2000000
R-Square Coeff Var Root MSE
QUALITY Mean
0.065356 28.90385 1.185058
4.100000
Source DF Anova SS Mean Square F
Value Pr > F

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SBUX ISIncome Statement - As Reported 10K in millionsIncome Statement - As Reported 10Q in millions9/30/139/30/149/30/159/30/169/30/179/30/18TTM12/30/173/30/186/30/189/30/1812/29/18TTM Company-operated stores$11,793.2$12,977.9$15,197.3$16,844.1$17,650.719,690.320,318.8 Company-operated stores4,741.84,828.05,060.45,060.1$5,370.3020,318.8 Total specialty$3,073.6$3,469.9$3,965.4$4,471.8$4,736.15,029.24,959.6 Total specialty1,331.91,203.81,249.91,243.5$1,262.404,959.6 Licensed stores$1,360.5$1,588.6$1,861.9$2,154.2$2,355.02,652.22,706.9 Licensed stores682.4625.6660.6683.6$737.102,706.9 CPG, foodservice and other$1,713.1$1,881.3$2,103.5$2,317.6$2,381.12,377.02,252.7 CPG, foodservice and other649.5578.2589.3559.9$525.302,252.7Total net revenues$14,866.8$16,447.8$19,162.7$21,315.9$22,386.8$24,719.525,278.4Total net revenues6,073.76,031.86,310.36,303.6$6,632.7025,278.4 Cost of sales including occupancy costs-$6,382.3-$6,858.8-$7,787.5-$8,511.1-$9,038.2-10,174.5-10,434.2 Cost of sales including occupancy costs-2,502.9-2,516.0-2,554.9-2,604.6($2,758.70)-10,434.2 Store operating expenses-$4,286.1-$4,638.2-$5,411.1-$6,064.3-$6,493.3-7,193.2-7,449.2 Store operating expenses-1,737.0-1,789.6-1,825.0-1,841.6($1,993.00)-7,449.2 Other operating expenses-$431.8-$457.3-$522.4-$545.4-$553.8-539.3-532.2 Other operating expenses-141.6-134.3-148.0-156.7($93.20)-532.2 Depreciation and amortization expenses-$621.4-$709.6-$893.9-$980.8-$1,011.4-1,247.0-1,321.6 Depreciation and amortization expenses-258.8-331.6-330.0-326.6($333.40)-1,321.6 General and administrative expenses-$937.9-$991.3-$1,196.7-$1,360.6-$1,393.3-1,759.0-1,797.8 General and administrative expenses-379.1-405.8-468.7-460.0($463.30)-1,797.8 Restructuring and impairments$0.0$0.0$0.0$0.0-$153.5-224.4-240.0 Restructuring and impairments-27.6-134.7-16.9-45.2($43.20)-240.0 Litigation credit / charge-$2,784.1$20.2$0.0$0.0$0.0$0.0Income from equity investees89.452.771.487.7$67.80279.6Income from equity investees$251.4$268.3$249.9$318.2$391.4301.2279.6Operating income / loss1,116.1772.51,038.2956.6$1,015.703,783.0Operating income / loss-$325.4$3,081.1$3,601.0$4,171.9$4,134.7$3,883.33,783.0Gain resulting from acquisition of joint venture1,326.3Net interest and other income62.3483-$24.8074.9 Gain resulting from acquisition of joint venture$0.0$0.0$390.6$0.0$0.01,376.4$0.0 Interest income and other, net88.2313239$24.80126.0Loss on divestiture of certain operations$0.0$0.0-$61.1$0.0$0.0499.2 Interest expense-25.9-503($75.00)-77.0 Interest income and other, net$123.6$142.7$43.0$108.0$275.3191.4$126.0Earnings / loss before income taxes3,005.9363236$965.501,068.7 Interest expense-$28.1-$64.1-$70.5-$81.3-$92.5-170.3-$77.0Income tax expense / benefit-755.8-35-45-64($205.10)-349.4Earnings / loss before income taxes-$229.9$3,159.7$3,903.0$4,198.6$4,317.5$5,780.0$1,068.7Net earnings / loss including noncontrolling interests2,250.18161,027932$760.403,534.721.83%Net earnings / loss attributab.

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Scan the articles in the attached course text.  Write a discussion initial post on one of the articles.  Choose the one that interests you most.  1.Provide a very brief overview of what you think are the key points (a literature review).  2.What about the policy area interests you?  3.What about the information systems involved in the article interested you?  4.How might this article’s research approach help you in your dissertation research project? (NOTE: Please cut and paste the above-numbered list into your reply to help with organization.) .

Scale Ratio Variable Histograms are useful for presenting qu.docx
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Scale Ratio Variable  Histograms are useful for presenting quantitative data such as the example variable ADULT_CT which describes the number of individuals per household. The variable measurement is scale ratio and as it depicts a number, a histogram is able to reflect the number of individuals belonging to each variable value or interval of values (Mishra, Pandey, Singh & Gupta, 2018).). Histograms divide the variable into equal intervals as shown below in individuals reported per home. The graph indicates nearly 3,000 reporting and displays the individual numbers per interval. The bar levels of the graph make it is easy to discern the average number reporting as 2 per household. Nominal Variable  As nominal variables depict qualitative data such as in the variable Q87 which describes the level of trust individuals felt towards others, a pie graph would be beneficial to use as it easily displays each group or individual share in the total being examined (Mishra, Pandey, Singh & Gupta, 2018). For example, the pie graph here which shows what percentage of trust was and wasn’t felt toward others. Graphs like these are appropriate for showing a variable that cannot be ordered or numerical in value such as feelings of trust (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020). References Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications. Mishra, P., Pandey, C. M., Singh, U., & Gupta, A. (2018). Scales of measurement and presentation of statistical data. Annals of cardiac anesthesia , 21 (4), 419. Wagner, III, W.E. (2020).  Using IBM® SPSS® statistics for research methods and social science statistics  (7th ed.). Thousand Oaks, CA: Sage Publications.   Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style. .

PERLEVEL 1 7.65977011 7.65977011
5.45 0.0221
• There is NO significant difference in the mean
4rd Dependent Variable analyzed LUCK
Source DF Squares Mean Square F
Value Pr > F
Model 1 10.5261663 10.5261663
3.64 0.0599
Error 78 225.2738337 2.8881261
Corrected Total 79 235.8000000
R-Square Coeff Var Root MSE LUCK
Mean
0.044640 37.35052 1.699449
4.550000
Source DF Anova SS Mean Square F
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
Source DF Squares Mean Square F
Value Pr > F
Model 1 4.3206981 4.3206981 2.50
0.1180
Error 78 134.8668019 1.7290616
Corrected Total 79 139.1875000
R-Square Coeff Var Root MSE DIFF
Mean
0.031042 39.69624 1.314938
3.312500
Source DF Anova SS Mean Square F
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

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Scan 12Scan 13Scan 14Scan 15Scan 16Scan 17Scan 18Scan 19 HIST 308 Sofia Clark Spring 2020 Research Paper Sample Outline: 1) Introduction 2) Story of capture 3) Background on British antislavery 4) Background on Royal Navy 5) Background on this specific Royal Navy vessel 6) Story of what treaty was used to condemn the slave ship 7) Background on treaty 8) Background on British relations with treaty country 9) Background on slave trade in this particular region 10) Story of what happens to the captives removed from this particular slave ship 11) Background on the general treatment of liberated Africans 12) Explanation of how the story of your ship exemplifies the broader history of slavery and anti-slavery Bibliography 1) The slave trade in general (i.e., either the Transatlantic slave trade or Indian Ocean slave trade depending on your ship) Article (JSTOR): Alkalimat, Abdul. "Slave Trade." In The African American Experience in Cyberspace: A Resource Guide to the Best Web Sites on Black Culture and History, 34-42. LONDON; STERLING, VIRGINIA: Pluto Press, 2004. Accessed May 30, 2020. doi:10.2307/j.ctt183q64x.8. Article (JSTOR): JUNKER, CARSTEN. "Containing Bodies—Enscandalizing Enslavement: Stasis and Movement at the Juncture of Slave-Ship Images and Texts." In Migrating the Black Body: The African Diaspora and Visual Culture, edited by RAIFORD LEIGH and RAPHAEL-HERNANDEZ HEIKE, 13-29. Seattle; London: University of Washington Press, 2017. Accessed May 30, 2020. www.jstor.org/stable/j.ctvcwnj4v.5. 2) The slave trade in the specific area of Africa in which your ship embarked enslaved African captives (e.g., Bight of Benin, Senegambia, Angola). Book (JSTOR): Strickrodt, Silke. "The Atlantic Connection: Little Popo & the Rise of Afro-European Trade on the Western Slave Coast, C. 1600 to 1702." In Afro-European Trade in the Atlantic World: The Western Slave Coast, C. 1550- C. 1885, 65-101. Woodbridge, Suffolk; Rochester, NY: Boydell & Brewer, 2015. Accessed May 30, 2020. doi:10.7722/j.ctt7zst5n.9. Article (JSTOR): Graham, James D. "The Slave Trade, Depopulation and Human Sacrifice in Benin History: The General Approach." Cahiers D'Études Africaines 5, no. 18 (1965): 317-34. Accessed May 30, 2020. www.jstor.org/stable/4390897. 3) Slavery in the region to which your ship was heading (e.g., Cuba, Bahia, Pernambuco). Book (One Search): Schneider, Elena Andrea. The Occupation of Havana: War, Trade, and Slavery in the Atlantic World. North Carolina Scholarship Online. Williamsburg, Virginia : Chapel Hill: Omohundro Institute of Early American History and Culture ; University of North Carolina Press, 2018. Article (Project Muse): Garrigus, John. "Cuba, Haiti, and the Age of Atlantic Revolution." Reviews in American History 44, no. 1 (2016): 52-57. doi:10.1353/rah.2016.0012. 4) British antislavery policy toward the country your ship was from (e.g., Portugal, Spain, USA) Book- page 14(Academic Search Premiere- also works for #.

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single sign onoktasso
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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  • 1. 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
  • 2. 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 ANOVA F-test for testing differences in mean DV2 for two IV levels Dependent Variable: UND Sum of Source DF Squares Mean Square F Value Pr > F Model 1 9.3002113 9.3002113 3.92 0.0513 Error 78 185.1872887 2.3741960 Corrected Total 79 194.4875000 R-Square Coeff Var Root MSE UND Mean 0.047819 36.36207 1.540843 4.237500 Source DF Anova SS Mean Square F Value Pr > F PERLEVEL 1 9.30021129 9.30021129 3.92 0.0513
  • 3. Note: Good strategy is conduct univariate ANOVA F-tests at a small α (e.g., α = .01 or .02) The ANOVA Procedure ANOVA F-test for testing differences in mean DV3 for two IV levels 3rd DV analyzed Dependent Variable: QUALITY Sum of Source DF Squares Mean Square F Value Pr > F Model 1 7.6597701 7.6597701 5.45 0.0221 Error 78 109.5402299 1.4043619 Corrected Total 79 117.2000000 R-Square Coeff Var Root MSE QUALITY Mean 0.065356 28.90385 1.185058 4.100000 Source DF Anova SS Mean Square F Value Pr > F PERLEVEL 1 7.65977011 7.65977011 5.45 0.0221 4th DV analyzed The ANOVA Procedure ANOVA F-test for testing differences in mean DV4 for two IV levels
  • 4. Dependent Variable: LUCK Sum of Source DF Squares Mean Square F Value Pr > F Model 1 10.5261663 10.5261663 3.64 0.0599 Error 78 225.2738337 2.8881261 Corrected Total 79 235.8000000 R-Square Coeff Var Root MSE LUCK Mean 0.044640 37.35052 1.699449 4.550000 Source DF Anova SS Mean Square F Value Pr > F PERLEVEL 1 10.52616633 10.52616633 3.64 0.0599 5th DV analyzed The ANOVA Procedure ANOVA F-test for testing differences in mean DV3 for two IV levels Dependent Variable: DIFF Sum of Source DF Squares Mean Square F Value Pr > F Model 1 4.3206981 4.3206981 2.50 0.1180
  • 5. Error 78 134.8668019 1.7290616 Corrected Total 79 139.1875000 R-Square Coeff Var Root MSE DIFF Mean 0.031042 39.69624 1.314938 3.312500 Source DF Anova SS Mean Square F 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
  • 6. 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 Source DF Squares Square F Value 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 Source DF Squares Square F Value Pr > F
  • 7. 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 Source DF Squares Square F Value 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 Source DF Squares Square F Value 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
  • 8. 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 NOTE: This test controls the Type I experimentwise error rate. 2nd DV analyzed Alpha 0.05 Error Degrees of Freedom 78 Error Mean Square 2.374196 Critical Value of t 1.99085 Minimum Significant Difference 0.7134 Harmonic Mean of Cell Sizes 36.975 NOTE: Cell sizes are not equal.
  • 9. Means with the same letter are not significantly different. Ranking of DV means (means with the same letter are not significantly different) Bon Grouping Mean N PERLEVEL A 4.6897 29 2 A A 3.9804 51 1 Bonferroni (Dunn) t Tests for QUALITY 3rd DV analyzed NOTE: This test controls the Type I experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 78 Error Mean Square 1.404362 Critical Value of t 1.99085 Minimum Significant Difference 0.5487 Harmonic Mean of Cell Sizes 36.975 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Bon Grouping Mean N PERLEVEL A 4.3333 51 1
  • 10. B 3.6897 29 2 4th DV analyzed Bonferroni (Dunn) t Tests for LUCK NOTE: This test controls the Type I experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 78 Error Mean Square 2.888126 Critical Value of t 1.99085 Minimum Significant Difference 0.7869 Harmonic Mean of Cell Sizes 36.975 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Bon Grouping Mean N PERLEVEL A 4.8235 51 1 A A 4.0690 29 2 Bonferroni (Dunn) t Tests for DIFF 5th DV analyzed NOTE: This test controls the Type I experimentwise
  • 11. error rate. Alpha 0.05 Error Degrees of Freedom 78 Error Mean Square 1.729062 Critical Value of t 1.99085 Minimum Significant Difference 0.6088 Harmonic Mean of Cell Sizes 36.975 NOTE: Cell sizes are not equal. Means with the same letter are not significantly different. Bon Grouping Mean N PERLEVEL A 3.6207 29 2 A A 3.1373 51 1 SOPHOMORE - PRACTICING A - level Written Communication SOPHOMORE - PRACTICING B - level Written Communication SOPHOMORE - PRACTICING C - level Written Communication
  • 12. SOPHOMORE - PRACTICING D - level Written Communication SOPHOMORE - PRACTICING F - level Written Communication 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. Using language (grammar, syntax, 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. Using language (grammar, syntax, 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
  • 13. subject. The work begins to address the context, audience and purpose of the assignment. Using language (grammar, syntax, 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. Using language (grammar, syntax, 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. SOPHOMORE - PRACTICING A - level Critical Thinking SOPHOMORE - PRACTICING B - level Critical Thinking SOPHOMORE - PRACTICING
  • 14. C - level Critical Thinking SOPHOMORE - PRACTICING D - level Critical Thinking SOPHOMORE - PRACTICING 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. Issue/problem to be considered 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. Issue/problem to be considered critically is generally clear. Ideas are taken from quality sources with adequate interpretation/ evaluation
  • 15. 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. Issue/problem to be considered 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. Issue/problem to be considered 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. SOPHOMORE - PRACTICING A - level Information Literacy SOPHOMORE - PRACTICING B - level Information Literacy
  • 16. SOPHOMORE - PRACTICING C - level Information Literacy SOPHOMORE - PRACTICING D - level Information Literacy SOPHOMORE - PRACTICING F - level Information Literacy 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 demonstrated through at least TWO
  • 17. 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. an explanation about why the source is relevant to the project/paper. A works cited page is required and must include at least 4 QUALITY sources that are incorporated into paper/project. Information literacy competency is 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. an explanation about why the source is relevant to the project/paper A works cited page is required and must include at least 3 QUALITY sources that are incorporated into
  • 18. 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
  • 19. “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
  • 20. 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...
  • 21. 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.
  • 22. 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:
  • 23. 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 Number of Observations Read 716 Number of Observations Used 569 DV (2 levels) Class Level Information 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
  • 24. 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
  • 25. 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 Number of Observations Read 716 DV (2 levels) Number of Observations Used 626 Class Level Information 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
  • 26. NORMAL DISCRIMINANT ANALYSIS The DISCRIM Procedure 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)
  • 27. 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 NORMAL DISCRIMINANT ANALYSIS The DISCRIM Procedure 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
  • 28. NORMAL DISCRIMINANT ANALYSIS The DISCRIM Procedure 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
  • 29. NORMAL DISCRIMINANT ANALYSIS The DISCRIM Procedure 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
  • 30. 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 Number of Observations Read 716 Number of Observations Used 569 DV (2 levels): 1=Adopt RFID 0=No adoption Response Profile Ordered Total Value ADOPT Frequency 1 0 468 2 1 101
  • 31. Probability modeled is ADOPT=1. NOTE: 147 observations were deleted due to missing values. Stepwise Selection Procedure SAS creates dummy variables for QL IVs Class Level Information 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
  • 32. 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
  • 33. 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 The LOGISTIC Procedure Model Information Data Set WORK.RFID
  • 34. Response Variable ADOPT Number of Response Levels 2 Model binary logit Number of Observations Read 716 Number of Observations Used 626 Response Profile Ordered Total π = P(Adopt RFID) is probability modeled in logistic regression equation Value ADOPT Frequency DV (2 levels): 1=Adopt RFID 0=No adoption 1 0 512 2 1 114 Probability modeled is ADOPT=1. NOTE: 90 observations were deleted due to missing values. 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
  • 35. 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) Analysis of Maximum Likelihood Estimates Standard Wald
  • 36. 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
  • 37. 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
  • 38. 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 The LOGISTIC Procedure Model Information Data Set WORK.RFID Response Variable ADOPT Number of Response Levels 2 Model binary logit Number of Observations Read 716 Number of Observations Used 626
  • 39. Response Profile Ordered Total Value ADOPT Frequency 1 0 512 2 1 114 Probability modeled is ADOPT=1. NOTE: 90 observations were deleted due to missing valuess. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates 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 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq
  • 40. Likelihood Ratio 151.4814 13 <.0001 Score 127.2428 13 <.0001 Wald 76.3402 13 <.0001 TEST CHANNEL LEADER INTERACTIONS Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > 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
  • 41. 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 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits 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
  • 42. 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 The LOGISTIC Procedure Model Information Data Set WORK.RFID Response Variable ADOPT Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 716 Number of Observations Used 626 Response Profile Ordered Total Value ADOPT Frequency 1 0 512 2 1 114
  • 43. Probability modeled is ADOPT=1. NOTE: 90 observations were deleted due to missing values. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates 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 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 161.5598 13 <.0001 Score 130.3523 13 <.0001 Wald 69.9183 13 <.0001 TEST NUMBER IT INTERACTIONS The LOGISTIC Procedure Analysis of Maximum Likelihood Estimates
  • 44. Standard Wald Parameter DF Estimate Error Chi-Square Pr > 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 Odds Ratio Estimates
  • 45. Point 95% Wald Effect Estimate Confidence Limits 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:
  • 46. 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
  • 47. 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 Source DF Squares Square F Value 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 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
  • 48. · There is a significant difference in the mean 2en Dependent Variable analyzed UND Source DF Squares Mean Square F Value Pr > F Model 1 9.3002113 9.3002113 3.92 0.0513 Error 78 185.1872887 2.3741960 Corrected Total 79 194.4875000 R-Square Coeff Var Root MSE UND Mean 0.047819 36.36207 1.540843 4.237500 Source DF Anova SS Mean Square F 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 Source DF Squares Mean Square F Value Pr > F Model 1 7.6597701 7.6597701 5.45 0.0221 Error 78 109.5402299 1.4043619 Corrected Total 79 117.2000000 R-Square Coeff Var Root MSE QUALITY Mean 0.065356 28.90385 1.185058 4.100000 Source DF Anova SS Mean Square F Value Pr > F
  • 49. PERLEVEL 1 7.65977011 7.65977011 5.45 0.0221 • There is NO significant difference in the mean 4rd Dependent Variable analyzed LUCK Source DF Squares Mean Square F Value Pr > F Model 1 10.5261663 10.5261663 3.64 0.0599 Error 78 225.2738337 2.8881261 Corrected Total 79 235.8000000 R-Square Coeff Var Root MSE LUCK Mean 0.044640 37.35052 1.699449 4.550000 Source DF Anova SS Mean Square F 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 Source DF Squares Mean Square F Value Pr > F Model 1 4.3206981 4.3206981 2.50 0.1180 Error 78 134.8668019 1.7290616 Corrected Total 79 139.1875000 R-Square Coeff Var Root MSE DIFF Mean 0.031042 39.69624 1.314938 3.312500
  • 50. Source DF Anova SS Mean Square F 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
  • 51. (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
  • 52. 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
  • 53. 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”
  • 54. (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