19 Statistics Course Programs

Due to COVID-19, classroom courses during Fall 2021 will be offered in a live online format. We continue to offer self-paced online courses.

“If you can’t measure it, you can’t change it.” Masterclass in Labour Statistics: From Standards to Data course enable us to monitor one of the most important aspects of our well-being: work. As we spend a large part of our lives at work and live on the earnings from it, governments must have access to timely and relevant data to inform accurate and sound policies.

Introduces basic statistical modeling techniques. One- or two- sample inference for unknown means, proportions and variances, categorical data analysis, introduction to design of experiments and analysis of variance, analysis of simple and multiple linear regression models, non-parametric methods.

Introduction to the logic and processes involved in descriptive and inferential statistics for psychology. Topics include statistical inference, significance, t-tests, ANOVAs, correlation, regression, and statistical software analysis. This is a hybrid class: class time is reserved for hands-on activities.

Undergraduate Prerequisites: Academic background that includes the material covered in a standard course on college algebra.

Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students learn to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?

We offer courses that, in the true spirit of the liberal arts, emphasize underlying ideas, their social and ethical context, and their explanatory power. The statistics major emphasizes a strong background in the fundamentals, while also providing pathways to explore how statistics is applied in a variety of fields. Statistics is a core component of many fields from medicine to economics to data science.

The Master of Arts and Master of Science degrees in Applied Statistics provide students with the necessary background to seek employment in statistics, data sciences or a related field. The degree programs provide training in applied statistics and consulting, as well as in the theory of statistics pertinent to applications. In addition, students are required to study an approved area outside of statistics at the graduate level. Possible areas include numerical analysis, biostatistics, education, economics, sociology, psychology, biology, business, bioinformatics, computer science, mathematical finance, and many others.

The Applied Statistics B.A. is designed for students to learn the skills necessary to work in fields such as Economics, Psychology & Biology. Applied Statistics is for students interested in pursuing other disciplines in the field of Statistics such as Economics, Psychology and Biology. The curriculum is focused for students pursuing a B.A. or a Minor in Applied Statistics through practical skills such as regression analysis and ANOVA, or gaining experience in statistical software such as SAS and SPSS.

Kenyon’s mathematics program blends theoretical ideas and methods, the modeling of real-world situations, and the statistical analysis of data. Members of the mathematics faculty are at the forefront of such innovative programs as mathematical biology and work closely with students on research projects.

The focus of our courses is on the broad understanding of the statistical methods and computational and presentation skills for statistical problem-solving. We also develop students’ ability to systematically think about how to describe the world around us and use data to make decisions and predictions. Statistical research methodology and associated analysis techniques aimed at a non-mathematical audience (non-calculus based). This course introduces procedures that are most commonly used in the investigation of survey and experimental data.

From probability and statistics to data analysis and machine learning, master the skills needed to solve complex challenges with data.

Causal Inference with R is the first course in a series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this introduction focuses on how to interpret treatment effects, and how to explore and derive key summary statistics from dataframes.

The broad objective of the course is to promote an understanding of an objective and disciplined approach in the study and analysis of problems especially those involving numerical data in experimental studies. The course covers: organization and presentation of numerical data, descriptive statistics, concepts of parameter and statistic, distribution of sample means, hypothesis and test of hypothesis, student's t-distribution, confidence interval and coefficient, x2-distribution, F-distribution, analysis of variance, randomized block design, simple regression and correlation.

The main goal of this learning module is to discuss phase transitions and critical phenomena in statistical physics. We introduce the ideas of mean-field theory and renormalization group and exploit them to calculate the critical exponents for the Ising model. More advanced topics concern the models with continuous symmetry, topological phase transitions, as well as Monte Carlo simulations.