Quantitative Approaches to Health Science: Syllabus and R Programming Integration

Quantitative Approaches to Health Science

Course Overview

This course introduces students to statistical methods essential for health sciences, with a strong emphasis on the use of R programming for data analysis. Students will learn foundational concepts in statistics, probability, and data analysis, and apply these using R and RStudio.

• Instructor: Hao Luo, PhD

• Teaching Assistants: Multiple TAs listed for support

• Teaching Mode: In-person lectures and lab sessions

• Office Hours: Wednesdays 4:00 PM – 5:00 PM, or by appointment

Course Description

Statistical methods are vital in health sciences for understanding data and making informed decisions. This course builds a foundation in basic statistical concepts and techniques, with a focus on their application in health research using R. Students will:

• Learn core ideas in probability and statistical inference, including probability distributions, estimation, and significance testing.

• Gain knowledge of descriptive statistics (mean, median, standard deviation, graphs).

• Choose appropriate statistical methods based on data and research questions.

• Use R to perform basic statistical analyses, including descriptive and introductory inferential techniques.

• Interpret and critically evaluate statistical methods and findings in health science studies.

Required Software

• R: A programming language for statistical analysis.

• RStudio: A user-friendly interface for R.

• Students are expected to have R and RStudio installed by the second week of class.

• Download RStudio

Course Schedule and Main Topics

The course is structured into weekly topics, each focusing on a key area of statistics and its application in R. Below is a summary of the schedule and topics:

No	Date	Day	Topics
1	Sep 4	Thursday	Introduction: What Is Statistics?
2	Sep 9	Tuesday	Descriptive Statistics I: Tabular and Graphical Methods
3	Sep 11	Thursday	Descriptive Statistics II: Numerical Methods
4	Sep 16	Tuesday	Exploratory Data Analysis with R I
5	Sep 18	Thursday	Exploratory Data Analysis with R II
6	Sep 23	Tuesday	Probability Topics
7	Sep 25	Thursday	Discrete Random Variables
8	Sep 30	Tuesday	Continuous Random Variables
9	Oct 2	Thursday	The Normal Distribution
10	Oct 7	Tuesday	The Central Limit Theorem
11	Oct 9	Thursday	Confidence Interval
12	Oct 14	Tuesday	Reading week
13	Oct 16	Thursday	Review of probability topics
14	Oct 21	Tuesday	Exam 01
15	Oct 23	Thursday	Hypothesis Testing with one sample
16	Oct 28	Tuesday	Hypothesis Testing with two samples
17	Oct 30	Thursday	Comparing groups: Two groups
18	Nov 4	Tuesday	Comparing groups: Multiple groups
19	Nov 6	Thursday	The relationship between two categorical variables
20	Nov 11	Tuesday	The relationship between two continuous variables
21	Nov 13	Thursday	Linear Regression I
22	Nov 18	Tuesday	Linear Regression II
23	Nov 20	Thursday	Nonparametric Statistics
24	Nov 25	Tuesday	Review
25	Nov 27	Thursday	Review
26	Dec 2	Tuesday	Exam 02

Key Statistical Concepts and R Programming Applications

• Descriptive Statistics: Summarizing and visualizing data using measures such as mean, median, mode, standard deviation, and graphical methods (histograms, boxplots).

• Probability Distributions: Understanding discrete and continuous random variables, including the normal distribution.

• Inferential Statistics: Confidence intervals, hypothesis testing, and the Central Limit Theorem.

• Comparing Groups: Statistical tests for comparing means and relationships between variables (e.g., t-tests, ANOVA, chi-square tests).

• Regression Analysis: Linear regression for modeling relationships between variables.

• Nonparametric Methods: Statistical methods that do not assume a specific data distribution.

• R Programming: Using R for data analysis, including data import, manipulation, visualization, and statistical testing.

Assessments & Activities

Component	Date or Due Date	Location/Submission Method	Weight (%)
Assignment I	Sep 24	LEARN Dropbox	10%
Assignment II	Oct 8	LEARN Dropbox	10%
Assignment III	Nov 5	LEARN Dropbox	10%
Assignment IV	Nov 26	LEARN Dropbox	10%
Midterm written exam	Oct 23	In-class	20%
Final written exam	Dec 2	In-class	30%

• Homework Assignments: Four assignments, each with conceptual and applied exercises, submitted as Word documents via LEARN Dropbox.

• Written Exams: Two in-class exams with multiple-choice and short-answer questions, including R output interpretation.

Policies and Academic Integrity

• Late/Missed Content: Extensions may be granted for extenuating circumstances with proper documentation.

• Academic Integrity: Students must avoid plagiarism and unauthorized collaboration. All work must be original and properly cited.

• Discussion Forum: Use the LEARN platform for course discussions and questions.

Summary Table: Key Course Components

Component	Description
Lectures	In-person, covering theory and R applications
Lab Sessions	Hands-on R programming and data analysis
Assignments	Applied exercises using R and statistical concepts
Exams	Assessment of theoretical understanding and R skills

Additional info:

• This syllabus integrates R programming as a core tool for statistical analysis in health sciences, making it highly relevant for R Programming college students.

• Students are expected to develop both conceptual understanding and practical skills in R.