Tag(s)
[BSc] M:ID+S+WP+PSc+GPH
Admission Requirements
Numeracy I (2,5 ECTS) and Numeracy II (2,5 ECTS), or Numeracy (5 ECTS)
Description
Today’s world relies much on the accumulation, presentation, and interpretation of large quantities of information. Statistics is a tool that enables us to organize our data in an efficient manner, and provides us with methods that help us understand the relationships that occur in our data and our increasingly complex world. In this course, we will draw examples from multiple disciplines, such as political science, economics, medical sciences, and biology to demonstrate how to search for and evaluate patterns in large amounts of data, as well as to interpret what these patterns tell us about the world. The material in this course proceeds gradually from simple descriptive statistics to more complex inferential statistics, such as multivariate regression. The course will primarily focus on developing substantive and precise understanding of the statistical concepts and their application to real-world examples using elementary computer programming in the R statistical programming package.
Course Objectives
This course aims to build students’ quantitative skills from descriptive (summary) statistics to inferential statistics. This course is designed to be accessible for students at all levels of mathematical skill. The focus is put on developing conceptual understanding of statistics without heavy reliance on rigorous mathematical background. The knowledge obtained from this course should provide solid background for students who wish to continue their statistical education with more advanced courses as well as prepare students to perform their own statistical analyses in their coursework and beyond.
Upon completion the course aims to provide the students with the following skills:
Critically analyze various types of data and learn to select most appropriate statistical technique to answer their research question;
Use statistical programming to enter data, generate descriptive statistics and graphs, perform hypothesis tests and estimate regression models;
Confidently communicate and present statistical results to a variety of audiences – academic experts and policy-makers.
Mode of Instruction
The course will consist of a combination of interactive lectures and lab seminars. Interactive lectures will mainly focus on developing an understanding of the main statistical concepts. Lab seminars are designed for the students to apply these concepts in a series of assignments using R statistical software. Students must bring their laptops for lab seminar.
Assessment
Assessment for this course comprises of a cumulative examination, individual assignments, final report and participation.
Assessment: Participation
Percentage: 10%
Deadline: Ongoing Weeks 1-7
Assessment: 2 pop quizzes
Percentage: 10%
Deadline: Ongoing Weeks 1-7
Assessment: 3 Individual Assignments
Percentage: 30% (each 10%)
Deadline: Weeks 1-7
Assessment: Exam
Percentage: 30%
Deadline: Week 7
Assessment: Final Research Project
Percentage: 20%
Deadline: Week 8
- Exam: The exam will cover the main concepts discussed in class. Students will receive exam sheets to review and practice for the exam.
•Individual assignments: Each individual assignment will have two parts. Part I contains problem sets covering material from class. In Part II, students will apply the skills obtained in the course to a question of their choosing.
•Final research project: Based on the findings from their individual assignments, students will, in the end, compile a research report focused on their chosen question.
•Participation: Participation grade comprises of:
-Lecture: students’ engagement with the material during interactive lecture (5%)
-Lab Seminar (5%): • Submission of all completed R scripts after each lab session (all due on Friday of the same week at midnight). • Introductory quiz. • Demonstrated level of preparedness through active participation in class. • Attendance is mandatory and follows the rules stipulated in Student Handbook. Students are advised to consult their Handbook for details.
NOTE! However, due to the high intensity of this course, missing ANY classes is highly inadvisable, as it tends to be strongly detrimental to the students’ performance in this class.
ASSIGNMENT SUBMISSION POLICY
All assignments will be submitted through Blackboard by midnight (23:59 pm) of the respective day they are due.
ACADEMIC HONESTY
LUC’s Honour code is stipulated in the LUC Student Handbook.
Literature
Required reading:
- Jessica Utts, Robert F. Heckard. 2011. Mind on Statistics, International Edition, Paperback. Thomson/Brooks/Cole.
OR
- Jessica Utts, Robert F. Heckard. 2012. Mind on Statistics, 4th and International Edition, Paperback. Thomson/Brooks/Cole.
Recommended readings:
The Chicago Guide to Writing About Numbers (2004), Haack, Dennis, University of Chicago Press – 1st edition.
Michael J. Crawley. 2005. Statistics: An Introduction using R, 1st Edition. Wiley-Blackwell, Paperback.
Freedman, D., Pisani, R., and Purves, R. 2007. Statistics. Norton: New York, and London, 4th edition.
Contact Information
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Weekly Overview
Week 1: Introduction to Quantitative Methods?
Week 2: Quantitative Data Collection Methods
Week 3: Analyzing Data: Summary and Comparison
Week 4: Inferential Statistics
Week 5: Hypothesis Testing
Week 6: Correlation and Regression
Week 7: Multivariate Regression and Discrete Variables
Week 8: Reading Week.
Preparation for first session
-