Spring 2020  STAT 203 D100
Introduction to Statistics for the Social Sciences (3)
Class Number: 3970
Delivery Method: In Person
Overview

Course Times + Location:
Mo 12:30 PM – 2:20 PM
SSCC 9002, BurnabyWe 12:30 PM – 1:20 PM
SSCC 9002, Burnaby 
Exam Times + Location:
Apr 19, 2020
12:00 PM – 3:00 PM
SSCC 9002, Burnaby

Instructor:
Gaitri Yapa
ggy1@sfu.ca

Prerequisites:
Recommended: 30 units including a research methods course such as SA 255, CRIM 220, POL 200, or equivalent.
Description
CALENDAR DESCRIPTION:
Descriptive and inferential statistics aimed at students in the social sciences. Scales of measurement. Descriptive statistics. Measures of association. Hypothesis tests and confidence intervals. Students in Sociology and Anthropology are expected to take SA 255 before this course. Intended to be particularly accessible to students who are not specializing in Statistics. Students cannot obtain credit for STAT 203 if they already have credit for  or are taking concurrently  STAT 101, 201, 205, 285, or any upper division STAT course. Quantitative.
COURSE DETAILS:
This course may be applied to the Certificate in Liberal Arts
STAT Workshop Coordinator: Marie Loughin
Outline:
This course covers Chapters 19, 11, 12, 1522, and 2427 of the textbook. Chapters 7, 11, 19, and 24 are section reviews (and thus are optional). Details of the other chapters are as follows:
 Descriptive Statistics (Chapters 1, 2, and 4 of text) Basic graphical statistics (e.g. bar graphs, pie charts, histograms, time plots, scatterplots) and basic numerical statistics (e.g. mean, median, mode, quartiles, standard deviation, correlation) are discussed. Scales of measurement are distinguished (e.g. nominal, ordinal, ratio and interval).
 Probability (Chapters 3 and 12 of text) The normal distribution is introduced along with probability rules.
 Sampling (Chapter 8 of text) Various sampling designs such as simple random sampling are discussed. The implementation of sampling procedures is also presented.
 Experiments and Observational Studies (Chapters 8 and 9 of text) The design of experiments is introduced with an emphasis on randomization, treatments, subjects, factors, pairing and controls. Comparisons are made with observational studies.
 Inference (Chapters 15, 16, 17, 18) Concepts related to the construction of confidence intervals (e.g. sampling distributions, confidence level, width, interpretation, the effect of sample size) are discussed. Also basic concepts related to the testing of hypotheses (e.g. hypotheses, pvalues, statistical significance) are presented.
 Estimation and Testing for One Sample Problems (Chapters 20 and 22 of text) Procedures for means and proportions are discussed with an emphasis on the use of statistical software and the interpretation of results.
 Estimation and Testing for Two Sample Problems (Chapters 21 and 23 of text) Procedures for means and proportions are discussed with an emphasis on the use of statistical software and the interpretation of results.
 One Way ANOVA (Chapter 27 of text) One way analysis of variance procedures are discussed with an emphasis on implementation using statistical software and the interpretation of results.
 ChiSquare Tests (Chapters 6 and 25 of text) Procedures for testing in contingency tables are discussed with an emphasis on the use of statistical software and the interpretation of results. Measures of association are discussed.
 Regression (Chapter 5 and 26 of text) Simple linear regression is introduced with an emphasis on carrying out regression on actual data using statistical software and the interpretation of results. Related concepts including residuals, least squares fit, testing and the construction of confidence intervals is addressed.
Grading
 Participation: inclass participation and performance via i>clicker and inclass activity worksheets 5%
 Weekly Online Quizzes 5%
 Weekly Written Assignments 5%
 Biweekly inclass Lab Assignments 5%
 Midterm Exams (4) 40%
 Final Comprehensive** Exam (twostage) (**you must pass the final exam to pass the course) 40%
NOTES:
Materials
MATERIALS + SUPPLIES:
R can be accessed via Jupyter, an online platform, at https://sfu.syzygy.ca/. Alternatively, R Studio and R statistical software can be downloaded free of charge from https://www.rstudio.com/ and https://cran.rproject.org/, respectively.
iClickers will be used in this course and are available through the SFU Bookstore.
REQUIRED READING:
Required Textbook:
The Basic Practice of Statistics (8th ed.) & Sapling Plus (Sapling Plus is recommended, but not required) by D. S. Moore, W. I. Notz, and M. A. Fligner. Publisher: W.H. Freeman Publishers
Looseleaf ISBN: 9781319188658 (available at SFU Bookstore)
Other options are available through the MacMillan Learning website.
Department Undergraduate Notes:
Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 7787823112 or csdo@sfu.ca
Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/needatutor.html. We accept no responsibility for the consequences of any actions taken related to tutors.
Registrar Notes:
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s1001.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS