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Statistics Minor

Discover the power of statistics to interpret data and solve problems, and prepare for a world of opportunities.

In a world that’s drowning in data, the ability to understand and interpret numbers has never been more valuable. A minor in Statistics from UND gives you the tools to analyze data and help solve problems in fields as diverse as agriculture, baseball and politics.

Program type:
Minor
Format:
On Campus or Online
Est. time to complete:
2 years
Credit hours:
21-22
  • Requirements
  • How to Apply
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Why minor in Statistics?

Studying statistics teaches you to understand and interpret information. And in our modern, data-driven world, statistical literacy has become an invaluable skill across all disciplines. With more data available than ever before, the demand for professionals with statistical skills is high and rising.

UND offers two paths to the minor in Statistics:

  • Plan A, which requires three semesters of calculus as a prerequisite
  • Plan B, which requires Applied Calculus I as a prerequisite

Whichever track you choose, you'll learn to analyze data, design experiments and solve real-world problems.

What majors pair well with a Statistics minor?

Combining a Statistics minor with certain majors can enhance your analytical abilities and broaden your career opportunities across various fields. Here are some bachelor's degrees that complement a Statistics minor:

  • Economics Degree: Economics involves extensive statistical analysis for modeling economic trends, analyzing data sets, and making informed decisions. Combining a Statistics minor with an Economics degree can provide a strong quantitative background for roles in economic research, analysis, and forecasting.
  • Data Science Degree: Data science heavily relies on statistical methods for analyzing large datasets, extracting insights, and building predictive models. Pairing a Statistics minor with a Data Science degree can provide a comprehensive skill set for roles in data analysis, machine learning, and business intelligence.
  • Mathematics Degree: Mathematics serves as the foundation for statistical theory and methods. A Mathematics major with a Statistics minor can offer a deep understanding of mathematical concepts and their applications in statistical analysis, providing a strong quantitative background for various career paths, including finance, research, and academia.
  • Computer Science Degree: Computer science intersects with statistics in areas such as machine learning, artificial intelligence, and data mining. Combining a Statistics minor with a Computer Science degree can prepare you for roles in data-driven software development, algorithm design, and big data analytics.
  • Engineering Degrees: Engineering disciplines often involve data analysis and statistical modeling for design optimization, process improvement, and risk assessment. Pairing a Statistics minor with an Engineering degree can provide valuable quantitative skills for engineering professionals working in fields such as manufacturing, operations research, and quality control.
  • Psychology Degree: Psychology research relies on statistical methods for designing experiments, analyzing data, and drawing conclusions. A Statistics minor complements a Psychology degree by providing expertise in quantitative research methods, enhancing your ability to conduct rigorous statistical analysis in psychological research.
  • Sociology Degree: Sociology research often involves analyzing survey data, conducting statistical tests, and interpreting social trends. Combining a Statistics minor with a Sociology degree can equip you with the quantitative skills needed to analyze complex social phenomena and conduct empirical research in sociology.
  • Biology Degree: Biological research increasingly relies on statistical analysis for experimental design, data interpretation, and hypothesis testing. Pairing a Statistics minor with a Biology degree can prepare you for careers in biostatistics, bioinformatics, and medical research, where statistical skills are essential for analyzing biological data.
  • Business Degree: Business analytics requires strong statistical skills for market research, forecasting, and decision-making. Adding a Statistics minor to a Business degree can provide a competitive edge in roles such as financial analysis, marketing research, and operations management, where data-driven insights drive strategic business decisions.

Statistics Minor at UND

  • Enjoy small, discussion-based classes.

  • Learn from experienced faculty.

  • Hear from experts in the field through our visiting speaker series.

Statistics Minor Courses

BIOL 470. Biostatistics. 4 Credits.

Analysis of biological data. Covers descriptive statistics, inferential statistics (e.g., t-tests, goodness-of-fit tests, regression, ANOVA and non-parametric tests), and interpreting and presenting statistical results. S.

PSYC 541. Advanced Univariate Statistics. 3 Credits.

Theory of univariate statistics; application to quantitative data in psychology. Prerequisite: Graduate standing, college algebra, and elementary statisitcs.

PSYC 543. Experimental Design. 3 Credits.

Application of statistics and probability theory to the design and analysis of experiments. Prerequisite: PSYC 541 or consent of instructor.

ECON 210. Introduction to Business and Economic Statistics. 3 Credits.

Descriptive statistics; probability; discrete and continuous probability distributions; sampling distributions; estimation and hypothesis testing of common parameters; analysis of variance; correlation; linear regression. Prerequisite: MATH 103 or MATH 107 or MATH 146 or MATH 165 or MATH 166. F,S,SS.

MATH 321. Applied Statistical Methods. 3 Credits.

Introductory statistics for students with a background in single-variable calculus. Topics include descriptive statistics, continuous and discrete probability density functions, sampling distributions, point and interval estimation, and tests of hypotheses. Prerequisite: MATH 166. F,S.

MATH 421. Statistical Theory I. 3 Credits.

Discrete and continuous random variables, expectation, moments, moment generating functions, properties of special distributions, introduction to hypothesis testing, sampling distributions, Central Limit Theorem, curve of regression, correlation, empirical regression by least squares, maximum likelihood estimation, Neyman-Pearson lemma, likelihood ratio test, power function, chi-square tests, change of variable, "t" and "F" tests, one and two-way ANOVA, nonparametric methods. Prerequisite: MATH 265. F.

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