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.
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 Statistical Decision Theory: Decision rules, loss functions, risk functions, decision principles, utility theory, Bayesian estimators, hypothesis testing, Bayesian sequential analysis.
Or
(b)
STA 751 Spatial Statistics: Stochastic process, first and second order stationarity, intrinsic hypothesis, models of spatial dependence, different forms of Kriging, bicubic splines, conditional simulation.
STA 769 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.
2. Curriculum Alignment of Student Learning Outcomes. Where is the information introduced, enriched, and/or reinforced in the courses required in the program?
Program Outcome Goals / MAT 657 / MAT 663 / STA 667 / STA 767 / Area of Specialization: 6 credits
a. STA 763, 765
b. STA 751, 769
Demonstrate strongly knowledge of applied statistics / I / I, E / I / I, E / I
Analyze strongly applied statistics concepts / I / I, E / I / I, E / I
Do master problem-solving in applied statistics / I / I, E / I / I, E / I
Do master applied statistics problems / I / I, E / I / I, E / I
I = Introduced E = Enhanced R = Reintroduced
Required Courses
Program Outcome Goals / 6 credits for thesis or an additional 6 credits at 700 level. / Final Examination: either to defend the thesis or a written comprehensive examination
Demonstrate strongly knowledge of applied statistics / I, E
Analyze strongly applied statistics concepts / I, E
Do master problem-solving in applied statistics / I, E
Do master applied statistics problems / I, E
I = Introduced E = Enhanced R = Reintroduced
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?
A student at completion of the degree will be able to demonstrate completing in: / Assessment Questions
Did students master? / Person responsible for instrument development/ Who will administer instruments and collect data / Instrument / When and where will data be collected
End of semester and
In class / Person responsible for data analysis and report
Farrokh Saba
Assessment Coordinator / Expected Measures (mean & standard deviation), component analysis, percentage of agreement or strongly agree, percentage who meet of exceed benchmark / Benchmark
Grade of A- or better
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 / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Advanced Matrix Theory and Applications.
Ortthogonal matrices, Gram-Schmidt method, Q_R factorization, least-square fits, eigenvalues and eigen vectors, Markov processes, simplex method. / Did students master Ortthogonal matrices, Gram-Schmidt method, Q_R factorization, least-square fits, eigenvalues and eigen vectors, Markov processes, simplex method? / Instructor/
Instructor / MAT 663
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Introductory to statistical inference, discrete and continuous probability, probability models, estimations, Bayesian estimation, confidence intervals, hypothesis testing.
applications. / 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 / STA 667
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Mathematical Statistics: Basic probability theory, conditional probability. / Did students master Basic probability theory, conditional probability? / Instructor/
Instructor / STA 767
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
a.
Regression and Multivariate Analysis: Matrix theory, examining residuals, multiple regression, nonlinear regression, multivariate normal, variance-covariance matrix, canonical correlation, distribution of characteristic. / a.
Did students master Matrix theory, examining residuals, multiple regression, nonlinear regression, multivariate normal, variance-covariance matrix, canonical correlation, distribution of characteristic? / Instructor/
Instructor / STA 763
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Statistical Decision Theory: Decision rules, loss functions, risk functions, decision principles, utility theory, Bayesian estimators, hypothesis testing, Bayesian sequential analysis. / Did students master Decision rules, loss functions, risk functions, decision principles, utility theory, Bayesian estimators, hypothesis testing, Bayesian sequential analysis? / Instructor/
Instructor / STA 765
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or 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
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 / STA 751
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
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 Multivariate Methods, testing for multivariate normality, multivariate control charts, exploratory data analysis, cluster analysis, factor analysis, and multivariate calibration problems? / Instructor/
Instructor / STA 769
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Demonstrate the ability to search scientific literature and work on a specific problem. / Did students Demonstrate the ability to search scientific literature and work on a specific problem ? / Instructor/
Instructor / 6 Credits for thesis or an additional 6 credits of STA courses at the 700 level.
Exams / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Demonstrate the ability to successfully present results in both oral and written formats. / Did students
demonstrate the ability to successfully present results in both oral and written formats? / 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. / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
Or
Demonstrate
Written Comprehensive Examination: Based on degree requirements. / Did students Demonstrate
Written Comprehensive Examination: Based on degree requirements? / Instructor/
Instructor / Or
Written Comprehensive Examination: Based on degree requirements. / End of semester and
In class / Assessment Coordinator / Grade of B or better / Grade of A- or better
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 / How will assessment results be disseminated to stakeholders?Chair of the Department of Mathematical Sciences / University website for Provost
updated 6 July 2008
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