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STAT 153 Probability I (3-2) 4
Sample space, events. Basic combinatorial probability,
conditional probability. Bayes’ theorem, independence, random
variables, distributions, expectation.
STAT 154 Probability II (3-2) 4
Transformations of random variables, generating functions,
conditional expectation. Limit theorems, central limit
theorem, limiting distributions.
Prerequisite: STAT 153, MATH 119
STAT 155 Principles of Statistics (3-2)
4
Brief history of statistics. Basic definitions and types of
data, descriptive statistics. Elementary probability, random
variables, probability distributions and their
properties. Introduction to use of computer solving
tools.
STAT 156 Statistical Methods (3-2) 4
Sampling distributions, estimation, confidence intervals,
hypothesis testing, power of test, analysis of variance for
one or two factor designs, linear regression, basic
nonparametric procedures. Elementary time series
analysis, trends, seasonality, forecasting.
Prerequisite: STAT 155
STAT 201 Introduction to Probability and Statistics
I (3-0) 3
Experiments and events. Set theory. Axioms and basic
theorems of probability. Finite sample spaces and counting
techniques. Independent events. Conditional probability.
Random variables and distributions. Expectation, variance,
covariance and correlation. Some special distributions.
STAT 202 Introduction to Probability and Statistics
II (3-0) 3
Random samples. Sample mean and variance. Chebychev's
inequality. Law of large numbers. Central limit theorem.
Estimation. Maximum likelihood, unbiased, minimum variance
unbiased, consistent and efficient estimators. Sufficiency.
Confidence intervals. Hypothesis testing. Introduction to
nonparametric methods. Regression and analysis of variance.
Prerequisite: STAT 201
STAT 221 Statistics for Engineers I (3-0) 3
Introduction to probability. Finite sample spaces.
Conditional probability and independence. Discrete and
continuous random variables. Random sample and statistics.
Statistical inference, estimation and tests of hypotheses.
Simple linear regression.
Prerequisite: MATH 120
STAT 256 Numerical Methods (3-2) 4
Accuracy in numerical computations. Numerical solution of
linear and nonlinear algebraic equations. Finding eigen-values
and eigenvectors. Finite difference calculus. Interpolation
and extrapolation. Numerical differentiation and integration.
Numerical approximation methods.
Prerequisite: STAT 291 or STAT 292, MATH 260
STAT 271 Mathematical Statistics I (3-2)
4
Common theoretical distributions. Sampling
distributions. Principles of point estimation.
Techniques of estimation .Properties of point estimators.
Optimality criteria in estimation. Selected topics from robust
inference. Bayesian inference.
Prerequisite: STAT 154, MATH 120
STAT 272 Mathematical Statistics II
(3-2) 4
Region (interval) estimation. Hypothesis testing.
Optimality properties for hypothesis testing. Likelihood ratio
tests. Sequential tests.
Prerequisite: STAT 271
STAT 290 Probability Theory (3-0) 3
Sigma-algebra of events. Probability measure. Axioms of
probability. Conditional probability and independence.
Combinatorial problems. Random variables and their
distributions. Functions of random variables. Distribution
functions. Expectation. Conditional expectation. Moments and
characteristic functions. Convergence of random variables.
Central limit theorem. Laws of large numbers.
Prerequisite: STAT 154
STAT 291 Statistical Computing I (3-2)
4
Introduction to statistical techniques in statistical
software. Managing and analyzing data using statistical
database packages. Introduction to MATLAB with applications to
matrix algebra.
Prerequisite: CENG 230, STAT 156.
STAT 292 Statistical Computing II (3-2)
4
Introduction to programming and computation. Introduction
to computer organization and basic data structures. An
advanced programming language with applications to statistical
procedures.
Prerequisite: CENG 230
STAT 356 Statistical Data Analysis
(3-2) 4
Types of data. Graphical and tabular represantation of
data. Approaches to finding the unexpected in data.
Exploratory data analysis for large and high-dimensional data.
Analysis of categorical data. Elements of robust estimation.
Handling missing data. Smoothing methods. Data mining.
Prerequisite: STAT 156, STAT 291
STAT 361 Computational Statistics
(3-2) 4
Random number generation. Generating from other
distributions. Monte Carlo methods for inferential statistics. Resampling.
Data partitioning. Cross-validation. Bootstraping.
Jackknifing. Tools for exploratory and graphical data
analysis. Nonparametric probability density estimation.
Prerequisite: STAT 291
STAT 363 Linear Models I (3-2)
4
Simple and Multiple Linear Regression
Models. Estimation , interval estimation
and test of hypothesis on the parameters of the models. Model
Adequecy Checking. Multicollinearity.
Transformation.
Prerequisite: MATH 260, STAT
156
STAT 364 Linear Models II (3-2)
4
Simple nonlinear models. Less than full rank
models : One- way , Two-way ANOVA models, Split Plot
Design, Random Effect Models, Analysis
of Covariance Model. Introduction to generalized linear
models.
Prerequisite: STAT 363
STAT 365 Survey Sampling Techniques (3-2)
4
Introduction to survey sampling. Probability sampling
techniques. Simple random sampling. Stratified element
sampling. Systematic sampling. Equal sized cluster sampling.
Unequal sized cluster sampling. PPS selection techniques.
Sampling errors.
Prerequisite: STAT 156 or equivalent for non-statistics
majors.
STAT 366 Survey Research Methods (3-0)
3
Introduction to survey research. Survey research methods.
Planning of sample surveys. Survey designs. Methods of data
collection. Questionnaire design techniques. Fieldwork
organization methods. Survey designs over time. Multiplicity
survey designs. Establishment survey designs. Components of
total survey error. Survey research project.
Prerequisite: STAT 365 or consent of department for
non-statistics majors.
STAT 376 Stochastic Processes (3-2)
4
Random walk. Markov chains, martingales. Discrete and
continuous parameter Markov processes. Branching
processes. Birth and death processes. Renewal processes.
Queuing processes. Applications.
Prerequisite: STAT 290
STAT 457 Statistical Design of Experiments
(3-2) 4
Strategies for experimentation. Randomized complete and
balanced incomplete block designs. Latin squares. General,
two-level, and fractional factorials. Blocking and confounding
in two-level factorials. Three and mixed level factorial and
fractional factorials. Introduction to response surface
methodology. Second-order experimental designs. Mixture
experiments. EVOP. Robust design. Nonnormal responses.
Unbalanced data in factorials. ANCOVA. Repeated measures.
Prerequisite: STAT 363
STAT 460 Nonparametric Statistics (3-0)
3
Review of basic statistics. Distribution-free statistics,
ranking statistics, U statistics. Large sample theory for U
statistics. Tests based on runs. Asymptotic relative
efficiency of tests. Hypothesis testing, point and interval
estimation. Goodness of fit, rank-order (for location and
scale), contingency table analysis and relevant models.
Measures of association, analysis of variance.
Prerequisite: STAT 272
STAT 461 System Simulation (3-2) 4
Introduction to discrete-event system simulation and
simulation software. Statistical models in simulation. Queuing
models. Input data modeling. Variance reduction techniques.
Verification and validation of simulation models. Output
analysis for a single model. Comparison and evaluation of
alternative system design.
Prerequisite: STAT 291
STAT 462 Biostatistics (3-2) 4
Populations and samples. Types of biological data. Data
transformations. Survival data analysis. Life tables. Sample
size determination in clinical trials. Measures of
association. The odds ratio and some properties. Application
of generalized linear models and logistic regression to
biological data. Analysis of data from matched samples.
Prerequisite: STAT 156
STAT 463 Reliability (3-0) 3
Reliability studies. Statistical failure models. Censoring
and truncation and their types. Useful limit theorems in
reliability. Inference procedures for lifetime distributions.
System reliability. Bayesian methods. Accelerated life
testing.
Prerequisite: STAT 272
STAT 464 Operations Research (2-2) 3
Basic operations research methodology. Basic models such as
network flow models, project scheduling, dynamic programming,
and production and inventory control. LP and game theory. Two
person zero-sum games and saddle points.
Prerequisite: MATH 260
STAT 465 Multivariate Analysis I (3-0)
3
Vectoral representation of multivariate data. Sample mean
vector and sample covariance matrix. Multivariate
distributions, multivariate normal distribution, some other
multivariate distributions. Parametric estimation. Hypothesis
testing. Reduction of dimensionality.
Prerequisite: MATH 260, STAT 156
STAT 466 Multivariate Analysis II (3-2)
4
MANOVA. Principal components, factor analysis. Multivariate
classification and clustering. Canonical correlation.
Prerequisite: STAT 465
STAT 472 Statistical Decision Analysis (3-2)
4
Introduction to decision making and types of decision
situations. Bayes theorem and Bayesian decision theory. Prior,
posterior and conjugate prior distributions. Loss functions.
Empirical Bayesian approach. Utility theory for decision
making. Value of information. Sequential decision procedures.
Multidecision problems.
Prerequisite: STAT 154
STAT 477 Statistical Quality Control (2-2)
3
Introduction to concepts of quality and total quality
management. Basic principles of teamwork and
learning. Probability in Quality Control. Methods and
Philosophy of Statistical Process. Control Charts for
variables and attributes. Cumulative-Sum and Exponentially
Weighted Moving-Average Control Charts. Process
Capability Analysis. Introduction to Experimental Design
and Factorial Experiments. Taguchi Method, Lot-by-Lot
Acceptance Sampling for attributes and by variables.
Prerequisite: STAT 156
STAT 479 Linear Programming (2-2) 3
Introduction to Linear Programming (LP). The simplex
method. Transportation, assignment and transshipment problems.
Sensitivity testing, duality theory and its applications.
Advanced methods in LP and revised simplex algorithm.
Prerequisite: MATH 260
STAT 480 Application of Statistical Techniques in
Socio-Economic Research (3-2) 4
Prinicipals of empirical socio-economic research.
Formulation of research problems, determination of research
design, application of sampling design. Strategies of field
work, collection of data, improving data quality, selecting
appropriate statistical methods. Evaluation of test of
hypothesis and interpretation of findings. Preparation and
presentation of a research proposal and report.
Prerequisite: STAT 356
STAT 482 Categorical Data Analysis (3-2)
4
Probability distributions and measures of association for
count data. Inferences for two-way contingency tables.
Generalized linear models, logistic regression and loglinear
models. Models with fixed and random effects for categorical
data. Model selection and diagnostics when response is
categorical. Classification trees.
Prerequisite: STAT 272
STAT 487 Insurance and Actuarial Analysis (3-0)
3
Basic definition of insurance. Historical background.
Insurance applications in government and private sector,
regulations and legislation in insurance. Fundamentals of
insurance. Types of insurance, disaster insurance and risk
menagement applications around the world. Turkish catastrophe
insurance pool. Definition of risk, probability aspect of
risk. Utility theory, claim processes, distribution of
claim processes.
Prerequisite: Consent of the department.
STAT 493 New Horizons in Statistics (3-0)
3
New advances in the field of statistics.
STAT 495 Applications in
Statistics (2-2) 3
Applications of different statistical methods in various
disciplines such as medicine, science, engineering and social
sciences. Presentation of projects involving these
applications as group studies.
Prerequisite: STAT 156
STAT 497 Applied Time Series Analysis (3-2)
4
Time series as a stochastic process. Means, covariances,
correlations, stationarity. Moving averages and
smoothing. Stationary and nonstationary parametric
models. Model specification. Estimation and
testing. Seasonality. Some forecasting procedures. Elementary
spectral domain analysis.
Prerequisite: STAT 272
STAT 499 Undergraduate Research (1-4)
3
This course is intended to improve the research
capabilities of graduating students. Each student will be
given a project and an academic advisor, lectures will be
given on research design, data evaluation and report writing.
A final report and/or seminar is required at the end of the
semester.
Prerequisite: Consent of the department.
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