1 Revised: 04/06/17

Statistics with R Course Syllabus

Course Description In this course, students will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

Corresponds with: Coursera Content Dated Beginner Specialization: No experience needed Commitment: 5 weeks, 5-7 hours per week Subtitles: English

Learning Objectives Upon completion of the course, students will understand:

• Sampling and exploring data, as well as basic probability theory and Bayes’ rule, as well as various types of sampling methods, and discuss how such methods can impact the scope of inference

• How to set up and perform hypothesis tests, interpret p-values, and report the results of an analysis in a way that is interpretable for clients or the public, including how to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest

• How to assess the relationship between variables in a data set and a continuous response variable, as well as simple and multiple linear regression models

• Fundamental theory behind linear regression and through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio

• Bayesian statistics and how to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm

Course Format Statistics with R is a self-paced, online course delivered through the website Coursera. The site to access the coursework is coursera.org. Coursework is delivered through videos, tutorials, and assignments. No textbooks are required for the course. There are 15 hours of video with approximately 18 assignment hours in this course, as well as 28 quizzes and 4 peer-reviewed assignments. Course Completion Requirements Statistics with R coursework is due within 90 days from the assignment date. The course hours listed at the top of the syllabus reflect the time it would take to click through the slides and do not account for taking notes or the end of module tests. You must complete all five modules within the course. Support

• For Technical support, contact: https://learner.coursera.help/hc/en-us • For course content-related support, contact: https://www.coursera.org/about/contact • For program support, please contact your IVMF advisor or O2O Installation Coordinator

Course Outline

2 Revised: 04/06/17

Topic 1: Statistics with R

1.1 Introduction to Probability and Data

1.2 Inferential Statistics

1.3 Linear Regression and Modeling

1.4 Bayesian Statistics

1.5 Capstone Project