1 - 10 of 135 results for: STATS

Statistical inference, with a minimum of mathematical formulation. Topics: comparisons and the randomized clinical trial, statistical significance, accuracy and the meaning of statistical error (plus or minus), correlation and regression to the mean, exploratory methods and data mining, life tables and survival analysis, and learning from experience (Bayesian inference). Lectures supplemented with web-based statistical simulations.

Preference to freshmen. The role of probability and statistics in understanding chance phenomena in an uncertain and unpredictable world. Goal: expose students to the range of real-world applications of probability and statistics, to read newspaper and journal articles with critical thinking, and to learn some simple back-of-the-envelope calculations to interpret data. Applications: statistics in court cases, randomized clinical trials and assessing the efficacy of new drugs; chance and strategy in sports; paradoxes in probability and statistics; predicting the stock market and the random walk hypothesis; analysis of ESP experiments. (Staff)

Preference to freshmen. Data reporting in newspaper and magazine accounts often leads to misinterpretations and erroneous conclusions. Goal is to introduce the basic statistical tools needed to critically interpret reported data. Applications from medicine, law, sports, parapsychology, business. (Staff)

The goal is the building of a statistical model, based on applicant data, for predicting admission to selective universities. The model will consider factors such as gender, ethnicity, legacy status, public-private schooling, test scores, effects of early action, and athletics. Common misconceptions and statistical pitfalls are investigated. The applicant data are not those associated with any specific university.

Preference to freshmen. Principles of displaying data and envisioning information based on literature and historical examples. Application of these principles to media such as computer graphics and the Internet. Student projects.

Preference to freshmen; preference to students with either AP calculus or AP statistics, or equivalent. The interplay between applied mathematics and the world around it through a tour of celebrated topics in statistics and mathematics such as John Snow, graphic display and cholera; sorting algorithms; the census, and random matrices. Computational experimentation, such as MATLAB, is encouraged. (Staff)

Preference to freshmen. The evidence for fractal-like behavior in different fields and why these ideas can be so attractive to proponents. Is there evidence for fractal and multi-fractal patterns in nature and in non-natural phenomena such as finance? Backlash against such claims. (Donoho)

Preference to freshmen. Cryptography and its counterpart, cryptanalysis or code breaking. How the earliest cryptanalysts used statistical tools to decrypt messages by uncovering recurring patterns. How such frequency-analysis tools have been used to analyze biblical texts to produce a Bible code, and to detect genes in the human genome. Overview of codes and ciphers. Statistical tools useful for code breaking. Students use simple computer programs to apply these tools to break codes and explore applications to various kinds of data.

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.