NURS972001 Statistics and Probability Guide to Support Epidemiology

Ruth played baseball from 1914-1935. In this example, consider Ruth's yearly number of home runs in his last 10 year of play to determine the mean, median, and mode

Sabeti quantifies the spread of Wahoo's Tacos' sales by calculating the standard deviation. Learn what it means to compare standard deviations.

In this example, a scatter plot shows a correlation between temperature and beach attendance.

Visit the Boston Beanstalks club for tall people. Height is normally distributed and we can use membership cutoffs and population data to calculate z-scores.

This clip explores the probability of seeing Australian animals on a wildlife tour and uses relative frequency to describe the chances of seeing particular animals.

Sabeti lists the assumptions required prior to calculating the confidence interval. Learn how to create a confidence interval for any confidence level

In this video lecture, Professor Naomi Lowe explains hypothesis testing (logic, process, one- and two-tailed tests, and significance in research).

In this video lecture,Professor Naomi Lowe discusses the different branches of statistics and looks at types of variables.

Host Dr. Pardis Sabeti's own research examines possible genetic resistance to deadly Lassa fever in West Africa. Using Inference for Two-Way Tables

The course covers sample space, random variables, expectations, transforms, Bernoulli and Poisson processes, finite Markov chains, and limit theorems.

The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data.

This course provides an elementary introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.

This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data

This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication.

This course covers the fundamental methods used for exploring the information content of observations related to kinematical and dynamical models.

This course is designed to introduce first-year Sloan MBA students to the fundamental techniques of using data. In particular, the course focuses on various ways of modeling, or thinking structurally about decision problems in order to make informed management decisions.

This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.

A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally