# Chapter 1 Welcome

Welcome to the course website for EPIB 607 FALL 2019: Inferential Statistics at McGill University.

## 1.1 Objectives

The aim of this course is to provide students with basic principles of statistical inference so that they can:

1. Visualize/Analyze/Interpret data using statistical methods with the R statistical software program.
2. Understand the statistical results in a scientific paper.
3. Apply statistical methods in their own research.
4. Use the methods learned in this course as a foundation for more advanced biostatistics courses.

## 1.2 Audience

The principal audience is researchers in the natural and social sciences who haven’t had an introductory course in statistics (or did have one a long time ago). This audience accepts that statistics has penetrated the life sciences pervasively and is required knowledge for both doing research and understanding scientific papers.

These notes are a collection of useful links, videos, online resources and papers for an introductory course in statistics. The instructors have found that no single book sufficiently teaches all the topics covered in this course. Part of this is due to advancements in computing which have far outpaced the publication of modern textbooks. Indeed, the computer has replaced many of the calculations that were traditionally taught to be done by hand. We direct the readers to what we think is a good learning resource for a given topic (following the Flipped Classroom strategy). We also provide our own commentary and notes when we think its useful.

## 1.4 Teaching strategy

This course will follow the Flipped Classroom model. Here, students are expected to have engaged with the material before coming to class (based on very precise pre-class instructions). The students will then be expected to answer a series of conceptual multiple choice questions using the DALITE online platform (Bhatnagar et al. 2016).

This allows the instructor to delegate the delivery of basic content and definitions to textbooks and videos, and enforces the idea that students cannot be simply passive recipients of information. This approach then allows the professor to focus valuable class time on nurturing efficient discussions surrounding the ideas within the content, guiding interactive exploration of typical misconceptions, and promoting collaborative problem solving with peers.

## 1.5 A focus on computation

Classic introductory statistics textbooks were written during a time when computers were still in their infancy. As such, even the newer editions heavily rely on by-hand computations such as looking up tables for tail probabilities. We take a modern approach and introduce computational methods in statistics with the statistical software program R.

## 1.6 DataCamp

This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.

You will be asked to complete some of the courses in DataCamp for background reading or for assignments. You can sign up for a free account at this link. Note: you are required to sign up with a @mail.mcgill.ca or @mcgill.ca email address.

## 1.7 R Code Conventions

We use R code throughout these notes. When R code is displayed1 it will be typeset using a monospace font with syntax highlighting enabled to ensure the differentiation of functions, variables, and so on. For example, the following adds 1 to 1

a = 1L + 1L
a

Each code segment may contain actual output from R. Such output will appear in grey font prefixed by #>. For example, the output of the above code segment would look like so:

[1] 2

## 1.8 Development

This book is built with bookdown and is open source and freely available. This approach encourages contributions, ensures reproducibility and provides access to the material worldwide. The online version of the book is hosted at sahirbhatnagar.com/EPIB607 and kept up-to-date thanks to Travis. The entire source code is available at https://github.com/sahirbhatnagar/EPIB607.

If you notice any errors, we would be grateful if you would let us know by filing an issue here or making a pull request by clicking the edit button in the top-left corner of the text:

The version of the book you are reading now was built on 2019-11-25 and was built on Travis.