# Under Review

The 2023-2024 requirements are not yet finalized and are subject to change so long as this notice is in place. While the information presented is generally reliable, you should confirm the information later this summer when requirements are finalized.Department of Statistics

# Minor in Statistics

Students on Summer 2021, Fall 2021, or Spring 2022 requirements STATMIN

The minor in statistics provides students the opportunity to pursue formal studies in statistics as a complement of their chosen major. The minor explores fundamental statistical concepts and basic statistical techniques well beyond what is learned in a single introduction to statistics course. Students completing the minor will gain valuable expertise in statistics and a potential advantage when competing for jobs.

## Requirements

The minor requires at least 15 credit hours (19 with the Addenda requirement), including the requirements listed below.

**Introductory Course.**One (1) of the following options:**Statistical Literacy.**One (1) course:- STAT-S 100 Statistical Literacy
- STAT-H 100 Statistical Literacy, Honors

# STAT-S 100 Statistical Literacy

- Credits
- 3
- Prerequisites
- None
- Notes
- R: Mastery of high school algebra; or MATH-M 014
- Description
- How to be an informed consumer of statistical analysis. Experiments and observational studies, summarizing and displaying data, relationships between variables, quantifying uncertainty, drawing statistical inferences.
- Repeatability
- Credit given for only one of STAT-H 100 or STAT-S 100.

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# STAT-H 100 Statistical Literacy, Honors

- Credits
- 3
- Prerequisites
- Consent of Hutton Honors College
- Notes
- R: Mastery of high school algebra; or MATH-M 014
- Description
- How to be an informed consumer of statistical analysis. Experiments and observational studies, summarizing and displaying data, relationships between variables, quantifying uncertainty, drawing statistical inferences.
- Repeatability
- Credit given for only one of STAT-H 100 or STAT-S 100.

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**Introduction to Statistics.**One (1) course:- STAT-K 310 Statistical Techniques
- STAT-S 211 Statistics for Journalists
- STAT-S 300 Introduction to Applied Statistical Methods
- STAT-S 301 Applied Statistical Methods for Business
- STAT-S 303 Applied Statistical Methods for the Life Sciences
- ANTH-A 306 Anthropological Statistics
- CJUS-K 300 Techniques of Data Analysis
- ECON-E 370 Statistical Analysis for Business and Economics
- ECON-S 370 Statistical Analysis for Business and Economics: Honors
- MATH-E 265 Introduction to Probability and Statistics for Data Science
- MATH-M 365 Introduction to Probability and Statistics
- POLS-Y 395 Quantitative Political Analysis
- PSY-K 300 Statistical Techniques
- PSY-K 310 Statistical Techniques
- SOC-S 371 Statistics in Sociology
- SPEA-K 300 STATISTICAL TECHNIQUES
- SPH-Q 381 INTRODUCTION TO BIOSTATISTICS

# STAT-K 310 Statistical Techniques

- Credits
- 3
- Prerequisites
- MATH-M 119 or equivalent
- Description
- Introduction to probability and statistics. Elementary probability theory, conditional probability, independence, random variables, discrete and continuous probability distributions, measures of central tendency and dispersion. Concepts of statistical inference and decision: estimation, hypothesis testing, Bayesian inference, statistical decision theory. Special topics discussed may include regression and correlation, time series, analysis of variance, nonparametric methods.
- Repeatability
- Credit given for only one of ANTH-A 306, CJUS-K 300, ECON-E 370, ECON-S 370, MATH-K 300, MATH-K 310, POLS-Y 395, PSY-K 300, PSY-K 310, SOC-S 371, SPEA-K 300, SPH-Q 381, STAT-K 310, STAT-S 300, STAT-S 301, or STAT-S 303.

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# STAT-S 211 Statistics for Journalists

- Credits
- 3
- Prerequisites
- None
- Notes
- R: Mastery of high school algebra; or MATH-M 014
- Description
- Essential statistical concepts and tools for journalists in the age of data, including probability, graphics, descriptive statistics, prediction, study design, comparison, testing, and estimation. The course has a heavier emphasis on writing and reading media reports than other introductory statistics courses.

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# STAT-S 300 Introduction to Applied Statistical Methods

- Credits
- 4
- Prerequisites
- None
- Notes
- R: Mastery of high school algebra; or MATH-M 014. Lecture and laboratory
- Description
- Introduction to methods for analyzing quantitative data. Graphical and numerical descriptions of data, probability models of data, inference about populations from random samples. Regression and analysis of variance.
- Repeatability
- Credit given for only one of ANTH-A 306, CJUS-K 300, ECON-E 370, ECON-S 370, MATH-K 300, MATH-K 310, POLS-Y 395, PSY-K 300, PSY-K 310, SOC-S 371, SPEA-K 300, SPH-Q 381, STAT-K 310, STAT-S 300, STAT-S 301, or STAT-S 303.

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# STAT-S 301 Applied Statistical Methods for Business

- Credits
- 3
- Prerequisites
- Math-M 118 or equivalent
- Description
- Introduction to methods for analyzing data arising in business, designed to prepare business students for the Kelley School\'s Integrative Core. Graphical and numerical descriptions of data, probability models, fundamental principles of estimation and hypothesis testing, applications to linear regression and quality control. Microsoft Excel used to perform analyses.
- Repeatability
- Credit given for only one of ANTH-A 306, CJUS-K 300, ECON-E 370, ECON-S 370, MATH-K 300, MATH-K 310, POLS-Y 395, PSY-K 300, PSY-K 310, SOC-S 371, SPEA-K 300, SPH-Q 381, STAT-K 310, STAT-S 300, STAT-S 301, or STAT-S 303.

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# STAT-S 303 Applied Statistical Methods for the Life Sciences

- Credits
- 3
- Prerequisites
- None
- Notes
- R: Mastery of high school algebra; or MATH-M 014
- Description
- Introduction to methods for analyzing data arising in the life sciences, designed for biology, human biology, and pre-medical students. Graphical and numerical descriptions of data, probability models, fundamental principles of estimation and hypothesis testing, inferences about means, correlation, linear regression.
- Repeatability

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# ANTH-A 306 Anthropological Statistics

- Credits
- 3
- Prerequisites
- None
- Description
- Fundamentals of univariate and bivariate statistics, construction and interpretation of graphs, and computer-assisted data analysis. Both statistical methodology and theory will be emphasized as well as computer literacy. Students will examine the primary literature in all branches of anthropology to familiarize themselves with the role of statistics in anthropological research.
- Repeatability

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# CJUS-K 300 Techniques of Data Analysis

- Credits
- 3
- Prerequisites
- None
- Notes
- R: To be successful in this course, students should have an understanding of basic algebra.
- Description
- CJUS-K 300 covers the properties of single variables, the measurement of association between pairs of variables, and statistical inference. Additional topics, such as the analyses of qualitative and aggregated data, address specific criminal justice concerns.
- Repeatability

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# ECON-E 370 Statistical Analysis for Business and Economics

- Credits
- 3
- Prerequisites
- MATH-M 118, MATH-S 118, or MATH-V 118
- Notes
- R: ECON-E 252 or ECON-B 252 and MATH-M 119
- Description
- Lectures emphasize the use of basic probability concepts and statistical theory in the estimation and testing of single parameter and multivariate relationships. In computer labs, using Microsoft Excel, each student calculates descriptive statistics, probabilities, and least squares regression coefficients in situations based on current business and economic events.
- Repeatability

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# ECON-S 370 Statistical Analysis for Business and Economics: Honors

- Credits
- 3
- Prerequisites
- MATH-M 118, MATH-S 118, or MATH-V 118; and Hutton Honors student
- Notes
- R: MATH-M 119 and ECON-E 252 or ECON-B 252
- Description
- Honors course. Designed for students of superior ability. Covers same core material as ECON-E 370.
- Repeatability

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# MATH-E 265 Introduction to Probability and Statistics for Data Science

- Credits
- 3
- Prerequisites
- MATH-M 211, CSCI-C 200, or CSCI-A 201
- Description
- Covers elementary concepts of probability and statistics for data science, including combinatorics, conditional probability, independence, random variables, discrete and continuous distributions, moments, statistical inference, point estimation, quantiles, confidence intervals, test of hypotheses, and related topics. Introduces the application of data analysis to the social and natural sciences.

# MATH-M 365 Introduction to Probability and Statistics

- Credits
- 3
- Prerequisites
- MATH-M 212, MATH-M 213, or MATH-S 212
- Description
- Elementary concepts of probability and statistics. Combinatorics, conditional probability, independence, random variables, discrete and continuous distributions, moments. Statistical inference, point estimation, confidence intervals, test of hypotheses. Applications to social, behavioral, and natural sciences.
- Repeatability
- Credit given for only one of MATH-M 360 or MATH-M 365.

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# POLS-Y 395 Quantitative Political Analysis

- Credits
- 3
- Prerequisites
- None
- Description
- Introduction to methods and statistics used in political inquiry, including measures of central tendency and dispersion, probability, sampling, statistical inference and hypothesis testing, measures of association, analysis of variance, and regression.
- Repeatability

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# PSY-K 300 Statistical Techniques

- Credits
- 3
- Prerequisites
- One of MATH-M 106, MATH-M 118, MATH-M 119, MATH-M 211, MATH-M 212, MATH-S 211, MATH-S 212, MATH-V 118, or, MATH-V 119
- Description
- Introduction to statistics; nature of statistical data; ordering and manipulation of data; measures of central tendency and dispersion; elementary probability. Concepts of statistical inference and decision: estimation and hypothesis testing. Special topics include regression and correlation, analysis of variance, non-parametric methods.
- Repeatability

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# PSY-K 310 Statistical Techniques

- Credits
- 3
- Prerequisites
- One of MATH-M 106, MATH-M 118, MATH-M 119, MATH-M 211, MATH-M 212, MATH-S 211, MATH-S 212, MATH-V 118, or, MATH-V 119
- Description
- Introduction to probability and statistics; elementary probability theory, conditional probability, independence, random variables, discrete and continuous probability distributions, measures of central tendency and dispersion. Covers concepts of statistical inference and decision; estimation and hypothesis testing; Bayesian inference; and statistical decision theory. Special topics include regression and correlation, time series, analysis of variance, non-parametric methods.
- Repeatability

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# SOC-S 371 Statistics in Sociology

- Credits
- 3
- Prerequisites
- None
- Description
- Introduces the logic of statistical inference. Students will learn how to use sample data to reach conclusions about a population of interest by calculating confidence intervals and significance tests. Estimating the effects of multiple independent variables using cross-tabulations and/or regression.
- Repeatability

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# SPEA-K 300 STATISTICAL TECHNIQUES

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

# SPH-Q 381 INTRODUCTION TO BIOSTATISTICS

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

**Statistical Inference.**One (1) course:- STAT-S 320 Introduction to Statistics
- STAT-S 350 Introduction to Statistical Inference

# STAT-S 320 Introduction to Statistics

- Credits
- 3
- Prerequisites
- MATH-M 212, MATH-S 212, MATH-M 301, MATH-M 303, or MATH-S 303
- Description
- Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations using actual data sets from various disciplines.
- Repeatability
- Credit given for only one of STAT-S 320 or STAT-S 350.

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# STAT-S 350 Introduction to Statistical Inference

- Credits
- 3
- Prerequisites
- MATH-M 118, MATH-M 119, or equivalent; and at least one course in Statistics; or MATH-M 212 or MATH-S 212; or consent of instructor
- Description
- Formulation of statistical inference using probability models. Point estimation, hypothesis testing, and set estimation for various models, including 1-, 2-, and K-sample location problems, goodness-of-fit, correlation and regression.
- Repeatability
- Credit given for only one of STAT-S 320 or STAT-S 350.

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**Data Modeling and Inference.**One (1) course:- STAT-S 352 Data Modeling and Inference

# STAT-S 352 Data Modeling and Inference

- Credits
- 3
- Prerequisites
- STAT-S 320 or STAT-S 350; or consent of instructor
- Description
- Intermediate-level survey of resampling, likelihood, and Bayesian methods of statistical inference. Distributional models of various data types. Categorical, count, time-to-event, time series, linear models, and hierarchical regression models.

**Advanced Electives.**One (1) of the following options:**Statistics Electives.**Two (2) courses:- STAT-S 470 Exploratory Data Analysis
- STAT-S 431 Applied Linear Models I
- STAT-S 432 Applied Linear Models II
- STAT-S 420 Introduction to Statistical Theory

# STAT-S 470 Exploratory Data Analysis

- Credits
- 3
- Prerequisites
- STAT-S 352; or consent of instructor
- Description
- Techniques for summarizing and displaying data. Exploration versus confirmation. Connections with conventional statistical analysis and data mining. Application to large data sets.

# STAT-S 431 Applied Linear Models I

- Credits
- 3
- Prerequisites
- STAT-S 320 or STAT-S 350; and MATH-M 301, MATH-M 303, MATH-S 303, or STAT-S 352; or consent of instructor
- Description
- Part I of a two-semester sequence on linear models. This part presents the analysis of simple and multiple linear regression in the presence of simple and complex regressors. In addition, transformations, regression diagnostics, influence analysis, and regression shrinkage methods are introduced.

# STAT-S 432 Applied Linear Models II

- Credits
- 3
- Prerequisites
- STAT-S 431; or consent of instructor
- Description
- Part II of a two-semester sequence on linear models, emphasizing linear regression and the analysis of variance, including topics from the design of experiments and culminating in the general linear model.

# STAT-S 420 Introduction to Statistical Theory

- Credits
- 3
- Prerequisites
- STAT-S 320 or STAT-S 350; and MATH-M 463 or MATH-S 463; or consent of instructor
- Description
- Fundamental concepts and principles of data reduction and statistical inference, including the method of maximum likelihood, the method of least squares, and Bayesian inference. Theoretical justification of statistical procedures introduced in STAT-S 320.

**Statistics Elective + Free Elective.**Both of the following:**Statistics Elective.**One (1) course:- STAT-S 470 Exploratory Data Analysis
- STAT-S 431 Applied Linear Models I
- STAT-S 432 Applied Linear Models II
- STAT-S 420 Introduction to Statistical Theory

# STAT-S 470 Exploratory Data Analysis

- Credits
- 3
- Prerequisites
- STAT-S 352; or consent of instructor
- Description
- Techniques for summarizing and displaying data. Exploration versus confirmation. Connections with conventional statistical analysis and data mining. Application to large data sets.

# STAT-S 431 Applied Linear Models I

- Credits
- 3
- Prerequisites
- STAT-S 320 or STAT-S 350; and MATH-M 301, MATH-M 303, MATH-S 303, or STAT-S 352; or consent of instructor
- Description
- Part I of a two-semester sequence on linear models. This part presents the analysis of simple and multiple linear regression in the presence of simple and complex regressors. In addition, transformations, regression diagnostics, influence analysis, and regression shrinkage methods are introduced.

# STAT-S 432 Applied Linear Models II

- Credits
- 3
- Prerequisites
- STAT-S 431; or consent of instructor
- Description
- Part II of a two-semester sequence on linear models, emphasizing linear regression and the analysis of variance, including topics from the design of experiments and culminating in the general linear model.

# STAT-S 420 Introduction to Statistical Theory

- Credits
- 3
- Prerequisites
- STAT-S 320 or STAT-S 350; and MATH-M 463 or MATH-S 463; or consent of instructor
- Description
- Fundamental concepts and principles of data reduction and statistical inference, including the method of maximum likelihood, the method of least squares, and Bayesian inference. Theoretical justification of statistical procedures introduced in STAT-S 320.

**Free Elective.**One (1) course:- ECON-E 371 Introduction to Applied Econometrics
- ECON-E 392 Seminar in Computational Methods and Econometrics (approved topics only; see academic advisor)
- ECON-E 471 Econometric Theory and Practice I
- ECON-E 472 Econometric Theory and Practice II
- MATH-M 463 Introduction to Probability Theory I
- MATH-M 464 Introduction to Probability Theory II
- MATH-S 463 Honors Course in Probability Theory I
- PSY-P 404 Computer and Statistical Models in Psychology
- INFO-I 368 INTRODUCTION TO NETWORK SCIENCE
- INFO-I 421 APPLICATIONS OF DATA MINING
- INFO-I 422 DATA VISUALIZATION
- INFO-I 468 NETWORK SCIENCE APPLICATIONS
- CSCI-B 455 Principles of Machine Learning

# ECON-E 371 Introduction to Applied Econometrics

- Credits
- 3
- Prerequisites
- ECON-E 251 or ECON-B 251; and ECON-E 370 or ECON-S 370; and MATH-J 113, MATH-M 119, MATH-V 119, MATH-M 211, or MATH-S 211
- Description
- An introduction to the theory and application of least-squares regression in empirical economics. Review of bivariate and multivariate regression models, hypothesis testing, and confidence intervals. Special topics include model specification, multicollinearity, heteroscedasticity, dummy variables, interactions, and various sources of estimation bias. Students will learn to work with both cross-sectional and time-series datasets, and analyze the data using an econometrics software package.
- Repeatability
- Credit given for only one of ECON-E 371 or ECON-S 371.

# ECON-E 392 Seminar in Computational Methods and Econometrics

- Credits
- 3
- Prerequisites
- ECON-E 321 or ECON-S 321; Additional prerequisites may be required depending on the seminar topic
- Description
- Intensive study of a topic area in computational methods or econometrics. Topics will vary.
- Repeatability
- May be repeated with a different topic for a maximum of 9 credit hours.

# ECON-E 471 Econometric Theory and Practice I

- Credits
- 3
- Prerequisites
- ECON-E 370, ECON-S 370, or MATH-M 365; and MATH-M 301, MATH-M 303, or MATH-S 303; and MATH-M 311 or MATH-S 311
- Notes
- Only 9 credit hours from ECON-E 371, ECON-S 371, ECON-E 471, and ECON-E 472 may be counted toward a major in economics
- Description
- Emphasis is on the classical linear regression model and its applications. Special topics include finite and asymptotic properties of least squares, hypothesis testing, model specification, dummy variables, proxies, multicollinearity and heteroscedasticity.

# ECON-E 472 Econometric Theory and Practice II

- Credits
- 3
- Prerequisites
- ECON-E 471
- Notes
- Only 9 credit hours from ECON-E 371, ECON-S 371, ECON-E 471, and ECON-E 472 may be counted toward a major in economics
- Description
- Emphasizes extensions of the classical linear-regression model such as: limited dependent variables, instrumental variables, stationary and nonstationary data, fixed-effect and random-effect models, multiple-equation models, censored regression, and sample selection.

# MATH-M 463 Introduction to Probability Theory I

- Credits
- 3
- Prerequisites
- MATH-M 301, MATH-M 303, or MATH-S 303; and MATH-M 311 or MATH-S 311
- Description
- The meaning of probability. Random experiments, conditional probability, independence. Random variables, expected values and standard deviations, moment generating functions. Important discrete and continuous distributions. Poisson processes. Multivariate distributions, basic limit laws such as the central limit theorem.
- Repeatability
- Credit given for only one of MATH-M 463 or MATH-S 463.

# MATH-M 464 Introduction to Probability Theory II

- Credits
- 3
- Prerequisites
- MATH-M 463 or MATH-S 463
- Description
- Conditional distributions and expectation, linear and nonlinear regression; simple stochastic processes: Poisson process, process with independent increments, random walk, Markov chain with finite state space; information theory.

# MATH-S 463 Honors Course in Probability Theory I

- Credits
- 3
- Prerequisites
- MATH-S 303 and MATH-S 311; or consent of instructor
- Description
- Honors version of MATH-M 463. For students of outstanding ability in mathematics.

# PSY-P 404 Computer and Statistical Models in Psychology

- Credits
- 3
- Prerequisites
- PSY-K 300 or equivalent
- Description
- This laboratory course provides an introduction to elementary mathematical, statistical, and computer models in psychology. Students learn to use computer spreadsheet packages to program formal models and to apply the models to analyze data obtained in psychological experiments.

# INFO-I 368 INTRODUCTION TO NETWORK SCIENCE

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

# INFO-I 421 APPLICATIONS OF DATA MINING

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

# INFO-I 422 DATA VISUALIZATION

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

# INFO-I 468 NETWORK SCIENCE APPLICATIONS

- Credits
- 3–3 credit hours
- Prerequisites
- None
- Description
- None

# CSCI-B 455 Principles of Machine Learning

- Credits
- 3
- Prerequisites
- CSCI-C 200 or CSCI-C 211; and MATH-M 211
- Description
- In this course, we explore (machine learning) algorithms that can learn from and make predictions on data. This course introduces the statistical, mathematical, and computational foundations of these frameworks, with a strong focus on understanding the mathematical derivations for the algorithms and simultaneously implementing the algorithms.

**Addenda Requirement*.**One (1) of the following options:**Finite + Brief Survey of Calculus.****Finite Mathematics.**One (1) course:- MATH-M 118 Finite Mathematics
- MATH-V 118 Finite Mathematics with Applications

# MATH-M 118 Finite Mathematics

- Credits
- 3
- Prerequisites
- None
- Notes
- R: To be successful, students will demonstrate mastery of two years of high school algebra as indicated by an appropriate ALEKS score or completion of MATH-M 014, MATH-M 018, or MATH-J 111
- Description
- Sets, counting, basic probability, including random variables and expected values. Linear systems, matrices, linear programming, and applications.
- Repeatability
- Credit given for only one of MATH-A 118, MATH-M 118, MATH-S 118, MATH-V 118; or MATH-D 116 and MATH-D 117.

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# MATH-V 118 Finite Mathematics with Applications

- Credits
- 3
- Prerequisites
- None
- Notes
- R: To be successful, students will demonstrate mastery of two years of high school algebra as indicated by an appropriate ALEKS score or completion of MATH-M 014, MATH-M 018, or MATH-J 111
- Description
- Sets, counting, basic probability, linear modelling, and other discrete topics. Applications to various areas depending on topic. Possibilities include social and biological sciences and consumer mathematics.
- Repeatability
- Credit given for only one of MATH-A 118, MATH-M 118, MATH-S 118, MATH-V 118; or MATH-D 116 and MATH-D 117.

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**Brief Survey of Calculus.**One (1) course:- MATH-M 119 Brief Survey of Calculus I
- MATH-V 119 Applied Brief Calculus I

# MATH-M 119 Brief Survey of Calculus I

- Credits
- 3
- Prerequisites
- None
- Notes
- R: To be successful, students will demonstrate mastery of two years of high school algebra, one year of high school geometry, and pre-calculus as indicated by an appropriate ALEKS score or completion of MATH-M 025 or MATH-M 027
- Description
- Introduction to calculus. Primarily for students from business and the social sciences.
- Repeatability
- Credit given for only one of MATH-J 113, MATH-M 119, MATH-V 119, MATH-M 211, or MATH-S 211.

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# MATH-V 119 Applied Brief Calculus I

- Credits
- 3
- Prerequisites
- None
- Notes
- R: To be successful, students will demonstrate mastery of two years of high school algebra, one year of high school geometry, and pre-calculus as indicated by an appropriate ALEKS score or completion of MATH-M 025 or MATH-M 027
- Description
- Introduction to calculus. Variable topic course with emphasis on non-business topics and applications. The topic(s) will be listed in the Schedule of Classes each semester.
- Repeatability
- A student may receive credit for only one of the following: MATH-J 113, MATH-M 119, MATH-M 211, MATH-S 211, or MATH-V 119.

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**Calculus II.**One (1) course:- MATH-M 212 Calculus II
- MATH-S 212 Honors Calculus II

# MATH-M 212 Calculus II

- Credits
- 4
- Prerequisites
- MATH-M 211 or MATH-S 211; or consent of department
- Description
- Techniques of integration (by parts, trigonometric substitutions, partial fractions), improper integrals, volume, work, arc length, surface area, infinite series.
- Repeatability
- Credit given for only one of MATH-M 120 or MATH-M 212.

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# MATH-S 212 Honors Calculus II

- Credits
- 4
- Prerequisites
- MATH-S 211 or consent of department
- Description
- Includes material of MATH-M 212 and supplemental topics. Designed for students of outstanding ability in mathematics.
- Repeatability
- Credit given for only one of MATH-M 120, MATH-M 212, or MATH-S 212.

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**Minor GPA, Hours, and Minimum Grade Requirements.****Minor GPA.**A GPA of at least 2.000 for all courses taken in the minor—including those where a grade lower than C- is earned—is required.**Minor Minimum Grade.**Except for the GPA requirement, a grade of C- or higher is required for a course to count toward a requirement in the minor.**Minor Upper Division Credit Hours.**At least 9 credit hours in the minor must be completed at the 300–499 level.**Minor Residency.**At least 9 credit hours in the minor must be completed in courses taken through the Indiana University Bloomington campus or an IU-administered or IU co-sponsored Overseas Study program.

Notes

##### Minor Area Courses

Unless otherwise noted below, the following courses are considered in the academic program and will count toward academic program requirements as appropriate:

- Any course at the 100-499 level with the
`STAT`

subject area prefix--as well as any other subject areas that are deemed functionally equivalent - Any course contained on the course lists for the academic program requirements at the time the course is taken--as well as any other courses that are deemed functionally equivalent--except for those listed only under Addenda Requirements
- Any course directed to a non-Addenda requirement through an approved exception

This program of study cannot be combined with the following:

- Bachelor of Science in Statistics (STATBS)

Exceptions to and substitutions for minor requirements may be made with the approval of the unit's Director of Undergraduate Studies, subject to final approval by the College of Arts and Sciences.