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Our main text will be OpenIntro Statistics (3rd edition), a free, open-source textbook. You can download a PDF of the book for free or buy a low-cost paperback. There is also a PDF optimized for tablets. The purpose of the readings is to make the lecture and exercises in class easier to understand. In the schedule, below, the chapters refer to this textbook. I've also prepared an online primer, An Introduction to Data Analysis & Presentation, to help you learn the material. In addition, I have made available, in the comment below, some additional readings on the topics we will discuss.

The link to the online bookstore for this course:

Read the textbook and 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 an open source statistical programming language, R, to do data analysis in this class. R is free and you can install it on your own computer (Windows, Mac, Linux) if you wish. The link to the R project site includes an online users guide. If you have a laptop and would like help installing R, bring your computer to office hours and I would be happy to assist you.


(1) M 1/29. Introduction to the course.

Part I. The Fundamentals
(2) W 1/31. Introduction to quantitative analysis. Read Introduction, Methodological Issues.

(3) M 2/5. Introduction to R. Read An Introduction to R, 1-32.
(4) W 2/7. More about programming. Read Data Structures in R.

Lab 1. The syntax of R. Due W 2/14.

(5) W 2/14. Measurement. Read Measurement, Data, and chapter 1. Learning objectives

(6) T 2/20. Probability. Read Sampling, Probability, and chapter 2 (slides). Learning objectives. * Conversion day: Monday classes
(7) W 2/21. More on probability. Read Categorical Data.

Lab 2. Probability. Due W 2/28.

(8) M 2/26. Descriptive statistics, part I. Read Central Tendency.
(9) W 2/28. Descriptive statistics, part II. Read Variability.

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.

(14) M 3/19. Inference, part II. Read The Confidence Interval, and chapter 4 (slides). Learning objectives, Learning objectives.
(15) W 3/21. *** College closed due to winter storm. ***

(16) M 3/26. *** Introduction to hypothesis testing. Read T-test.
(17) W 3/28. *** Comparing more than two means. Read Analysis of Variance.

(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
(23) M 4/30. Bivariate and partial correlations. Read Correlation Coefficients,
(24) W 5/2. Bivariate regression. Read Linear Regression.

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.