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Assessment Plan Template
Program
M.S. in Mathematical Sciences (Applied Statistics Concentration)
Assessment Coordinator for the program
Farrokh Saba
Department(s) or Interdisciplinary Council Responsible for the Program Mathematical Sciences
Five-Year Implementation Dates______ (2004-2005 to 2009-2010)_
Is this program accredited by an external organization?
No
X
Yes, and the organization is Northwest Commission on
Colleges and Universities.
NOTE: The program may submit the most recent self study assessment documents/information in substitution for this plan.
1. Student Learning Outcomes for the program. List the Student Learning Outcomes for the program.
Upon completion of the master degree in mathematical sciences (Applied Statistics Concentration), students would be able to
demonstrate knowledge of applied statistics, analyze applied statistics concepts, do problem-solving in applied statistics in the
following areas:
MAT 657
Finite & infinite sets, sequences of functions, continuous functions, differentiation of functions of one
variable.
MAT 663
Advanced Matrix Theory and Applications.
STA 667
Introductory to statistical inference, discrete and continuous probability, probability models, estimations,
Bayesian estimation, confidence intervals, hypothesis testing.
STA 767
Advanced Mathematical Statistics: Point estimation: Equivalences, admissibility, minimaxity, optimality properties,
asymptotic properties, unbiasedness, similarity, invariance, admissibility, minimaxity in hypotheses testing,
linear hypotheses, and conditional inference.
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6 credit from:
(a)
STA 763
Regression and Multivariate Analysis: Matrix theory, examining residuals, multiple regression, nonlinear
regression, multivariate normal, variance-covariance matrix, canonical correlation, distribution of characteristic roots.
STA 765
Or
(b)
STA 751
STA 769
Statistical Decision Theory: Decision rules, loss functions, risk functions, decision principles, utility theory, Bayesian
estimators, hypothesis testing, Bayesian sequential analysis.
Spatial Statistics: Stochastic process, first and second order stationarity, intrinsic hypothesis, models of spatial
dependence, different forms of Kriging, bicubic splines, conditional simulation.
Environmental Statistics II: Multivariate Methods, testing for multivariate normality, multivariate control charts,
exploratory data analysis, cluster analysis, factor analysis, and multivariate calibration problems.
6 credits of Thesis:
Or
An additional 6 credits of MAT or STA at 700 level from area of specialization.
Thesis Defense: Demonstrate the ability to successfully present results in both oral and written formats.
Or
Written Comprehensive exam: Based on degree requirements.
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2. Curriculum Alignment of Student Learning Outcomes. Where is the information introduced, enriched, and/or reinforced in the
courses required in the program?
Required
Courses
Area of
Program
MAT 657
MAT 663
STA 667
STA 767
Specialization:
Outcome
6 credits
Goals
a. STA 763,
765
b. STA 751,
769
Demonstrate
strongly
knowledge of
applied
statistics
Analyze
strongly
applied
statistics
concepts
Do master
problemsolving in
applied
statistics
Do master
applied
statistics
problems
I
I, E
I
I, E
I
I
I, E
I
I, E
I
I
I, E
I
I, E
I
I
I, E
I
I, E
I
I = Introduced E = Enhanced R = Reintroduced
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Required
Courses
Program
Outcome
Goals
6 credits for
thesis or an
additional 6
credits at 700
level.
Demonstrate
strongly
knowledge of
applied
statistics
Analyze
strongly
applied
statistics
concepts
Do master
problemsolving in
applied
statistics
Do master
applied
statistics
problems
Final
Examination:
either to defend
the thesis or a
written
comprehensive
examination
I, E
I, E
I, E
I, E
I = Introduced E = Enhanced R = Reintroduced
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3. Methods, Instruments and Analysis. What instruments will be used in each of the five years? When and where will they be
administered in each of the five years? Which Student Learning Outcomes will be assessed during each of the 5 years? How will
results be reported (e.g. percentages, ranks, state or national comparisons) for each of the 5 years?
Learning
Outcome
Assessment
Questions
A student at
completion of
the degree will
be able to
demonstrate
completing in:
Person
Instrument
responsible for
instrument
development/
Who will
administer
instruments
and collect
data
Did students
master?
Introduction to
Real Analysis:
Finite &
infinite sets,
sequences of
functions,
continuous
functions,
differentiation
of functions of
one variable.
Did students master
Finite & infinite
sets, sequences of
functions,
continuous
functions,
differentiation of
functions of one
variable
Instructor/
Instructor
MAT 657
Exams
When and
where will
data be
collected
Person
responsible
for data
analysis and
report
End of
semester and
In class
Farrokh Saba
Assessment
Coordinator
End of
semester and
In class
Assessment
Coordinator
Expected
Measures
(mean &
standard
deviation),
component
analysis,
percentage
of
agreement
or strongly
agree,
percentage
who meet
of exceed
benchmark
Grade of B
or better
Benchmark
Grade of Aor better
Grade of Aor better
5
Advanced
Matrix Theory
and
Applications.
Ortthogonal
matrices,
Gram-Schmidt
method, Q_R
factorization,
least-square
fits,
eigenvalues
and eigen
vectors,
Markov
processes,
simplex
method.
Introductory to
statistical
inference,
discrete and
continuous
probability,
probability
models,
estimations,
Bayesian
estimation,
confidence
intervals,
hypothesis
testing.
applications.
Did students master
Ortthogonal
matrices, GramSchmidt method,
Q_R factorization,
least-square fits,
eigenvalues and
eigen vectors,
Markov processes,
simplex method?
Did students master
Banach spaces,
Hilbert spaces,
computational
applications, linear
functionals and
operators,
operators, fixed
point theorems,
iterative methods,
elementary spectral
theory, and
applications?
Instructor/
Instructor
MAT 663
Exams
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Instructor/
Instructor
STA 667
Exams
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
6
Mathematical
Statistics:
Basic
probability
theory,
conditional
probability.
a.
Regression and
Multivariate
Analysis:
Matrix theory,
examining
residuals,
multiple
regression,
nonlinear
regression,
multivariate
normal,
variancecovariance
matrix,
canonical
correlation,
distribution of
characteristic.
Did students master
Basic probability
theory, conditional
probability?
a.
Did students master
Matrix theory,
examining
residuals, multiple
regression,
nonlinear
regression,
multivariate
normal, variancecovariance matrix,
canonical
correlation,
distribution of
characteristic?
Instructor/
Instructor
STA 767
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Exams
Instructor/
Instructor
STA 763
Exams
7
Statistical
Decision
Theory:
Decision rules,
loss functions,
risk functions,
decision
principles,
utility theory,
Bayesian
estimators,
hypothesis
testing,
Bayesian
sequential
analysis.
Did students master
Instructor/
Decision rules, loss Instructor
functions, risk
functions, decision
principles, utility
theory, Bayesian
estimators,
hypothesis testing,
Bayesian sequential
analysis?
STA 765
Exams
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Or
b. Spatial
Statistics:
Stochastic
process, first
and second
order
stationarity,
intrinsic
hypothesis,
models of
spatial
dependence,
different forms
of Kriging,
bicubic splines,
conditional
simulation.
Or
STA 751
Exams
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Did students master
Stochastic process,
first and second
order stationarity,
intrinsic
hypothesis, models
of spatial
dependence,
different forms of
Kriging, bicubic
splines, conditional
simulation.
Instructor/
Instructor
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Environmental
Statistics II:
Multivariate
Methods,
testing for
multivariate
normality,
multivariate
control charts,
exploratory
data analysis,
cluster
analysis, factor
analysis, and
multivariate
calibration
problems.
Did students master
Instructor/
Multivariate
Instructor
Methods, testing
for multivariate
normality,
multivariate control
charts, exploratory
data analysis,
cluster analysis,
factor analysis, and
multivariate
calibration
problems?
STA 769
Exams
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Demonstrate
the ability to
search
scientific
literature and
work on a
specific
problem.
Did students
6 Credits for
thesis or an
additional 6
credits of STA
courses at the
700 level.
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
Demonstrate the
ability to search
scientific literature
and work on a
specific problem ?
Instructor/
Instructor
Exams
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Demonstrate
the ability to
successfully
present results
in both oral
and written
formats.
Did students
Or
Demonstrate
Written
Comprehensive
Examination:
Based on
degree
requirements.
Did students
demonstrate the
ability to
successfully
present results in
both oral and
written formats?
Demonstrate
Written
Comprehensive
Examination:
Based on degree
requirements?
Instructor/
Instructor
Instructor/
Instructor
Final
Examination:
This will be
either an
examination to
defend Thesis
Defense:
Demonstrate
the ability to
successfully
present results
in both oral
and written
formats.
Or
Written
Comprehensive
Examination:
Based on
degree
requirements.
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
End of
semester and
In class
Assessment
Coordinator
Grade of B
or better
Grade of Aor better
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4. Process for Program Improvement and Dissemination. When, where, and how will results be disseminated to stakeholders?
Every semester the results will be disseminated to stakeholders.
Identify person(s) responsible for reviewing results and making
recommendations
Chair of the Department of Mathematical Sciences
How will assessment results be disseminated to stakeholders?
University website for Provost
updated 6 July 2008
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