This course is designed to help students become better teachers and communicators of statistics. Students will be introduced to innovative pedagogical approaches for teaching college level statistics, with an emphasis on introductory statistics education. Course will be divided into three units:
The course will be based primarily on discussion, practice teaching, and feedback.
The course counts as one of the two pedagogy courses required for the graduate school’s Certificate in College Teaching.
Tuesdays, 10:05 - 11:20am at Old Chem 025
By appointment
Assessment will be based on participation, improvement shown in sample teaching sessions, and the written teaching statement.
I will regularly send announcements by email, please make sure to check your email daily.
Any non-personal content-related questions should be posted on Piazza. Before posting a new question please make sure to check if a thread on your question exists. If so, pick up the discussion there. You should also answer each others’ questions.
Note that one reason for using Piazza for course discussion is to teach you how you can use online discussion forums effectively in your own teaching.
Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Student Disability Access Office at<PHONE_NUMBER> as soon as possible to better ensure that such accommodations can be made.
Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Cheating on exams and quizzes, plagiarism on homework assignments and projects, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Duke Community Standard, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved as well as being reported to the Office of Student Conduct. Additionally, there may be penalties to your final class grade. Please review the Duke’s Academic Dishonesty policies.
Class attendance is a firm expectation; frequent absences or tardiness will be considered a legitimate cause for grade reduction. You must be in class on a day that you are scheduled to present, there are no make ups for presentations.


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Lorem ipsum dolor sit amet, te sea eirmod eloquentiam. Mei cu illum scaevola, et verterem constituam mei. Tantas feugiat vis ex, unum saperet mandamus has te. Cum cibo solum recusabo et, nam saepe prompta an, mnesarchum posidonium in sed. An purto viderer persecuti vel, inani veniam recteque sed an.
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Pro eu odio tamquam theophrastus, nam ne tota minimum cotidieque. Vel ad congue verterem definiebas. Ne usu dicta utamur virtute, omnis eligendi nominati eos ex. Sonet volumus efficiantur id pro.
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No per postea noster referrentur, ea tollit mollis mei. Delicata inimicus suscipiantur quo te, eos et wisi lorem, te inimicus neglegentur contentiones his. Ut choro homero sea. Partiendo definitiones sea te. No vel probo mandamus, id atqui iusto comprehensam quo, eam an deserunt mnesarchum.
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I
scarcely know where to begin, though I sometimes facetiously place the cause of it all to Charley
Furuseth's credit. He kept a summer cottage in Mill Valley, under the shadow of Mount Tamalpais, and never
occupied it except when he loafed through the winter mouths and read Nietzsche and Schopenhauer to rest
his brain. When summer came on, he elected to sweat out a hot and dusty existence in the city and to toil
incessantly. Had it not been my custom to run up to see him every Saturday afternoon and to stop over till
Monday morning, this particular January Monday morning would not have found me afloat on San Francisco Bay.
Not but that I was afloat in a safe craft, for the Martinez was a new ferry-steamer, making her fourth or
fifth trip on the run between Sausalito and San Francisco. The danger lay in the heavy fog which blanketed
the bay, and of which, as a landsman, I had little apprehension. In fact, I remember the placid exaltation
with which I took up my position on the forward upper deck, directly beneath the pilot - house, and allowed
the mystery of the fog to lay hold of my imagination. A fresh breeze was blowing, and for a time I was
alone in the moist obscurity - yet not alone, for I was dimly conscious of the presence of the pilot, and of
what I took to be the captain, in the glass house above my head. I remember thinking how comfortable it
was, this division of labour which made it unnecessary for me to study fogs, winds, tides, and navigation,
in order to visit my friend who lived across an arm of the sea. It was good that men should be specialists,
I mused. The peculiar knowledge of the pilot and captain sufficed for many thousands of people who knew no
more of the sea and navigation than I knew. On the other hand, instead of having to devote my energy to
the learning of a multitude of things, I concentrated it upon a few particular things, such as, for
instance, the analysis of Poe's place in American literature - an essay of mine, by the way, in the current
Atlantic. Coming aboard, as I passed through the cabin, I had noticed with greedy eyes a stout gentleman
reading the Atlantic, which was open at my very essay. And there it was again, the division of labour,
the special knowledge of the pilot and captain which permitted the stout gentleman to read my special
knowledge on Poe while they carried him safely from Sausalito to San Francisco. A red - faced man, slamming
the cabin door behind him and stumping out on the deck, interrupted my reflections, though I made a mental
note of the topic for use in a projected essay which I had thought of calling "The Necessity for Freedom:
A Plea for the Artist." The red - faced man shot a glance up at the pilot - house, gazed around at the fog,
stumped across the deck and back (he evidently had artificial legs), and stood still by my side, legs
wide apart, and with an expression of keen enjoyment on his face. I was not wrong when I decided that
his days had been spent on the sea. "It's nasty weather like this here that turns heads grey before their
time," he said, with a nod toward the pilot - house. "I had not thought there was any particular strain,"
I answered. "It seems as simple as A, B, C. They know the direction by compass, the distance, and the
speed. I should not call it anything more than mathematical certainty." "Strain!" he snorted. "Simple
as A, B, C! Mathematical certainty!" He seemed to brace himself up and lean backward against the air as
he stared at me. "How about this here tide that's rushin' out through the Golden Gate?" he demanded, or
bellowed, rather. "How fast is she ebbin'? What's the drift, eh? Listen to that, will you? A bell-buoy,
and we're a - top of it! See 'em alterin' the course!" From out of the fog came the mournful tolling of a
bell, and I could see the pilot turning the wheel with great rapidity. The bell, which had seemed straight
ahead, was now sounding from the side. Our own whistle was blowing hoarsely, and from time to time the
sound of other whistles came to us from out of the fog. "That's a ferry - boat of some sort," the new-comer
said, indicating a whistle off to the right. "And there! D'ye hear that? Blown by mouth." Some scow
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class: center, middle, inverse, title-slide # Intro to linear models
(for scared ecologists) ## Lesson 3: Analysing count data ### Guy F. Sutton ### Centre for Biological Control, Department of Zoology and Entomology, Rhodes University, Makhanda ### 2021 --- class: title-slide, right, top .right-column[ # Intro to linear models
 (for scared ecologists) ### Lesson 3: Analysing count data ### **Guy F. Sutton**
 *Centre for Biological Control, Department of Zoology and Entomology, Rhodes University, Makhanda*
 2021 ] --- # Count data - Count data is ubiqutous in ecology. -- - e.g. No. of FCM per fruit - e.g. No. of ticks aebra - e.g. Abundances of impala per hectare in the Kruger National Park -- - Counts (or abundances) are defined as non-negative integers. -- - i.e. They cannot take a decimal place -- - e.g. 0 FCM per fruit, 7 ticks on a zebra, 385 impala per hectare -- - NOT: 33.15 psyllids per leaf -- - ALSO NOT: 27% of the flies dead --- # An example - Count the no. of FCM larvae from n = 20 fruits from 10 different citrus varieties (n = 200 fruits in total). -- - Question: *Are FCM abundances higher on some citrus varieties than others?* --  -- .center[What's the big deal?] --- # Counts are not normal (literally) - Counts typically do not conform to assumptions of normality for statistical tests. -- - Count data typically follows a strong mean-variance relationship. -- - In many count datasets, there are many zeros and small counts, and successively fewer larger counts. --  --- # Modelling count data -- - There are two basic options for modelling count data: -- 1. *Poisson GLM* - The poisson distribution assumes the mean = variance. -- 2. *Negative binomial GLM* - Expects more variance than the Poisson. Variance > mean. --  --- # How to fit these models in R? -- - Assuming our question is: *Are FCM abundances [fcm_count] higher on some citrus varieties [citrus_variety] than others?* -- - *Poisson GLM*: `mod_poisson <- glm(fcm_count ~ citrus_variety, family = poisson(link = "log"), data = data)` -- - *Negative binomial GLM*: `mod_nb <- glmmTMB::glmmTMB(fcm_count ~ citrus_variety, family = nbinom2, data = data, REML = FALSE)` -- Let's go to R and fit some of these models.