22 Statistics Preparatory Programs

Random variables, independence, and conditional probability; discrete and continuous distributions, moments, and distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite: CME 100/ENGR154 or MATH 51 or 52.

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.

Basic concepts of estimation and tests of hypotheses, ideas from probability; one-, two-, and multiple-sample problems. Applications are in the social sciences, and students will be able to understand the basics of using a sample to predict uncertainty.

Numerical and graphical summaries of univariate and bivariate data. Basic probability, random variables, binomial distribution, normal distribution. One-sample statistical inference for normal means and binomial probabilities. Primarily for students in the social sciences with limited mathematics preparation.

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.

Exposes students to the fundamentals of probability, decision analysis, and statistics, and their application to business. Topics include probability, decision analysis, distributions, sampling, estimation, hypothesis testing, and chi-square. Effective Fall 2018, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning I.

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?

Introduces students to macro and micro approaches to project cost estimation. Case studies of both pre-project and in-process estimating cover some of the more common perils of human irrationality associated with project estimation to help develop more sensible, achievable project outcomes.

Examines the use of economic and statistical tools for making business decisions at an advanced level and prepares students for future study in business analytics. Introduces programming for data analysis (no previous programming knowledge required) and links data analysis to decision-making using both spreadsheet modeling and statistical programming.

Introduction to probability theory, including probability axioms, conditional probability, independence, random variables, and expectation. Joint, marginal, and conditional distributions. Discrete models (binomial, hypergeometric, Poisson) and continuous models (normal, exponential).

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.

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.

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.