Courses Taught by Philip Boonstra

BIOSTAT699: Analysis of Biostatistical Investigations

Graduate level
Residential
Winter term(s) for residential students;
4 credit hour(s) for residential students;
Instructor(s): Jeremy Taylor, Irina Gaynanova, Jean Morrison, Philip Boonstra, (Residential);
Prerequisites: Registration for last term of studies to complete MS or MPH
Description: Identifying and solving design and data analysis problems using a wide range of biostatistical methods. Written and oral reports on intermediate and final results of case studies required.
Syllabus for BIOSTAT699

Concentration Competencies that BIOSTAT699 Allows Assessment On
Department	Degree	Competency	Specific course(s) that allow assessment
BIOSTAT	MS	Interpret the results of statistical analysis in a variety of health-related areas (e.g. public health, medicine, genetics, biology, psychology, economics, management and policy, nursing, or pharmacy) for the broad scientific community	BIOSTAT699
BIOSTAT	MS	Communicate statistical analysis through written scientific reporting for public health, medical, and basic scientists, and/or educated lay audiences	BIOSTAT699
BIOSTAT	PhD	Communicate through written and oral presentation based on statistical analysis for audience from a variety of health-related areas (e.g. public health, medicine, genetics, biology, psychology, nursing, or pharmacy) and for the broad scientific community	BIOSTAT699

Undergraduate level
Residential
Winter term(s) for residential students;
2 credit hour(s) for residential students;
Instructor(s): Philip Boonstra (Residential);
Prerequisites: None
Advisory Prerequisites: STAT250
Description: This course teaches both the principles and practice of effective data visualization using the R statistical environment, with a special focus on public health data sets. Historical and contemporary examples of data visualizations will be assessed with respect to their effectiveness and integrity.
Learning Objectives: 1. To understand the principles of effective and accurate graphical representation of different data types; 2. To draw conclusions from graphical representations about relationships and trends in variables; 3. To understand how graphical representations of data can be used to mislead or exaggerate relationships; 4. To create and improve data visualizations using the R statistical environment;