Applied Statistics

The ability to analyze and use such data requires a new set of skills that an Applied Statistics major offers.

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.

Students who desire to major in applied statistics may be broadly classified into four groups:

  1. Those whose interest lie primarily in the study of mathematical statistics as a science, the purpose of such students being usually to continue their studies at the graduate level in order to become teachers at the college level, or persons otherwise engaged in an occupation in which knowledge of advanced statistics is required.
  2. Those whose interests lie in the fields of engineering, biology, chemistry, economics, physics, with emphasis on applied statistics.
  3. Those who wish to integrate their program between statistics and related fields of public health, and the health and social sciences.
  4. Those whose interests lie in the field of economics and the actuarial sciences.

Prerequisites to the Major

Students majoring in Applied Statistics must take the following Prerequisites:

MATH 113Calc I for Biology & Life Sci4
or MATH 115 Calculus I
MATH 114Calc II for Biology & Life Sci4
or MATH 116 Calculus II
MATH 217Intro to Matrix Algebra2-3
or MATH 227 Introduction to Linear Algebra
Total Credit Hours10-11

Dearborn Discovery Core Requirement

The minimum passing grade for a Dearborn Discovery Core (DDC) course is 2.0. The minimum GPA for the program is 2.0. In addition, the DDC permits any approved course to satisfy up to three credit hours within three different categories. Please see the General Education Program: The Dearborn Discovery Core section for additional information.

Foundational Studies

Written and Oral Communication (GEWO) – 6 Credits

Upper Level Writing Intensive (GEWI) – 3 Credits

Quantitative Thinking and Problem Solving (GEQT) – 3 Credits

Critical and Creative Thinking (GECC) – 3 Credits

Areas of Inquiry

Natural Science (GENS) – 7 Credits

  • Lecture/Lab Science Course
  • Additional Science Course

Social and Behavioral Analysis (GESB) – 9 Credits

Humanities and the Arts (GEHA) – 6 Credits

Intersections (GEIN) – 6 Credits

Capstone

Capstone (GECE) – 3 Credits

Foreign Language Requirement

Complete a two-semester beginning language sequence.

Ancient Greek I and II MCL 105 and MCL 106
Arabic I and II ARBC 101 and ARBC 102
Armenian I and II MCL 111 and MCL 112
French I and II FREN 101 and FREN 102
German I and II GER 101 and GER 102
Latin I and II LAT 101 and LAT 102
Spanish I and II SPAN 101 and SPAN 102

Major Requirements

24 credit hours at the 300+ level is required.

Mathmatics Core
MATH 325Probability3
MATH 425Mathematical Statistics3
Applied Statistics Core
Select 12 hours from the following:12
Biostatistics I
Applied Statistics I
Applied Statistics II
Applied Regression Analysis
Design and Analysis of Expermt
Electives in Statistics
Select any two upper level STAT courses:6
Cognates
Select 6 credit hours from the following:6
Quantitative Model and Anlys I
Quantitative Model and Anly II
Experimental Economics
Introduction to Econometrics
Six Sigma & Stat Proc Improv
Linear Algebra
Stochastic Processes
Advanced Calculus I
Other courses by Petition. See the Applied Statistics Program Advisor.
Total Credit Hours30

Notes:

  1. At least 12 of the 24  upper level credit hours in Statistics (STAT) must be elected at UM-Dearborn
  2. Students cannot receive credit for both STAT 301 and STAT 325.
  3. Students wishing to use graduate level courses (STAT 500+) as part of the 24 credit hours required for the major must submit a Petition to obtain the approval of the Applied Statistics Program Advisor.

Minor or LIBS Concentration

A minor or concentration consists of 12 credit hours of upper level courses (300 or above level) in Applied Statistics (STAT). Only one of STAT 301 or STAT 325  can be used to satisfy this requirement. Students with majors in mathematics, the natural sciences, or the social sciences may find the minor in Applied Statistics to be a valuable supplement to their major.

STAT 263     Introduction to Statistics     3 Credit Hours

Frequency distributions and descriptive measures. Populations, sampling, and statistical inference. Elementary probability and linear regression, use of statistical computer packages to analyze data. Students intending to elect this course should have taken at least one year of high school algebra. (F,W,S).

STAT 301     Biostatistics I     3 Credit Hours

Samples and populations, quantitative vs. categorical data; clinical vs. epidemiological studies; comparative displays and analysis; linear regression. Estimation of effect size is emphasized along with the P-value for a statistical test: difference of means in simple comparative data together with a confidence interval and t-test; relative risk for appropriate categorical data; slope of a regression line together with a confidence interval and t-test. Study design is emphasized: clinical trials in experimental settings; case-control and cohort studies in epidemiological settings. Students are expected to make presentations interpreting and reporting the results of research from the literature. Students can receive credit for only one of MATH 301, MATH 363, STAT 301, CRJ 383, SOC 383, STAT 325.

Prerequisite(s): MATH 113 or MATH 115

STAT 305     Intro. to Data Science     3 Credit Hours

WIth increasing availability of data, companies, governments, and nonprofits alike are striving to convert this data into knowledge and insight. This course will provide students with the basic skill set needed to handle such data. The course will focus on three broad areas- inferential thinking, computational thinking, and real-word applications. We will discuss data collection, data cleaning and exploratory analysis of data so that students can connect the data to the underlying phenomena and be able to think critically about the conclusions that are drawn from the data analysis. The students will also learn how to write short programs to be able to automate the data analysis process developing an applied understanding of different analytics methods, including linear regression, logistic regression, clustering, data visualization, etc. Most of the material will be taught using real world data. (YR)

STAT 325     Applied Statistics I     3 Credit Hours

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).

Prerequisite(s): MATH 113 or MATH 115 or MPLS with a score of 116

STAT 326     Applied Statistics II     3 Credit Hours

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, chi- square, 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. (W).

Prerequisite(s): STAT 325

STAT 330     Intro to Survey Sampling     3 Credit Hours

An introduction to survey sampling techniques assuming only-aiimited 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 Credit Hours

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. (OC)

Restriction(s):
Can enroll if Level is Undergraduate

STAT 430     Applied Regression Analysis     3 Credit Hours

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.

Prerequisite(s): STAT 425 or STAT 326

STAT 440     Design and Analysis of Expermt     3 Credit Hours

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. (W).

Prerequisite(s): STAT 425 or STAT 326

STAT 450     Multivariate Stat Analysis     3 Credit Hours

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.

Prerequisite(s): STAT 430

STAT 455     Environmental Statistics     3 Credit Hours

The primary objective of the course is to introduce statistical techniques to make data driven decisions to students majoring in the environmental and biological sciences. This course aims to nurture the importance of statistical methods to enhance the understanding of issues related to environmental sciiences. A one-semester course cannot be exhaustive in depth and width of literature but the aim of this course is to create interest and encourage students to delve more into the subject. (AY)

Restriction(s):
Can enroll if Level is Undergraduate

STAT 460     Time Series Analysis     3 Credit Hours

An-Introduction to time series, including trend effects and seasonality, while assuming only a limited knowledge of higher-level 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.

Prerequisite(s): STAT 430

STAT 490     Topics in Applied Statistics     3 Credit Hours

STAT 490A     Topics in Applied Statistics     3 Credit Hours

TOPIC TITLE: Multivariate Statistical Analysis A coverage of 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, discriminant analysis, and cluster analysis.

 

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally