統計與統計計算語言 R
Statistics and Statistical Computing Language R
Instructor:
Time:
Room:
Credits:
E-mail:
Chen-Chao Tao
Monday 1:20-5:20 pm
Office:
HK222
Office Phone: 31540
Office Hours: Monday 11:00-13:00 and by
appointment
4
taoc@nycu.edu.tw
I usually respond to students’ e-mails within 24 hours of receipt. If 48 hours
pass, please resend your e-mail.
TAs
Molly Huang (黃莫勛)
mo312483015.hk12@nycu.edu.tw
Sonia Hsiao (蕭羽彤)
sonia.hk12@nycu.edu.tw
Course description
This course aims to offer a solid introduction to and integration of statistical thinking and
statistical computing language R. Statistical thinking is a way of knowing to facilitate decisionmaking in the face of uncertainty and constitutes the foundation of science. Given that data
have permeated every section of society, how to make sense of data and translate them into
actionable insight is an indispensable ability for the future world. Moreover, you will learn R for
statistical analysis in step. R is a free software across multiple operating systems, designed
specifically for statistical computing and graphics with interactive features. It suits the needs of
expert number-crunchers as well as amateur data analysts. Real world datasets from Taiwan
Communication Survey and media industry may be used to foster your competence.
Source: http://timoelliott.com/
Statistics and statistical computing language R 2
Goals and Objectives
By the completion of this course, students should be able to:
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▪
▪
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Understand fundamental concepts and procedures in statistical methods.
Critically evaluate and discern research claims and statistical analyses encountered in
everyday life and professional work.
Perform statistics using R and RStudio and become a coder.
Choose appropriate statistical methods to solve real-world problems.
Required Textbooks
Howell, D. C. (2017). Fundamental statistics for the behavioral sciences (9th ed.). Boston, MA:
Cengage Learning.
Recommended Textbooks
Salkind, N. J., & Shaw, L. A. (2019). Statistics for people who (think they) hate statistics using R.
Sage.
Required Software
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R (https://www.r-project.org/) and RStudio (https://posit.co/)
Grading/Evaluation
Assignment
Exercises
Labs
Exam 1
Exam 2
Exam 3
Percent of grade
36%
12%
20%
20%
20%
Due date
Weeks 1-5, 7, 9-10, & 12-15
Weeks 1-5, 7, 9-10, & 12-15
Week 6 (Chs. 1-8)
Week 11 (Chs. 9-11)
Week 16 (Chs. 19, 20, 12- 14, 16)
Overview of Assignments
Submission format: Unless otherwise specified, all assignments must be completed in R
markdown. Use your student ID as the filename. The HTML file generated from the R
Markdown file must be submitted on E3. No scanned images are allowed.
Collaboration policy: You are encouraged to discuss assignments with classmates. However,
anything submitted (including both code and written text) must reflect your original work in
your own words. Note that copying others’ work and allowing others to copy your work will
both get zero.
Chen-Chao Tao
Statistics and statistical computing language R 3
Labs. There will be around 50-100-minute lab sessions every class period. Unless otherwise
specified, all lab guides must be completed and due at 18:30 on the day they are released. Lab
work cannot be made up. The work you submit for grading must be written up independently.
Exercises. Problem sets will be announced after each lecture and due next week before
class. The work you submit for grading must be written up independently.
Exams. There will be three exams during the semester, consisting of mainly short
answer/analysis-based questions. Each exam will not be cumulative; it will focus on course
material taught after the previous exam. However, many concepts tested on the later exams
will build on concepts tested on the earlier exams. All exams include two parts: the first part is
close-book and close-note and takes about 1-2 hours; the second part is open-book and opennote and take about 2-3 hours. There will be a 10-minute break between these two parts.
Calculators, cellphones, and any other technologies are not acceptable. Each exam covers
approximately one-third of the material (150-200 pages). You are responsible for attending
examinations as scheduled. No make-up exam will be given. Generative AI tools are not allowed
during exams.
Required online supplement
By enrolling in this course, you are required to join the Facebook group “Statistics and
Statistical Computing Language R.” It is an online community where all members of this course
can ask questions, provide possible solutions, and share experiences.
Course Policies
Academic misconduct
Late work
Technology
AI policy
Food
Chen-Chao Tao
Plagiarism, cheating on exams, and copying of exercises will be
subject to disciplinary action.
All assignments must be turned in electronically (and in hard copy
if noticed) on time. Assignments turned in after the due day will be
subtracted 10 points (one full grade) for each day late.
No electronic equipment is allowed during lecture. All cell or
smartphones, tablets, and notebooks must be turned off.
Generative artificial intelligence (generative AI) tools (such as
ChatGPT, Claude, Gemini, and others) have been gradually
available for wide use. Academic research is not an exception. You
may use generative AI tools for debugging and learning. Be aware
of the hallucination produced by generative AI tools. But all your
assignments submitted in this course must be your own. The use
of generative AI tools to complete any part of your work will be
treated as plagiarism.
You may not eat during class.
Statistics and statistical computing language R 4
Course Schedule and Readings
Week Date
Topic
1
0217
Introduction & Overview
Reading(s)
Howell, Ch 1 (17 pages)
Assignments
Exercise 1
Howell, Ch 2 (17 pages)
Howell, Ch 3 (29 pages)
Howell, Ch 4 (16 pages)
Howell, Ch 5 (28 pages)
Howell, Ch 6 (22 pages)
Howell, Ch 7 (20 pages)
Exercise 2
Howell, Ch 8 (33 pages)
Exercise 5
Howell, Ch 9 (43 pages)
Exercise 6
2
0224
3
0303
4
0310
5
0317
6
0324
Basic Concepts &
Displaying data
Measures of Central
Tendency & Variability
The Normal Distribution &
Basic Concepts of
Probability
Sampling distribution and
Hypothesis testing
Exam 1
7
0331
Correlation
8
0407
Spring break!
9
0414
Regression
Howell, Ch 10 (44 pages)
Exercise 7
10
0421
Multiple regression
Howell, Ch 11 (34 pages)
Exercise 8
11
0428
Exam 2
12
0505
0512
Howell, Ch 19 (29 pages)
Howell, Ch 20 (20 pages)
Howell, Ch 12 (35 pages)
Exercise 9
13
14
0519
15
0526
16
0602
Chi-Square
Nonparametric Statistics
Hypothesis Tests Applied
to Means: One Sample
Hypothesis Tests Applied
to Means: Two
Related/Independent
Samples
One-Way Analysis of
Variance
Exam 3
Chen-Chao Tao
Exercise 3
Exercise 4
Exercise 10
Howell, Ch 13 (17 pages)
Howell, Ch 14 (27 pages)
Exercise 11
Howell, Ch 16 (44 pages)
Exercise 12