DKU STATS 101 ● Fall 2 2022
Intro to Applied Statistical Methods
|Schedule||Click here to jump to the weekly schedule|
|Instructor||Sjur Hamre (firstname.lastname@example.org)|
|Textbook||Intro Stats, 6th Ed. by De Veauz, Velleman, and Bock.|
What is this course about?
How can we use data to shed light on age-old and new human problems such as pollution, discrimination, and economic growth? How can we be “sure” that the evidence we have points us in the right direction? How meaningful are our findings? Do our results suggest the relationships we find between factors such smoking and cancer are meaningful or meaningless? How would we know? How should we properly display and explain our statistical results to communicate our findings on important issues such as these?
What will I learn?
By the end of this course, successful students will be:
- Discerning consumers of quantitative research, able to:
- Describe the central tendencies and distributions of variables, and the relationships between multiple variables, including substantive and statistical significance as well as causality; and
- Interpret and critique common statistical analyses found in research literature, and understand how they support (or fail to support) the researchers’ arguments
- Effective producers of basic quantitative research, able to:
- Determine which statistical methods can be used to answer real-world social science questions, and discuss their benefits and limitations;
- Apply common techniques to clean and transform data, and evaluate the impact of common data problems such as missing data and non-random sampling;
- Apply common statistical methods to describe data and make inferences about relationships between variables; and
- Create and accurately interpret standard reports, results tables, and graphs using R and RStudio.
What will I do?
What is a typical week like?
|Do the readings||Syllabus|
|Help forum||Ed Discussion|
|ARC tutoring||Tutoring schedule|
|Pre-lecture quiz||M 12:00||Tests & Quizzes|
|Lecture meeting||M 13:10-15:45||Room 2.5/2.6|
|Lab||T 14:20-15:20||Room 2.5/2.6|
|Office hours||W 10:00-12:00||Sign up here|
|Pre-lecture quiz||W 12:00||Tests & Quizzes|
|Lecture meeting||W 13:10-15:45||Room 2.5/2.6|
|Lab||R 14:20-15:20||Room 2.5/2.6|
|Office hours||R 16:00-18:00||Sign up here|
|Datacamp assignment||F 12:00||Datacamp|
|ARC review session||F 15:00-16:00||Sign up here|
|End-of-week assignment||U 12:00||Assignments|
Green: Class sessions; Yellow: Assignments due; Grey: Optional support
See the course structure page for a detailed description of each activity.
What texts and materials are required?
DKU will provide you with a copy of our textbook, Intro Stats, 6th Ed. by De Veauz, Velleman, and Bock. We will work in RStudio in our labs, and occasionally in lecture; please bring your laptops. R and RStudio are free software; please install them ahead of our first lab by following these instructions.
Additional R resources are available on the resources page. Lecture slides and lab material will be posted to Sakai.
What background knowledge do I need?
This is an introductory course and no specific background knowledge is required. We will typically use applied examples from across the social sciences that are intuitive enough to be readily understood by non-majors.
Where can I learn more?
In this course, we will focus on developing an intuitive understanding of statistical inference and measures of uncertainty, using limited amounts of math. If you are interested in developing this intuitive understanding of statistics further and with more mathematical rigor, and/or to explore a broader range of statistical techniques and applications, I encourage you to consider the following DKU courses:
- MATH 205: The mathematical foundations of statistics
- ECON 203: Advanced study of modern regression techniques
- SOCSCI 350: Advanced statistical techniques applied to real-world problems