Read the online material before the class meeting in which we will begin discussing it; journal articles may be obtained using the citation databases on the BC Library website.
We will be using a open source statistical programming language, Python, to do data analysis in this class. Python is free and you can install it on your own computer (Windows, Mac, Linux) if you wish, or you can run it online using Google’s Colaboratory.
The course is structured as a series of modules, called units, that have a consistent structure: intake, process, demonstrate. Each unit will have one or more laboratory assignments in which you will do some data analysis with Python and write about what you’ve learned. The units will incorporate in-class meetings and outside-of-class activities; each week will also include a learning reflection which will count as participation in the course. In the schedule below, synchronous activities will be displayed in all caps and asynchronous activities will be displayed in sentence case. This is to make it easier to spot the synchronous work each week.
M 1/29. (0.1) Introduction to the course.
Part I. The Fundamentals
Unit 1. Concepts and Tools
M 2/5. (1.2) Introduction to Python. Read . PROCESS: GUIDED TOUR OF GOOGLE’S COLABORATORY. Do: Reflection #2
W 2/7. (1.3) More about programming. Read . PROCESS: AN INTRODUCTION TO PYTHON SYNTAX. Do: Reflection #3, Lab 1. Python notebooks..
Unit 2. Probability
T 2/20. (2.1) Probability. Read Sampling, Probability. PROCESS: CLASS PRESENTATION AND DISCUSSION OF PROBABILITY AND SAMPLING. Do: Reflection #5.
W 2/21. (2.2) More on probability. Read Categorical Data. PROCESS: COMPUTATION OF PROBABILITY AND ODDS. Do: Reflection #6, Lab 2. Probability.
Unit 3. Describing a Sample
For this section, also read: Massoni (see comment, below).
(10) 3/5. (Finish up descriptive statistics.) Review for midterm examination 1.
(11) W 3/7. Midterm examination 1. *** College closed due to winter storm. ***
Part II. Inference with Means and Percentages
(12) M 3/12. *** Rescheduled Midterm 1 *** Programming part of the exam will be due by the start of class on M 3/19.
(13) W 3/14. Inference, part I. Read The Normal Curve, Sampling Distributions, and chapter 3 (slides).
Lab 3. Descriptive statistics. Due M 3/26.
(18) M 4/9. Factorial analysis of variance. Read Factorial ANOVA, chapter 5.
Lab 4. Inference (standard scores, z-test, confidence interval). Due W 4/11.
Lab 5. Comparing means (t-test, F-test). Due W 4/18.
(19) M 4/16. More on factorial analysis of variance.
(20) W 4/18. Even more on factorial analysis of variance.
For this section, also read: Howard, et al. (See comment, below).
(21) M 4/23. Review for midterm examination 2.
(22) W 4/25. Midterm examination 2.
Part III. The Linear Model
Lab 6. Bivariate correlation and regression. Due W 5/9.
(25) M 5/7. The linear model. Read Multiple Linear Regression chapter 7.
(26) W 5/9. Recoding and indexing. More on the linear model.
(27) M 5/14. Even more on the linear model.
Lab 7. The linear model. Due W 5/16
@ (28) W 5/16. Review for the final examination.
For this section, also read: Perez (See comment, below).
TH 5/17. Reading day
Final examination. Distributed: W 5/16. Due: W 5/23.