# Statistics for Life Sciences (332-0-20)

## Instructors

Hongmei Jiang

847.467.1087

2006 Sheridan, Room 101A

## Meeting Info

Annenberg Hall G01: Tues, Thurs 11:00AM - 12:20PM

## Overview of class

This is a practical statistics course with emphasis on the application of statistical methods and data analysis techniques to the life sciences. We will cover topics including descriptive statistics, normal distribution, random variables, sampling distribution, confidence intervals, hypothesis tests, p-values and multiple correction, linear regression, model selection, diagnostics, logistic regression, contingency tables, resampling, clustering, dimension reduction, and genomics data analysis. In addition to classical parametric statistics (e.g., two-sample t-test), we will also cover nonparametric approaches (e.g., rank-based test) and resampling based approaches (e.g., permutation test) when data do not fit assumptions required by the standard approaches.

*PLEASE NOTE: This course does not count for Statistics major or minor credit

## Registration Requirements

Prerequisite is one introduction to statistics course

## Learning Objectives

This is a practical statistics course with emphasis on the application of statistical methods and data analysis techniques to the life sciences. We will cover topics including descriptive statistics, normal distribution, random variables, sampling distribution, confidence intervals, hypothesis tests, p-values and multiple correction, linear regression, model selection, diagnostics, logistic regression, contingency tables, resampling, clustering, dimension reduction, and genomics data analysis. In addition to classical parametric statistics (e.g., two-sample t-test), we will also cover nonparametric approaches (e.g., rank-based test) and resampling based approaches (e.g., permutation test) when data do not fit assumptions required by the standard approaches. By the end of the quarter, students should be able to (1) formulate statistical questions for a life science question; (2) use visualization techniques to explore the data; (3) choose the appropriate statistical methods and justify the choice; (4) perform data analysis using R programming; (5) describe and present the data analysis results.

## Teaching Method

Lecture, 2 one hour and twenty minute meetings per week

## Evaluation Method

Homework will be assigned weekly or biweekly (about 7 assignments) One open-book and open-notes midterm exam (in class) One take-home final exam/project

## Class Materials (Required)

The Analysis of Biological Data, Third Edition| 2020, Michael C. Whitlock; Dolph Schluter, ISBN-13: 978-1319226237 and ISBN-10: 131922623X. Amazon price as in Jan. 2023 is $56-$127 for eTextbook, $200 for hardcover. Northwestern bookstore should have the textbook available for purchase.

## Class Materials (Suggested)

Free and open-source statistical software R/RStudio will be used.

## Class Notes

Lecture notes and R scripts will be posted on canvas.

## Class Attributes

Formal Studies Distro Area