The Department of Mathematics and Statistics offers a B.A. / B.S. degree in Applied Statistics.
This page lists the requirements for students seeking an undergraduate concentration in Applied Statistics. For a more detailed listing of course requirements please go to CASL Advising and Records or talk with the Math & Stat Undergraduate Advisor.
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Statistics is the science of learning from data. It includes planning for the collection of data, managing data, analyzing, interpreting, and drawing conclusions from data, and identifying problems, solutions and opportunities using the analysis. Massive amounts of data are being collected from digital applications and mobile devices in addition to those from the fields of engineering, environment, finance, healthcare, retail, and social sciences. The volume, variety and velocity of this data poses unique opportunities and challenges. The ability to analyze and use such data requires a new set of skills that an Applied Statistics major offers. This makes Applied Statistics one of the fastest growing career fields today. The Applied Statistics major builds critical thinking and problem solving skills in data analysis and empirical research. It prepares students for careers in business, industry, and government as well as for advanced degree programs in statistics and quantitative fields. The applied statistics major allows students to focus on their passions including genetics, healthcare, pharmaceuticals, public transportation, automotive areas, communication systems, financial markets, utilities, public policy, public health, government, manufacturing, quality control and others.
The Statistics Major consists of 30 credit hours with Calc I & II required. Degree-seeking students must fulfill the required courses in effect at the time of admittance or re-admittance to the program. Since these are subject to change, students should see an advisor for current requirements.
The major includes a core set of courses in Applied Statistics, Statistical Theory, and Computational Statistics.
A student can expect to take a variety of the below statistic courses.
STAT 325 Applied Statistics I
3.000 Credits Prerequisites: MATH 113 or MATH 115 or MPLS 116 A study of the fundamental concepts and methods of probability and statistics. Topics include counting problems, discrete probability, random variables and probability distributions, special distributions, sampling distributions, the central limit theorem, introduction to hypothesis testing, and the use of statistical computer packages for data analysis. Students can receive credit for only one of MATH 363, STAT 363, SOC 383 and STAT 325. (F,W).
STAT 326 Applied Statistics II
3.000 Credits Prerequisites: STAT 325 A continuation of STAT 325. This course treats both the principles and applications of statistics. Elementary theory of estimation and hypothesis testing, the use of the normal, chisquare, F and t distributions in statistics problems will be covered. Other topics are selected from regression and correlation, the design of experiments, analysis of variance, analysis of categorized data, nonparametric inference, and sample surveys.
STAT 330 Intro to Survey Sampling
3.000 Credits An introduction to survey sampling techniques assuming onlyaiimited knowledge of higher- level mathematics. Topics include: simple and stratified random sampling, estimation, systematic sampling, simple and two stage cluster sampling, population size estimation.
STAT 390 Topics in Applied Statistics
3.000 Credits Must be enrolled in one of the following Levels: Undergraduate A course designed to offer selected topics in applied statistics. The specific topic or topics will be announced together with the prerequisites when offered. Course may be repeated for credit when specific topics differ.
STAT 430/530 Applied Regression Analysis
3.000 Credits Prerequisites: STAT 425 Topics include single variable linear regression, multiple linear regression and polynomial regression. Model checking techniques based on analysis of residuals will be emphasized. Remedies to model inadequacies such as transformations will be covered. Basic time series analysis and forecasting using moving averages and autoregressive models with prediction errors are covered. Statistical packages will be used. Students cannot receive credit for both STAT 430 and STAT 530.
STAT 440 Design and Analysis of Expermt
3.000 Credits Prerequisites: STAT 425 An introduction to the basic methods of designed experimentation. Fixed and random effects models together with the analysis of variance techniques will be developed. Specialized designs including randomized blocks, latin squares, nested, full and fractional factorials will be studied. A statistical computer package will be used.
STAT 450 Multivariate Stat Analysis
3.000 Credits Prerequisites: STAT 430 An introduction to commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. Topics include: multivariate analysis of variance, multivariate regression, principal components and factor analysis, canonical correlation, and discriminant analysis.
STAT 460/560 Time Series Analysis
3.000 Credits Prerequisites: STAT 430 An-Introduction to time series, including trend effects and seasonality, while assuming only a limited knowledge of higherlevel mathematics. Topics include: linear Gaussian processes, stationarity, autocovariance and autocorrelation; autoregressive (AR), moving average (MA) and mixed (ARMA) models for stationary processes; likelihood in a simple case such as AR(1); ARIMA processes, differencing, seasonal ARIMA as models for non-stationary processes; the role of sample autocorrelation, partial autocorrelation and correlograms in model choice; inference for model parameters; forecasting: dynamic linear models and the Kalman filter.
STAT 535 Data Analysis and Modeling
3.000 Credits Linear models including models with factors associated with both fixed and random effects together with covariates. We will discuss models containing more complex covariance structure including repeated measures and time dependence. The major topics covered are generalized Linear Model, Generalized Linear Mixed Models and Generalized Estimating Equations. Statistical Computing Software’s such as R or SAS will be used extensively in examples, assignments and projects.
STAT 545 Reliability & Survival Analysis
3.000 Credits Parametric and nonparametric modeling of reliability data (from industrial experiments) and survival data (from biological experiments) will be discussed. This includes models where the data may be from the Weibull, log-normal, or the gamma distribution including nonparametric proportional hazards model and Cox regression. Major topics include Survival function of Proportional Hazard Regression Model, Best Subsets and Multivariate Fractional Polynomial Models, recurrent event models and Competing Risk Models. Statistical Computing Software’s such as R or SAS will be used extensively in examples, assignments and projects.
STAT 590 Topics in Applied Statistics
3.000 Credits A course designed to offer selected topics in applied statistics. The specific topic will be announced together with the prerequisites when offered. Course may be repeated for credit when specific topic differs.
STAT 597 Ind Studies in Statistics
1.000 TO 3.000 Credits Independent Study in statistics for topics at the graduate level. Topics and objectives chosen by agreement between students and instructor.
MATH 325 Probability
3.000 Credits Prerequisites: MATH 114 or MATH 116 Brief overview of summary and display of data, probability concepts, discrete and continuous random variables and associated probability models, expectation, independent random variables, probability generating functions and moment generating functions, sampling distributions, the central limit theorem, the t-distribution, properties of estimators, and interval estimation. Previously taught as Mathematical Statistics I.
MATH 425 Mathematical Statistics
3.000 Credits Prerequisites: MATH 325 Interval estimation and pivotal quantities, maximum likelihood estimation, hypothesis tests, linear models and analysis of variance, bivariate normal distribution, regression and correlation analysis, and nonparametric methods. Students cannot receive credit for both MATH 425 and MATH 525. Previously taught as Mathematical Statistics II.