Syllabus: Statistics with R (200-level)
Serdar Kayavisibility
…
description
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This course introduces students to the scientific approach to social studies. Weekly classes and tutorials are geared toward helping the students gain a basic understanding of systematic social inquiry. In the lectures, students learn about the fundamentals of quantitative research, and accustom to strategies for data analysis, hypothesis testing, and statistical inference. Each lecture is followed by a computer lab session, where students put their knowledge to practice, and perform tasks that revolve around visualizing data, and conducting statistical analyses.
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SYLLABUS
Statistics with R
200-level
instructor Serdar Kaya
email
office
Thursdays, between 12:30pm and 1:30pm office hours
Course Description
This course introduces students to the scientific approach to social studies. Weekly classes and
tutorials are geared toward helping the students gain a basic understanding of systematic social
inquiry. In the lectures, students learn about the fundamentals of quantitative research, and accustom
to strategies for data analysis, hypothesis testing, and statistical inference. Each lecture is followed by
a computer lab session, where students put their knowledge to practice, and perform tasks that revolve
around visualizing data, and conducting statistical analyses.
Hours and Locations
– Lecture: Thursdays, between 10:30am and 12:20pm
– Tutorial: Thursdays, between 1:30pm and 2:20pm (computer lab)
Main Texts
– Agresti, Alan; et al. 2016. Statistics [5th edition]. Pearson.
– Kaya, Serdar. R: An Introduction. (free digital e-book downloadable from Canvas)
– Additional readings downloadable from Canvas.
Recommended Texts
–
–
–
–
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Kellstedt, Paul; and Guy Whitten. 2018. The Fundamentals of Political Science Research [3rd ed.].
Bryman, Alan; and Edward Bell. 2019. Social Research Methods [5th Canadian edition].
Neuman, W. Lawrence; and Karen Robson. 2017. Basics of Social Research [4th Canadian edition].
Leedy, Paul D.; and Jeanne Ellis Ormrod. 2018. Practical Research [12th edition].
Booth, Wayne C., et al. 2016. The Craft of Research [4th edition].
Syllabus, Statistics with R
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Grading
–
–
–
–
Midterm Exam
Homework Assignments
Take-Home Final Exam
Participation
20%
50%
20%
10%
week 7, in class
(a total of ten homeworks, each worth 5% of your grade)
Computer Lab Sessions
– Tutorials will take place in a computer lab. Each session will start with a set of brief instructions on
the use of the R commands. Students will then work on their assigned tasks under supervision.
– A typical lab tutorial will focus on a particular homework assignment, due before the following
Sunday, at 11:55pm, via the online platform. Students are required to complete all homework
assignments using R, which can be downloaded for PC and Mac for free from https://www.rproject.org/
Penalty for Late Submissions
– For late submissions, students will be assessed a penalty of 10% per each calendar day.
– Late submissions will not be accepted after three calendar days.
– Note: All submissions in this course are digital. There will be no hard copies.
Classroom Rules
– Full attendance is necessary for a successful grade.
– Students are expected to do all the readings for the week before coming to class.
– Electronic devices are strictly prohibited. If you require any special accommodation regarding notetaking, please see the instructor after the first class. For the rationale of this policy, see:
Susan Dynarski. 2017. Laptops are Great. but not during a Lecture or a Meeting, The New York
Times, November 22.
Communication
•
The online platform:
Check the online platform regularly for announcements. Use the File section to view or
download lecture slides, instructions on assignments, and other supporting documents.
•
Email:
My policy is to reply to all messages within 24 hours. If you do not receive a reply in 48 hours,
feel free to send me a reminder message.
Centre for Accessible Learning
Students with special needs are encouraged to register with the Centre for Accessible Learning as soon
as possible to ensure that they are eligible for approved classroom or exam accommodations and
services, and that they are implemented in a timely fashion.
Syllabus, Statistics with R
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Academic Dishonesty and Misconduct Policy
All departments follow the university policy on academic dishonesty and misconduct procedures. All
written assignments must cite sources, and include a bibliography that follows a citation style. It is the
responsibility of students to inform themselves of the content of the aforementioned policies.
Grade Appeals
Only final grades or written assignments may be appealed. Grades may be raised, lowered, or remain
unchanged upon review.
– Students must first consult with their instructor, providing a written account of why their grade
should be changed. The grade will be discussed with the instructor informally.
– If Step 1 is unsuccessful, students should submit a completed grade appeal form to the Department
Chair, along with all of the graded material being appealed. The Department Chair will arrange
for a re-evaluation of the work in question and assign a new grade.
– If a student feels their grade appeal has been dealt with inappropriately at the department level, they
may convey their concern to the Dean, who will review and confirm the new grade assigned, or
initiate an alternate means of reconsideration. The decision of the Dean shall be final, subject
only to an appeal to Senate.
Syllabus, Statistics with R
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Course Schedule
WEEK ONE
Introduction. Univariate Statistics.
Required Reading
#1: Statistics: The art and science of learning from data (Agresti & Franklin, Ch. 1)
#2: Exploring data with graphs and numerical summaries (Agresti & Franklin, Ch. 2)
#3: Basics (Kaya, Ch. 1)
#4: Generating numbers, creating variables (Kaya, Ch. 2)
#5: Managing variables (Kaya, Ch. 3)
Recommended Reading
#1: The nature of quantitative research (Bryman and Bell, Ch. 4)
Recommended Online Reading
#1: Frost, Jim. 21 February, 2013. Why statistics is important.
http://blog.minitab.com/blog/adventures-in-statistics-2/why-statistics-is-important
#2: Martz, Eston. 10 August 2016. When should you mistrust statistics?
http://blog.minitab.com/blog/understanding-statistics/when-should-you-mistruststatistics
#3: Martz, Eston. 14 December 2011. Three dangerous statistical mistakes.
http://blog.minitab.com/blog/understanding-statistics/three-dangerous-statisticalmistakes
#4: Martz, Eston. 29 July 2015. 10 statistical terms designed to confuse non-statisticians.
http://blog.minitab.com/blog/understanding-statistics/10-statistical-terms-designedto-confuse-non-statisticians
#5: Martz, Eston. 15 December 2015. Approaching statistics as a language.
http://blog.minitab.com/blog/understanding-statistics/approaching-statistics-as-alanguage
#6: Martz, Eston. 29 June 2016. Those 10 simple rules for using statistics? They're not just for
research. http://blog.minitab.com/blog/understanding-statistics/those-10-simplerules-for-using-statistics-theyre-not-just-for-research
#7: Paret, Michelle. 13 February 2017. Three things a histogram can tell you.
http://blog.minitab.com/blog/michelle-paret/3-things-a-histogram-can-tell-you
WEEK TWO
Bivariate Statistics. Managing Data.
Required Readings
#1: Association: contingency, correlation, and regression (Agresti & Franklin, Ch. 3)
#2: Gathering data (Agresti & Franklin, Ch. 4)
#3: Importing and exporting Data (Kaya, Ch. 4)
#4: Playing with data (Kaya, Ch. 5)
#5: Graphical displays (Kaya, Ch. 6)
Syllabus, Statistics with R
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Recommended Reading
#1: Sampling (Bryman and Bell, Ch. 12)
#2: Getting to know your data (Kellstedt and Whitten, Ch. 5)
#3: Butovsky, Jonah. 2007. "Phony Populism: The Misuse of Opinion Polls in the National
Post." Canadian Journal of Communication 32(1): 91-102.
Recommended Online Reading (spread of data)
#1: Runkel, Patrick. 6 July 2012. Variations on a theme of variation: R, V, SD, SE, and CI.
http://blog.minitab.com/blog/statistics-and-quality-data-analysis/variations-on-atheme-of-variation-r-v-sd-se-and-ci
#2: Paret, Michelle. 22 June 2016. How to identify outliers (and get rid of them).
http://blog.minitab.com/blog/michelle-paret/how-to-identify-outliers-and-get-rid-ofthem
Recommended Online Reading (correlations)
#1: Heckman, Eric. 8 August 2016. Correlation: What It Shows You (and What It Doesn't).
http://blog.minitab.com/blog/starting-out-with-statistical-software/correlation%3Awhat-it-shows-you-and-what-it-doesnt
#2: Keller, Dawn. 23 February 2015.A Mommy’s Look at Scoliosis: A Study in Correlation.
http://blog.minitab.com/blog/adventures-in-software-development/a-mommy
%E2%80%99s-look-at-scoliosis%E2%80%A6a-study-in-correlation
WEEK THREE
Introduction to Probability
Required Reading
#1: Probability in our daily lives (Agresti & Franklin, Ch. 5)
#2: More on graphical displays, and an introduction to probability (Kaya, Ch. 7)
Recommended Reading
#1: Trumbo, Suess and Schupp (2004, Using R to compute probabilities of matching birthdays)
#2: Probability and statistical inference (Kellstedt and Whitten, Ch. 6)
WEEK FOUR
Probability Distributions
Required Reading
#1: Probability distributions (Agresti & Franklin, Ch. 6)
#2: Probability distributions (Kaya, Ch. 8)
Recommended Reading
#1: Prosecutor's fallacy (download from Canvas)
#2: Strategies for analyzing quantitative data (Leedy and Ormrod, Ch. 11)
Syllabus, Statistics with R
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Recommended Online Reading
#1: Meldrum, Karen. 8 August 2011. Every sample statistic is (at least) a little bit wrong.
http://blog.minitab.com/blog/statistics-tips-from-a-technical-trainer/tip-1-everysample-statistic-is-a-at-least-little-bit-wrong
#2: Stone, Bonnie K. 24 October 2016. Random samples and statistical independence.
http://blog.minitab.com/blog/quality-business/common-assumptions-about-datapart-1-random-samples-and-statistical-independence
#3: Paret, Michelle. 15 October 2013. Explaining the central limit theorem with bunnies &
dragons. http://blog.minitab.com/blog/michelle-paret/explaining-the-central-limittheorem-with-bunnies-and-dragons-v2
WEEK FIVE
Sampling Distributions
Required Reading
#1: Sampling distributions (Agresti & Franklin, Ch. 7)
#2: Sampling distributions, Confidence Intervals (Kaya, Ch. 9)
#3: Thornton, Robert J.; and Jennifer A. Thornton. 2004. "Erring on the Margin of Error."
Southern Economic Journal 71(1): 130-135.
Recommended Reading
#1: Structured observation (Bryman and Bell, Ch. 7)
#2: Survey research: structured interviewing & questionnaires (Bryman and Bell, Ch. 9)
#3: Survey research (Neuman and Robson, Ch. 7)
Recommended Online Reading
#1: Meldrum, Karen. 25 October 2011. Reaching a sweet conclusion with confidence intervals.
http://blog.minitab.com/blog/statistics-tips-from-a-technical-trainer/tip-2-a-sweetconclusion-with-confidence-intevals
#2: Meldrum, Karen. 28 January 2013. Gain confidence with confidence intervals.
http://blog.minitab.com/blog/statistics-tips-from-a-technical-trainer/tip-3-gainconfidence-with-confidence-intervals-v2
WEEK SIX
Reading Week
No lecture.
No assigned readings.
Syllabus, Statistics with R
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WEEK SEVEN
Midterm Exam
In-class, during regular lecture time.
No lecture.
No assigned readings.
WEEK EIGHT
Statistical Inference: Confidence Intervals
Required Reading
#1: Statistical inference: confidence intervals (Agresti & Franklin, Ch. 8)
#2: T-distributions (Kaya, Ch. 10)
Recommended Reading
#1: Quantitative data analysis (Bryman and Bell, Ch. 13)
WEEK NINE
Statistical Inference: Significance Tests
Required Reading
#1: Statistical inference: significance tests about hypotheses (Agresti & Franklin, Ch. 9)
#2: Significance tests (Kaya, Ch. 11)
Recommended Reading
#1: Bivariate hypothesis testing (Kellstedt and Whitten, Ch. 7)
WEEK TEN
Comparing Two Groups (t-test, proportion test, McNemar's test)
Required Reading
#1: Comparing two groups (Agresti & Franklin, Ch. 10)
#2: Comparing two means (Kaya, Ch. 12)
#3: Comparing two proportions (Kaya, Ch. 13)
Recommended Reading
#1: Analysis of quantitative data (Neuman and Robson, Ch. 10)
Syllabus, Statistics with R
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Recommended Online Reading
#1: Frost, Jim. 20 April 2016. Understanding t-tests: t-values and t-distributions.
http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-t-tests-tvalues-and-t-distributions
#2: Frost, Jim. 4 May 2016. Understanding t-tests: 1-sample, 2-sample, and paired t-tests.
http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-t-tests%3A-1sample%2C-2-sample%2C-and-paired-t-tests
#3: Frost, Jim. 23 March 2016. The American Statistical Association's statement on the use of p
values. http://blog.minitab.com/blog/adventures-in-statistics-2/the-americanstatistical-associations-statement-on-the-use-of-p-values
#4: Frost, Jim. 5 March 2015. Understanding hypothesis tests: why we need to use hypothesis
tests in statistics.
http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesistests:-why-we-need-to-use-hypothesis-tests-in-statistics
#5: Frost, Jim. 19 March 2015. Understanding hypothesis tests: significance levels (alpha) and
p values in statistics. http://blog.minitab.com/blog/adventures-instatistics-2/understanding-hypothesis-tests%3A-significance-levels-alpha-and-pvalues-in-statistics
#6: Frost, Jim. 17 April 2014. How to correctly interpret p values.
http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-correctly-interpret-pvalues
#7: Frost, Jim. 10 December 2015. Why are p value misunderstandings so common?
http://blog.minitab.com/blog/adventures-in-statistics-2/why-are-p-valuemisunderstandings-so-common
#8: Frost, Jim. 1 May 2014. Not all p values are created equal.
http://blog.minitab.com/blog/adventures-in-statistics-2/not-all-p-values-are-createdequal
#9: Frost, Jim. 2 April 2015. Understanding hypothesis tests: confidence intervals and
confidence levels.
http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesistests%3A-confidence-intervals-and-confidence-levels
#10: Runkel, Patrick. 10 June 2013. What is a t-test? And why is it like telling a kid to clean up
that mess in the kitchen? http://blog.minitab.com/blog/statistics-and-quality-dataanalysis/what-is-a-t-test-and-why-is-it-like-telling-a-kid-to-clean-up-that-mess-in-thekitchen
#11: Frost, Jim. 15 May 2014. Five guidelines for using p values.
http://blog.minitab.com/blog/adventures-in-statistics-2/five-guidelines-for-using-pvalues
#12: Martz, Eston. 5 May 2017. How can a similar p-value mean different things?
http://blog.minitab.com/blog/understanding-statistics/how-can-a-similar-p-valuemean-different-things
#13: Martz, Eston. 20 June 2011. Three things the p-value can't tell you about your hypothesis
test. http://blog.minitab.com/blog/understanding-statistics/three-things-the-p-valuecant-tell-you-about-your-hypothesis-test
#14: Runkel, Patrick. 4 November 2016. What are t values and p values in statistics?
http://blog.minitab.com/blog/statistics-and-quality-data-analysis/what-are-t-valuesand-p-values-in-statistics
#15: Runkel, Patrick. 10 June 2013. What is a t-test? And why is it like telling a kid to clean up
that mess in the kitchen? http://blog.minitab.com/blog/statistics-and-quality-dataanalysis/what-is-a-t-test-and-why-is-it-like-telling-a-kid-to-clean-up-that-mess-in-thekitchen
Syllabus, Statistics with R
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#16: Runkel, Patrick. 8 April 2016. What are degrees of freedom in statistics?
http://blog.minitab.com/blog/statistics-and-quality-data-analysis/what-are-degreesof-freedom-in-statistics
WEEK ELEVEN
Comparing Several Means (ANOVA test)
Required Reading
#1: Analysis of varience methods (Agresti & Franklin, Ch. 14)
#2: Comparing several means (ANOVA) (Kaya, Ch. 14)
Recommended Online Reading
#1: Paret, Michelle. 19 November, 2015. What Is ANOVA? And who drinks the most beer?
http://blog.minitab.com/blog/michelle-paret/what-is-anova-and-who-drinks-themost-beer
#2: Frost, Jim. 18 May 2016. Understanding analysis of variance (ANOVA) and the F-test.
http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-analysis-ofvariance-anova-and-the-f-test
#3: Frost, Jim. 13 November 2014. The power of multivariate ANOVA (MANOVA).
http://blog.minitab.com/blog/adventures-in-statistics-2/the-power-of-multivariateanova-manova
WEEK TWELVE
Association between Two Categorical Variables (chi-squared and Fisher's tests)
Required Reading
#1: Analyzing the association between categorical variables (Agresti & Franklin, Ch. 11)
#2: Chi-squared tests (Kaya, Ch. 15)
Recommended Online Reading
#1: Martz, Eston. 5 October 2012. Using cross tabulation and chi-square: the survey says...
http://blog.minitab.com/blog/understanding-statistics/using-cross-tabulation-andchi-square-the-survey-says
#2: Martz, Eston. 10 December 2012. What statistical hypothesis test should i use?
http://blog.minitab.com/blog/understanding-statistics/what-statistical-hypothesistest-should-i-use
Syllabus, Statistics with R
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WEEK THIRTEEN
Linear Regression
Required Reading
#1: Regression analysis (Agresti & Franklin, Ch. 12)
#2: Multiple regression (Agresti & Franklin, Ch. 13)
#3: Linear regression (Kaya, Ch. 16)
Recommended Reading
#1: Bivariate regression models (Kellstedt and Whitten, Ch. 8)
Recommended Online Reading
#1: Paret, Michelle. 2 June 2016. Regression versus ANOVA: which tool to use when.
http://blog.minitab.com/blog/michelle-paret/regression-versus-anova%3A-whichtool-to-use-when
#2: Frost, Jim. 7 September 2016. How to identify the most important predictor variables in
regression models. http://blog.minitab.com/blog/adventures-in-statistics-2/how-toidentify-the-most-important-predictor-variables-in-regression-models
#3: Frost, Jim. 24 February 2016. Five reasons why your R-squared can be too high.
http://blog.minitab.com/blog/adventures-in-statistics-2/five-reasons-why-your-rsquared-can-be-too-high
#4: Martz, Eston. 29 August 2017. The easiest way to do multiple regression analysis.
http://blog.minitab.com/blog/understanding-statistics/the-easiest-way-to-domultiple-regression-analysis
#5: Runkel, Patrick. 27 January 2017. So why is it called "regression," anyway?
http://blog.minitab.com/blog/statistics-and-quality-data-analysis/so-why-is-it-calledregression-anyway
WEEK FOURTEEN
General Overview and Wrap Up
No assigned readings.
FINAL EXAM PERIOD
April 14-25
TAKE-HOME FINAL EXAM FOR THIS COURSE
Exam starts: Saturday, April 16th, 5pm (time when questions will be uploaded to Canvas)
Exam ends: Sunday, April 17th, 5pm (deadline to submit answers)
-- END OF SYLLABUS --
Syllabus, Statistics with R
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