Increasingly, government agencies, the media, and scientists report on the state of events with reference to quantitative and statistical relationships. It is all too easy to be fooled by our faulty human intuition if we don’t know how to interpret this information in an informed and thoughtful manner. The consequences of such misunderstanding can be dire. Indeed, without a sound literacy in mathematical reasoning (numeracy), it is becoming more and more difficult to be able to effectively engage in these debates critically, effectively, and responsibly. This course aims to teach students to both leverage their existing mathematical techniques for thinking about real global challenges and problems through a focus on mathematically modeling phenomena relevant to these challenges.
Upon completion the course aims to provide the students with the following skills:
Link numerical values and concepts to real-world scenarios, and feed-back lessons from more formal mathematical thinking to inform those real-world scenarios;
Develop an expanded capacity
Understand and critically evaluate quantitative research findings reported in scientific papers and in the media with reference to issues of research design, measurement, and both substantive and statistical significance;
Develop a firm understanding of the empirical foundations and historical implications of probability theory, randomness, and stochasticity for problems of inference.
Mode of Instruction
Instruction in Numeracy is somewhat unique. There will be occasionally recorded lectures and/or video paired with a set of required and recommended readings. Class sessions will proceed with a portion dedicated to reviewing questions and quizzing, but will mostly be dedicated to lab work on specific exercises drawing on readings in preparation for homework assignments. Students will be expected to work in groups that will be randomly assigned each week. Students should tutor each other in addition to seeking guidance from their lab instructor and most importantly the Science and Media Centre Student Assistants.
To be confirmed in course syllabus.
Week 1 – Intro & Philosophy and Method of Mathematics and Science
Week 2 – Measurement (Numbers to Things)
Week 3 –Linear relationships
Week 4 – Graphical analysis and quadratic functions
Week 5 – Exponentials and Logarithms
Week 6 – Introduction to methods and meaning of calculus
Week 7 – Midsemester Exam Review
Week 8 – Midsemester Exam
Week 9 – Statistical inference and basic probability theory
Week 10 – Descriptive Statistics: Probability distributions and moments of the distribution
Week 11 – Inferential statistics
Week 12 – Hypothesis testing & Confidence Intervals
Week 13 – Basic statistics for Linear Regression
Week 14 – Understanding Regression
Week 15 – Final Exam Review
Week 16 – Final Exam & DAI/Numeracy Conference
Preparation for first session
Students should prepare the first sessions readings, which will consist of a number of short chapters from the reader and a supplementary reading. Details will be communicated to students before the beginning of session.