Cornell University Registrar

Statistics is about understanding the world through data. We are surrounded by data, so there is a lot to understand. Covers data exploration and display, data gathering methods, probability, and statistical inference methods through contingency tables and linear regression. The emphasis is on thinking scientifically, understanding what is commonly done with data (and doing some of it for yourself), and laying a foundation for further study. Students learn to use statistical software and simulation tools to discover fundamental results. They use computers regularly; the test includes both multimedia materials and a software package. This course does not focus on data from any particular discipline, but will use real-world examples from a wide variety of disciplines and current events.

A second course in statistics that emphasizes applications to the social sciences. Topics include simple linear regression, multiple linear regression (theory, model building, and model diagnostics), and the analysis of variance. Computer packages are used extensively.

This seven week, two-credit class will cover the regression requirements, hypothesis tests, and interpretation of results. Students will learn to identify the data necessary to perform a regression analysis, evaluate the conditions, and apply the statistical tests. Interpretation of overall results will be made. Independent/group projects by each student will be done. These will consist of identifying an issue of interest, a relevant data set, and analysis using the regression methods. Presentation of the results in verbal and written form will be required. Recommended for students who want to develop applied analysis skills.

This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, the method of moments, sampling distributions and confidence intervals.

Theory and application of statistical sampling, especially in regard to sample design, cost, estimation of population quantities, and error estimation. Assessment of nonsampling errors. Discussion of applications to social and biological sciences and to business problems.

This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, sampling distributions and hypothesis testing.

Causal claims are essential in both science and policy. Would a new experimental drug improve disease survival? Would a new advertisement cause higher sales? Would a person's income be higher if they finished college? These questions involve counterfactuals: outcomes that would be realized if a treatment were assigned differently. This course will define counterfactuals mathematically, formalize conceptual assumptions that link empirical evidence to causal conclusions, and engage with statistical methods for estimation. Students will enter the course with knowledge of statistical inference: how to assess if a variable is associated with an outcome. Students will emerge from the course with knowledge of causal inference: how to assess whether an intervention to change that input would lead to a change in the outcome.

This course is on the basics of multivariate statistical analysis. The focus ison the applied side, and the students will learn by examples of multiple real-life datasets. Studentswill learn to visualize the datasets and conduct simple statistical analysis using linear/nonlinearmethods. We will also cover web-scraping and data cleaning.

Categorical data analysis, including logistic regression, log-linear models, stratified tables, matched pairs analysis, polytomous response, and ordinal data. Applications in biological, biomedical and social sciences.

This course begins with a discussion of some general principles of experimental design. Classical designs are covered in detail, motivated by real data applications. These include completely randomized, randomized block, balanced incomplete block, split-plot, repeated measures and fractional factorial designs. If time permits rank-based nonparametric versions of the classical designs will also be covered.

Introduces statistical tools for the analysis of time-dependent data. Data analysis and application will be an integral part of this course. Topics include linear, nonlinear, seasonal, multivariate modeling, and financial time series.

Students are eligible for the ILR senior honors program if they: (1) earn a minimum 3.700 cumulative GPA at the end of junior year; (2) propose an honors project, entailing research leading to completion of a thesis, to an ILR faculty member who agrees to act as thesis supervisor; and (3) submit an honors project, endorsed by the proposed faculty sponsor, to the Academic Standards and Integrity Committee. Accepted students embark on a two-semester sequence. The first semester consists of determining a research design, familiarization with germane scholarly literature, and preliminary data collection. The second semester involves completion of the data collection and preparation of the honors thesis. At the end of the second semester, the candidate is examined orally on the completed thesis by a committee consisting of the thesis supervisor and a second faculty member.

All requests for permission to register for an internship must be approved by the faculty member who will supervise the project and the chairman of the faculty member's academic department before submission for approval by the director of off-campus credit programs. Upon approval of the internship, each student will be enrolled in ILRST 4970, for 4 letter-graded credits for individual research, and in ILRST 4980, for 8 S/U credits, for completion of a professionally-appropriate learning experience, which is graded by the faculty sponsor.

All requests for permission to register for an internship must be approved by the faculty member who will supervise the project and the chairman of the faculty member's academic department before submission for approval by the director of off-campus credit programs. Upon approval of the internship, each student will be enrolled in ILRST 4970, for 4 letter-graded credits for individual research, and in ILRST 4980, for 8 S/U credits, for completion of a professionally-appropriate learning experience, which is graded by the faculty sponsor.

For individual or group research projects conducted under the direction of a member of the ILR faculty, in a special area not covered by regular course offerings. Sophomores, juniors, and seniors with a preceding semester GPA average of 3.0 are eligible to submit projects for approval by the Academic Standards and Integrity Committee. Students should consult with an advisor in the Office of Student Services at the time of course enrollment to arrange for formal submission of their directed study.

A first course in statistics for graduate students in the social sciences. Descriptive statistics, probability and sampling distributions, estimation, hypothesis testing, simple linear regression, and correlation. Students are instructed on the use of a statistics computer package at the beginning of the term and use it for weekly assignments.

Second course in statistics that emphasizes applications to the social sciences. Topics include simple linear regression, multiple linear regression (theory, model building, and model diagnostics), and the analysis of variance. Computer packages are used extensively.

Develops and uses statistical methods to analyze data arising from a wide variety of applications. Topics include descriptive statistics, point and interval estimation, hypothesis testing, inference for a single population, comparisons between two populations, one- and two-way analysis of variance, comparisons among population means, analysis of categorical data, and correlation and regression analysis. Introduces interactive computing through statistical software. Emphasizes basic principles and criteria for selection of statistical techniques.

Properties of the multivariate normal distribution. Distribution theory for quadratic forms. Properties of least squares and maximum likelihood estimates. Methods for fixed-effect models of less than full rank. Analysis of balanced and unbalanced mixed-effects models. Restricted maximum likelihood estimation. Some use of software packages and illustrative examples.