Courses

Indigenous people in Ecuador, which represent about 7% of the Ecuadorian population (United Nations, 2015), are disproportionately poor compared with the rest of the population. In 2008 the country embarked on an effort to improve their situation by creating and approving a new constitution. Despite all of these efforts, indigenous people continue to struggle in Ecuador. For indigenous women specially the battle goes beyond the economic hardship, as they face domestic violence and abuse in a daily basis. 

The proposed course is designed to provide students with a unique one-to-one interaction with Spanish native speakers in three different sites: the Ecuadorian Amazon Rainforest Reservation of Papallacta, the Reservation of Mandaripanga, and the Runatupari Community in the Andean Region of Ecuador.  It aims to:

1. Explore, learn and document the work some indigenous groups have been doing since the new constitution was approved back in 2008.

2. Provide students with a service-learning opportunity working hand in hand when possible with women community leaders at the different sites.

3. Learn about how their communities work to preserve their resources and maintain a sustainable culture.

4. Immerse themselves in the Spanish language and culture by interacting, sharing, and living with native Spanish speakers.

5. Have student produce a focused final essay linking the key concepts from the readings and their lived experiences in the communities visited.

Students will examine the generally accepted account principles (GAAP) underlying financial statements and their implementation in practice. The perspective and main focus of the course is from the users of the information contained in the statements, including investors, financial analysts, creditors and, management. By the end of this class students will be able to construct a cash flow statement, balance sheet and decipher a 10K report.

Prerequisites: some high school algebra. Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Prerequisites: some high school algebra. Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Prerequisites: working knowledge of calculus (differentiation and integration). Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.

Prerequisites: working knowledge of calculus (differentiation and integration). Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.

This is a course in intermediate statistical inference techniques in the context of applied research
questions in data science. Assuming some prior exposure to probability and statistics, this course will
first introduce the student to the principles of Bayesian inference, then apply them in estimation and
prediction in the context of linear and generalized linear models, counting and classification, mixture and
multilevel models, including scientific computation (like MCMC methods). Students will also learn
about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior
information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter
estimation “pooling” in hierarchical model and so on.

Prerequisites: A good working knowledge of calculus, including derivatives, single and double, limits, sums and series. Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved. Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.

Prerequisites: MATH V1101 Calculus I and MATH V1102 Calculus II, or the equivalent, and STAT W1111 or STAT W1211 (Introduction to Statistics). Corequisites: MATH V1201 Calculus III, or the equivalent, or the instructor's permission. This course can be taken as a single course for students requiring knowledge of probability or as a foundation for more advanced courses. It is open to both undergraduate and master students. This course satisfies the prerequisite for STAT W3107 and W4107. Topics covered include combinatorics, conditional probability, random variables and common distributions, expectation, independence, Bayes' rule, joint distributions, conditional expectations, moment generating functions, central limit theorem, laws of large numbers, characteristic functions.