Page 1

001: Wed and Fri, 12:30–1:30 pm, WS 55

002: Tues and Thurs, 3:30–4:30 pm, NCB 101

See p.6 for lab sections

INSTRUCTOR DETAILS

Name: Jennifer Peter

Departments: Biology, and, Statistical & Actuarial Sciences

Drop-in hours: Mon 2:30–4:30 pm Thurs 9:00—11:00 am Both times in NCB 301L. No appointments need-ed...just ‘drop-in’!

Contact: Please use “Messages” on our OWL site instead of email (my email is too similar to someone else’s, which causes problems); send to Jennifer Peter.

CALENDAR DESCRIPTION

An introductory course in the application of statistical methods, intended for students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Top-ics include sampling, confidence intervals, analysis of variance, regression and correlation. Cannot be taken for credit in any module in Statistics, Actuarial Science, or Financial Modelling.

Antirequisite(s): All other courses in Introductory Statistics (except Statistical Sciences 1023A/B, Statistical Sciences 1024A/B): Economics 2122A/B, Economics 2222A/B, Geography 2210A/B, Health Sciences 3801A/B,MOS 2242A/B, Psychology 2810, Psychology 2820E, Psychology 2830A/B, Psychology 2850A/B, Psychology 2851A/B, Social Work 2207A/B, Sociology 2205A/B, Statistical Sciences 2035, Statistical Sciences 2141A/B, Statistical Sciences 2143A/B, Sta-tistical Sciences 2858A/B, Statistical Sciences 2037A/B if taken prior to Fall 2010, former Psychology 2885 (Brescia), former Statistical Sciences 2122A/B, former Social Work 2205. Prerequisite(s): A full (1.0) mathematics course, or equivalent, numbered 1000 or above. Statistical Sciences 1024A/B can be used to meet 0.5 of the 1.0 mathematics course requirement. Unless you have either the requisites for this course or written special permission from your Dean to enroll in it, you may be re-moved from this course and it will be deleted from you record. This decision may not be appealed. You will receive no adjustment to your fees in the event that you are dropped from a course for failing to have the necessary prerequisites.

Winter 2020 lectures

WHAT’S IN THIS SYLLABUS?

2244 banner graphics © 2018 rscidata.utoronto.ca, used under CC-BY-NC 4.0

Course Materials 2

Expectations 2

Evaluation Scheme 3

How this course works 3

Learning outcomes 4

Assessment Descriptions 5-6

Policies and Supports 7-8

Course Outline 9-10

Have a question? Find the best communication medium below:

Page 2

R logo is © 2016 The R Foundation, used under CC-BY-SA 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/).

SaplingPlus logo is © Macmiillan Learning; used with permission.

COURSE MATERIALS

These materials are “required” in that each student needs access to them to be successful in the course. Whether that access is individual, shared by a group of individuals, or borrowed from the commons (e.g. computer labs, li-braries, etc.) is up to you. In addition to these three main resources, we will occasionally use articles, videos, and applets available freely online to supplement your learning. If you discover any (open access) resources that are helpful to you for this course, I encourage you to share the details with the rest of the class!

OWL Course Site: Biology 2244B 003 FW19 (owl.uwo.ca)

Statistical Software: R (www.r-project.org)

Textbook: Baldi, B. and DS. Moore. 2018. The Practice of Statistics in the Life Sciences. 4th Edition, W.H. Freeman and Company.

You will be asked to read parts of the textbook as independent study. I promote the SaplingPlus version (i.e. 6-month subscription to online portal + ebook) because it is the cheapest option ($83.35 through the UWO Bookstore; search Stat 2244B), has extra prac-tice questions, and I have created topic-related modules within it.

The OWL site is used heavily. It provides: • Lecture slides (PDF format) • Content for independent study

(lecture videos, readings, etc.) • Online Modules for learning to

use the statistical software, R • Lab Assignments (and related

materials) • Access to quizzes and other

graded components • Practice questions • Communication tools

The Lab Assignments require using statistical software—specifically, R (and the highly recommended inte-grated development environment, R Studio available at www.rstudio.com)—to transform, visualize and analyse data. Both software packages are free pro-grams that can be downloaded to your personal computers and are available on campus GenLab com-puters. Instructions for download-ing is on OWL under Labs.

EXPECTATIONS

To help maintain a safe, respectful, and productive community in which we—students and teaching team alike—can take risks in our learning/teaching, tackle challenging concepts, and ultimately grow as scientists, we should en-deavor to follow these mutual expectations:

IN THE ACADEMIC CONTEXT...

In addition to these expectations, there are some not-always-obvious expectations associated with academia where intellec-tual property rights, and academic integri-ty, and confidentiality are important. Ask for written permission before:

• making an audio recording of class; • sharing/reproducing/distributing

course materials (for free or for profit)

Page 3

Assessment Scheme 1 Scheme 2

Clicker Participation 5% 0%

Quizzes 7% 7%

Activities 3% 3%

4 Lab Assignments Highest lab

2nd highest lab 3rd & 4th highest labs

22% total 8% 6%

4% each

27% total 9% 8%

5% each

Fixed Distribution (37%)

Your final grade will automatically be calculated us-ing both Scheme 1 and Scheme 2. Your final course grade will use whichever scheme results in the high-est grade for you as an individual.

Assessment S1 S2 S3 S4

Test 1 10% 0% 10% 0%

Test 2 13% 18% 0% 0%

Final Exam 40% 45% 53% 63%

Flexible Distribution (63%)

Each of the following course items are initially weighted according to Scenario 1 (“S1”) below. Your final grade will automatically be calculated under all four scenarios. Your final course grade will use whichever scenario results in the highest grade for you as an individual.

EVALUATION SCHEME

The evaluation is set up to promote mastery of the material/skills by the end of the course, and to provide oppor-tunities to learn from mistakes. The course evaluation is divided into ‘Fixed Distribution’ and ‘Flexible Distribution’ segments; for more details on each assessment item, see pages 5 and 6.

Why is this so complicated!?

The success-based weighting of Lab Assignments and the Flexible Distribution are set up so you have multiple op-portunities to receive feedback and demonstrate mastery. If you discover your understanding is not complete, and/or you perform below your desired level of success on a particular item of the Flexible Distribution, you still have future opportunities to regain some/all marks associated with those items. Because all assessment items are cu-mulative, the relative weighting of course material is (approximately) equivalent under each scenario.

HOW THIS COURSE WORKS

This course follows a blended learning approach (i.e. online + in-person delivery) that integrates five ’spheres’ of en-gagement. While days/weeks vary, in general, you should expect the following framework:

ACTIVITY

Collect data

Explore applet

PREPARATORY WORK

Read text Watch video

Online R Modules

CLASS (LECTURE/LAB)

Clickers

Discussion

Q&A

Explore data

QUIZ

Try questions

FOLLOW UP

Read text

Practice questions

Complete Assignment

Study/Review

Page 4

LEA

RN

ING

OU

TCO

MES

(LO

s)

This

co

urs

e is

bo

th in

tro

du

cto

ry a

nd

co

mp

reh

ensi

ve, c

on

cep

tual

an

d p

racti

cal.

At

a fu

nd

amen

tal l

evel

, th

e co

urs

e is

org

aniz

ed t

o d

emo

nst

rate

th

at

sta

tisti

cs

is a

sci

enti

fic

dis

cip

line

tha

t ca

n a

nd

sh

ou

ld in

form

res

earc

h a

t a

ll st

ag

es, f

rom

pro

ble

m d

efin

itio

n t

o in

terp

reta

tio

n a

nd

co

ncl

usi

on

. To

re

info

rce

this

ove

r-ar

chin

g le

arn

ing

ou

tco

me,

th

e co

urs

e to

pic

s ar

e o

rgan

ized

aro

un

d a

“b

ackb

on

e” b

ased

on

th

e P

PD

AC

fra

mew

ork

fo

r sc

ien

tifi

c in

qu

iry

(Mac

Kay

an

d O

ldfo

rd

200

0).

M

ore

sp

ecifi

cally

, by

the

en

d o

f th

e co

urs

e, y

ou

sh

ou

ld b

e ab

le t

o:

A. D

esig

n s

amp

ling

and

st

ud

y p

roce

du

res

to

colle

ct r

ele

van

t d

ata

to

add

ress

a r

ese

arch

q

ues

tio

n.

B. C

reat

e an

d in

terp

ret

app

rop

riat

e su

mm

arie

s o

f d

ata.

C. A

nal

yse

dat

a u

sin

g in

fere

nce

pro

ced

ure

s to

ad

dre

ss a

re

sear

ch

qu

esti

on

.

F. D

esc

rib

e m

od

els

and

/o

r co

nce

ptu

al b

ack-

gro

un

d f

or

com

mo

n

infe

ren

ce p

roce

du

res.

D. U

se s

tati

stica

l so

ft-

war

e t

o e

xplo

re, s

um

-m

ariz

e, a

nal

yse,

an

d

inte

rpre

t d

ata.

E. C

om

mu

nic

ate

stati

s-ti

cal c

on

cep

ts, a

nal

-ys

es, a

nd

arg

um

ents

in

an a

ccu

rate

an

d s

cho

l-ar

ly m

ann

er.

A1

. Dis

tin

guis

h

amo

ng

sam

plin

g an

d

stu

dy

des

ign

s.

A2

. Id

enti

fy is

sue

s as

soci

ated

wit

h

sam

plin

g an

d s

tud

y d

esig

n (

e.g.

bia

s,

valid

ity,

co

nfo

un

d-

ing)

A3

. Id

enti

fy r

ele

van

t in

fere

nce

pro

ce-

du

res

bas

ed o

n r

e-

sear

ch q

ue

stio

n a

nd

va

riab

les.

B2

. In

terp

ret

sum

-m

arie

s to

iden

tify

an

d/o

r d

escr

ibe

patt

ern

s, t

ren

ds,

an

d in

tere

stin

g fe

a-tu

res

in d

ata.

B1

. Sel

ect

sum

mar

-ie

s b

ased

on

re-

sear

ch q

ue

stio

n a

nd

va

riab

les.

C1

. Sel

ect

app

rop

ri-

ate

infe

ren

ce p

roce

-d

ure

s to

ad

dre

ss a

re

sear

ch q

ues

tio

n.

C2

. In

terp

ret

and

d

escr

ibe

con

fid

ence

in

terv

als

an

d h

y-p

oth

esis

te

st r

esu

lts

(an

d a

sso

ciat

ed

con

cep

ts).

D1

. Use

R t

o c

reat

e an

d m

od

ify

grap

h-

ical

an

d n

um

eric

al

sum

mar

ies

of

dat

a.

D2

. Use

R t

o c

on

-d

uct

co

mm

on

infe

r-en

ce p

roce

du

res,

in

clu

din

g ev

alu

atin

g co

nd

itio

ns

for

mo

d-

el fi

t.

D3

. In

terp

ret

R o

ut-

pu

t (i

ncl

ud

ing

ac-

com

pan

yin

g co

de)

co

rrec

tly.

E1. A

pp

ly v

oca

bu

-la

ry a

ccu

rate

ly t

o

des

crib

e st

atisti

cal

con

cep

ts, p

roce

-d

ure

s, a

nd

idea

s.

E2. A

pp

ly c

on

ven

-ti

on

al f

orm

ats

for

rep

orti

ng

and

inte

r-p

reti

ng

resu

lts

of

stati

stica

l an

alys

es

in w

ritt

en/g

rap

hic

fo

rm.

E3. J

usti

fy t

he

cho

ice

of

stati

stica

l p

roce

du

res

(e.g

. se

lect

ed s

tud

y d

e-si

gns)

.

F1. D

esc

rib

e th

e m

od

els

for

com

mo

n

infe

ren

ce p

roce

-d

ure

s.

F2. D

esc

rib

e sa

m-

plin

g d

istr

ibu

tio

ns

(bas

ed o

n s

imp

le

ran

do

m s

amp

les)

fo

r co

mm

on

ly u

sed

st

atisti

cs.

F3. D

iscu

ss a

nd

de-

scri

be

issu

es a

sso

-ci

ated

wit

h in

fer-

ence

(e.

g. p

ow

er,

p

reci

sio

n, T

ype

I/II

er

rors

).

C3

. Eva

luat

e th

e fi

t o

f m

od

els

for

com

-m

on

infe

ren

ce p

ro-

ced

ure

s.

Ma

cKa

y, R

.J. a

nd

R.W

. Old

ford

. 20

00

. Sci

enti

fic

met

ho

d, s

tati

stica

l met

ho

d, a

nd

th

e sp

eed

of

ligh

t. S

tati

stica

l Sci

ence

15

(3):

25

4-2

78

.

C4

. Id

enti

fy s

itu

a-ti

on

s an

d d

ata

that

re

qu

ire

alte

rnati

ve

(i.e

. no

t co

vere

d in

th

is c

ou

rse

) in

fer-

ence

pro

ced

ure

s.

Page 5

ASSESSMENT DESCRIPTIONS

CLICKER PARTICIPATION

WHY? Instant feedback on learning. Provoke discussion & engagement during class.

WHAT? Multiple choice questions during class. Students must attend their registered lecture section.

HOW? iClicker app (free); account set-up information under Administration on OWL site.

GRADES. Awarded for participation (not correct answers). Full participation in each class is worth 1 point; you earn a proportional fraction of the point for a given class based on the number of clicker questions you answer during the class. The percentage of points you earn across the course puts you in one of 6 categories (see table) to determine your final grade.

ACCOMMODATION? Built into the grading scheme (i.e. the 20% margins) and should cover occasional lapses in participation and/or technical problems. If you have circumstances that cause larger ‘absences’, please obtain relief/consideration from Academic Counseling.

% points earned

Grade /5%

0 0

0 < % < 20 1

20 ≤ % < 40 2

40 ≤ % < 60 3

60 ≤ % < 80 4

80 ≤ % ≤ 100 5

QUIZZES

WHY? Feedback on independent learning/preparatory work. Accountability for class preparation.

WHAT? Multiple choice and/or numeric response questions. Not meant to reflect exam-style questions.

HOW? Quizzes & Activities tool on OWL site. Available for ~36 h before deadline; limited time for completion once started. One deadline for ALL students.

GRADES. Marked for correct answers. The percentage of quiz questions answered correctly across the entire term places you in one of 8 categories (see table) to determine your final grade.

ACCOMMODATION? Built into the grading scheme (i.e. the ~14% margins) and should cover occasional missed quizzes and/or tech-nical problems. For more extenuating circumstances, please obtain relief/consideration from Aca-demic Counseling.

% questions correct

Grade /7%

0 0

0 < % < 14 1

14 ≤ % < 28 2

28 ≤ % < 42 3

42 ≤ % < 56 4

56 ≤ % < 70 5

70 ≤ % < 85 6

85 ≤ % ≤ 100 7

ACTIVITIES

WHY? Collect data for use & discussion in class. Pro-mote active learning of core concepts.

WHAT? Structure varies: instructions for each activity will be provided on OWL site. Often involves using an applet to collect some information/data.

HOW? Responses submitted via Quizzes & Activities tool on OWL site. Typically available for ~36 h before deadline (if not longer).

GRADES. Points are awarded for completion with plau-sible responses; instructions will specify exact require-ments. The percentage of points collected (out of those offered) across the course places you in one of 4 categories (see table) to determine your final grade.

ACCOMMODATION? Built into the grading scheme (i.e. the 33% margins) and should cover occasional missed activities and/or technical prob-lems. For more extenuating cir-cumstances, please obtain re-lief/consideration from Aca-demic Counseling.

% points earned

Grade /3%

0 0

0 < % < 33 1

16 ≤ % < 67 2

67 ≤ % ≤ 100 3

TESTS and FINAL EXAM

WHY? Assessment of understanding, application, and integration of course material.

WHAT? Multiple choice questions (unless stated otherwise); ~15-20 questions for Tests, ~45 questions for Final Exam.

HOW? Using Scantron sheets during an on-campus, in-person test. Non-programmable calculators permitted.

GRADES. Your mark on the tests/exam is based on the number of correct answers submitted.

ACCOMMODATION? The Tests are part of the “Flexible Distribution” (see p.2); weight from the Tests is automatically redistributed (see p. 3) so no relief/consideration from counseling or Self-Reporting is necessary for missed Tests. Stu-dents who miss the Final Exam must obtain relief/consideration from Academic Counseling.

Page 6

DID YOU KNOW?

LAB ASSIGNMENTS

WHY? Assessment of your application of course concepts in an authentic manner, including use of statistical software (R).

WHAT? 4 assignments, each composed of ~4 multi-part, short-answer questions requiring written responses (including graphs and R code/output). Assignments move progressively through the phases of the PPDAC framework (MacKay & Old-ford 2000), and involve answering questions that relate to an overall research objective and set of research questions. Suc-cess on assignments will involve applying course concepts in a novel setting, and demonstrating appropriate use of R.

HOW? To earn credit, assignments are submitted as a PDF file to both (1) “Assignments (OWL Submissions)” on the OWL site and (2) Gradescope. The submission process will be discussed during the first in-lab session.

Working with data in R (as assessed through Lab Assignments) is an essential requirement. To pass 2244, you must:

MacKay, R.J. and R.W. Oldford. 2000. Scientific method, statistical method, and the speed of light. Statistical Science 15(3): 254-278.

COMMENTS ON 2244 GRADES AND EVALUATION

The assessment weights have been set to: • recognize the workload of each component; • highlight the relative importance to the learning outcomes for the course; • acknowledge that mastery develops over time.

The evaluation scheme is also set up with an awareness that we aren’t ‘perfect’ every day, and some of our not-so-good days may coincide with a test or assignment deadline. The scheme, therefore, places higher value on your best work. Because this structure is already in place, I do not re-weight assessments, nor accept additional/revised work, to accommodate poor performance and/or non-relieved absences. For reference, I also do not bump indi-vidual grades (e.g. to meet program cut-offs), nor force grades to follow a particular distribution.

• submit and receive a non-zero grade for at least 3 of the 4 Assignments, and,

• Earn an overall passing grade for the lab assignments component of the course.

Otherwise, your final course grade will be recorded as 45% (or your cal-culated grade—whichever is lower).

Section Day Time Location

003 Tues 6:30-9:30 pm HSB-14

004 Tues 6:30-9:30 pm NCB-105

005 Wed 6:30-9:30 pm HSB-16

006 Wed 6:30-9:30 pm HSB-14

007 Wed 6:30-9:30 pm NCB-105

008 Thurs 6:30-9:30 pm HSB-13

009 Thurs 6:30-9:30 pm HSB-16

011 Tues 1:30-4:30 pm HSB-16

012 Wed 1:30-4:30 pm HSB-14

013 Thurs 6:30-9:30 pm NCB-105

014 Tues 1:30-4:30 pm HSB-14

ACCOMMODATIONS? For any missed Lab Assignment deadline, you must obtain relief/consideration from Academic Counseling or through Self-Reporting, otherwise a grade of zero will be awarded. Self-reporting for an Assignment results in either a deadline extension to 24 h after the 48-h self-report period ends, or, relieves the student from completing the Assignment (whichever is preferred). One missed and relieved assignment will be given a grade equal to the mean of the other non-relieved assignments, and assigned the lowest weight (see p. 3). If more than one assignment is missed with relief, a final course grade of Incomplete will be issued, and the student will be required to make up the relieved assignments in the future.

LAB EXEMPTION. If you previously took Biol/Stat 2244 after August 2016, you may be eli-gible for a lab exemption. More details on the OWL site under Administration. Decision to take the exemption is due before the dead-line for Lab Assignment 1.

GRADES. Marked for correct/valid application of course concepts and use of R. Regrade requests must be made through Gradescope within one week of receiving graded assignment.

Page 7

POLICIES AND SUPPORTS

Looking for policies, support, or resources? The website for Registrarial

Services is http://www.registrar.uwo.ca

Submit your own work. Scholastic offences are taken seriously and students are directed to read the appropriate policy, specifically, the definition of what constitutes a Scho-lastic Offence, at this website: http:// www.uwo.ca/univsec/pdf/academic_policies/appeals/scholastic_discipline_undergrad.pdf.

Sick or unable to complete course requirements?

If you are unable to meet a course requirement due to illness or other serious circumstances, you must seek approval for the absence as soon as possible. Approval can be granted either through a self-reporting of absence or via the Dean’s Office/Academic Counselling unit of your Home Faculty. If you are a Science student, the Academic Counselling Office of the Faculty of Science is located in NCB 280, and can be con-tacted at scibmsac@uwo.ca. For further infor-mation, please consult the university’s policy on academic consideration for student absences: https://tinyurl.com/AcademicConsiderations

Use your UWO email. In accordance with policy, http://www.uwo.ca/its/identity/activatenonstudent.html, the centrally administered e-mail account provided to students will be considered the individual’s official university e-mail address. It is the re-sponsibility of the account holder to ensure that e-mail received from the University at his/her official university address is attended to in a timely man-

Missed the Final Exam? Heavy exam load?

If you miss the Final Exam, please contact your faculty’s Academic Counselling Office as soon as you are able to do so. They will assess your eligibility to write the Special Exam (the name given by the university to a makeup Final Exam). You may also be eligible to write the Special Exam if you are in a “Multiple Exam Situation” (see http://www.registrar.uwo.ca/examinations/exam_schedule.html)

DID YOU KNOW? It is Faculty of Science policy that a student who chooses to write a test or exam deems themselves fit enough to do so, and the student must ac-cept the mark obtained. Claims of medical, physical, or emotional distress after the fact will not be considered.

Eyes on your own test. Computer-marked, multiple-choice tests and/or exams may be subject to submission for similarity review by software that will check for unusual coincidences in answer patterns that may indicate cheating.

We use Turnitin. All required papers may be subject to submission for tex-tual similarity review to the commercial plagiarism detec-tion software under license to the University for the detec-tion of plagiarism. All papers submitted for such checking will be included as source doc-uments in the reference data-base for the purpose of de-tecting plagiarism of papers subsequently submitted to the system. Use of the service is subject to the licensing agree-ment, currently between The University of Western Ontario and Turnitin.com (http://www.turnitin.com).

What’s Gradescope?

This course will use Grad-escope, an online collabo-rative grading and analytic platform. For information on their privacy policy, please visit their website.

Respect one another. The Department of Statistical and Actuarial Sciences has adopted a "Mutual Expectations" policy governing the classroom environment and all work submitted by students. The full text of the policy can be found at: http://www.uwo.ca/stats/undergraduate/mutual-expectations.html. In summary, the policy was developed under the premise that all interactions between students and faculty should be governed by the principles of courtesy, re-spect and honesty.

Page 8

POLICIES AND SUPPORTS, continued

Clicker Responsibility. We subscribe to and use clicker software produced by iClicker (https://www.iclicker.com/) because it is the platform currently supported by Western’s Technology Services (WTS) and is free to registered students. If using a clicker, you are responsible for bringing your own device for use as a clicker and setting up your iClicker account correctly. The instructor is not responsible (i.e. no academic relief/consideration will be made) for WiFi failure/inconsistencies; the grading scheme is set up to account for these issues.

Clicker Academic Record. Your clicker use will be recorded in lecture and will become part of your academic rec-ord. As such, your clicker record will be afforded the same degree of security, confidentiality, and transparency that is customary for test marks, etc.

Research. Your clicker data will not be used for any non-academic or research purpose without your consent. For any research study in which you are invited to participate, you will be provided with a Letter of Information with an opportunity to give or withhold consent. Such research will not replace the usual end of term Student Ques-tionnaire given by the University.

Academic Integrity. Use of a clicker associated with an identity other than your own is an academic offense. Granting permission for someone else to submit answers on your behalf is an academic offence. In a test, lab, lec-ture, or tutorial, possession of a device associated with the identity of another student for the purpose of clicker participation, will be interpreted as intent to commit an academic offense and will be reported as such.

Need an alternate format? Please contact the course instructor if you require mate-rial in an alternate format or if you require any other ar-rangements to make this course more accessible to you. You may also wish to contact Services for Students with Disabilities (SSD) at 661-2111 ext. 82147 for any specific question regarding accommodation. Help with learning strategies?

Learning-skills counsellors at the Student Development Cen-tre (http://www.sdc.uwo.ca) are ready to help you improve your learning skills. They offer presentations on strategies for improving time management, multiple-choice exam prepara-tion/writing, textbook reading, and more. Individual support is offered throughout the Fall/Winter terms in the drop-in Learning Help Centre, and year-round through individual counselling.

Need support with disabilities?

The policy on Accommodation for Students with Disabilities can be found here: www.uwo.ca/univsec/pdf/academic_policies/appeals/accommodation_disabilities.pdf

Have a religious conflict?

The policy on Accommodation for Religious Holidays can be found here: http://www.uwo.ca/univsec/pdf/academic_policies/appeals/accommodation_religious.pdf

In mental/emotional distress?

Students who are in emotional/mental distress should refer to Mental Health@Western (http://www.uwo.ca/uwocom/mentalhealth/) for a complete list of options about how to obtain help.

Additional student-run support services are offered by the USC, http://westernusc.ca/services.

Senate definitions of grades For your reference, the Senate definition for meaning of letter grades is:

Letter Grade Grade range (%) Definition

A+ 90 – 100 One could scarcely expect better from a student at this level.

A 80 – 89 Superior work which is clearly above average.

B 70 – 79 Good work, meeting all requirements, and eminently satisfactory.

C 60 – 69 Competent work, meeting requirements.

D 50 – 59 Fair work, minimally acceptable.

F Below 50 Fail.

Page 9

CO

UR

SE O

UTL

INE

This

sch

edu

le is

ten

tati

ve; w

e o

ccas

ion

ally

get

a li

ttle

ah

ead

/beh

ind

on

co

urs

e to

pic

s. C

on

seq

uen

tly,

th

e ti

min

g o

f A

ctivi

ties

an

d Q

uiz

zes

will

be

adju

ste

d t

o

mat

ch o

ur

pro

gres

sio

n t

hro

ugh

th

e co

urs

e (a

nd

occ

asio

nal

ly, q

uiz

zes

and

/or

acti

viti

es a

re a

dd

ed/r

emo

ved

, an

d t

op

ics

cove

red

ad

just

ed

). Im

po

rtan

tly,

th

e w

eek

s fo

r la

bs

and

th

e A

ssig

nm

ent

du

e d

ates

may

get

bu

mp

ed (

to b

e la

ter.

..nev

er e

arlie

r) d

epen

din

g o

n o

ur

pro

gres

sio

n.

A w

eekl

y e

mai

l to

stu

den

ts (

sen

t o

n

Frid

ays

typ

ical

ly)

will

pro

vid

e d

etai

ls o

n u

pco

min

g d

ead

line

s, e

tc. t

o c

lari

fy/a

nn

ou

nce

an

y ad

just

me

nts

to

th

e co

urs

e sc

hed

ule

.

Lect

ure

To

pic

(s)

Gra

de

d c

om

po

ne

nts

R

eq

uir

ed

‘Re

adin

g’

Jan

7 &

8

PP

DA

C: a

sci

enti

fic

inq

uir

y fr

ame

wo

rk (

“Pro

ble

m”)

Co

urs

e sy

llab

us

and

exp

lore

OW

L co

urs

e si

te

Jan

9 &

10

Sa

mp

ling

de

sign

co

nsi

de

rati

on

s (“

Pla

n”)

- p

art

A

Ind

epen

den

t St

ud

y: S

amp

ling

Des

ign

s (“

Pla

n”)

A

ctivi

ty: T

akin

g a

sam

ple

C

h 6

, p1

46

–1

55

plu

s v

ideo

s o

n O

WL

Lab

s N

on

e...

do

n’t

str

ess.

We’

ll g

et y

ou

rea

dy

in w

eek

2.

Jan

14

& 1

5

Sam

plin

g d

esi

gn c

on

sid

era

tio

ns

(“P

lan

”) -

par

t B

In

dep

end

ent

Stu

dy:

Stu

dy

Des

ign

s (“

Pla

n”)

Q

uiz

1—

Sam

plin

g C

h 6

, p1

41

–1

44

, p. 1

55

-15

9

Ch

7, p

16

8–

17

8 p

lus

vid

eos

on

OW

L

Jan

16

& 1

7

Stu

dy

des

ign

co

nsi

der

atio

ns

(“P

lan

”) -

par

t A

Q

uiz

2—

Co

urs

e St

ruct

ure

du

e F

rid

ay

Acti

vity

: Pra

ctice

lab

ass

ign

me

nt

du

e F

rid

ay

Lab

s In

tro

du

ctio

n t

o la

b c

om

po

nen

t o

f 2

24

4 a

nd

R T

uto

rial

(im

po

rtin

g d

ata

& u

sin

g R

scr

ipt

file

s)

Jan

21

& 2

2

Stu

dy

des

ign

co

nsi

der

atio

ns

(“P

lan

”) -

par

t B

In

dep

end

ent

Stu

dy:

Gra

ph

ical

& n

um

eric

al s

um

mar

ies

Qu

iz 3

—St

ud

y d

esig

n

Ch

1, p

4–2

8 (

exce

pt

“Tim

e Se

ries

”) a

nd

Ch

2 (

all s

ec-

tio

ns)

Jan

23

& 2

4

Pla

nn

ing

ahea

d: S

amp

ling

vari

abili

ty (

“Pla

n”)

Q

uiz

4—

On

line

R M

od

ule

1 d

ue

Fri

day

Lab

s n

on

e

Jan

28

& 2

9

Sum

mar

izin

g &

Exp

lori

ng

Dat

a (“

Dat

a”)

- p

art

A

Qu

iz 5

—Su

mm

ariz

ing

& E

xplo

rin

g D

ata

Jan

30

& 3

1

Sum

mar

izin

g &

Exp

lori

ng

Dat

a (“

Dat

a”)

- p

art

B

Lab

s A

ssig

nm

ent

1 w

ork

/hel

p p

erio

d a

nd

R T

uto

rial

(su

b-s

etti

ng

and

rep

orti

ng

cod

e)

Feb

4 &

5

Sum

mar

izin

g &

Exp

lori

ng

Dat

a (“

Dat

a”)

- p

art

C

Pla

nn

ing

a St

ud

y (A

ssig

nm

en

t 1

) d

ue

Mo

nd

ay

Feb

6 &

7

Pro

bab

ility

Mo

del

s &

Vo

cab

ula

ry (

fou

nd

atio

ns)

Q

uiz

6 –

On

line

R M

od

ule

2 d

ue

Fri

day

C

h 9

, p2

22

–2

24

N

ote

: Ch

10

, p2

58

–26

4 is

no

t re

qu

ired

, bu

t sh

ou

ld b

e fo

r st

ud

ents

inte

rest

ed in

hea

lth

-rel

ated

fiel

ds

Lab

s A

ssig

nm

ent

2 w

ork

/hel

p p

erio

d

Feb

11

& 1

2

Pro

bab

ility

mo

del

s: N

orm

al (

fou

nd

atio

ns)

Q

uiz

7—

No

rmal

dis

trib

uti

on

s C

h 1

1, p

27

1—

27

8, 2

88

—2

92

Feb

13

& 1

4

Pro

bab

ility

mo

del

s: B

ino

mia

l (fo

un

dati

on

s)

Qu

iz 8

—B

ino

mia

l dis

trib

uti

on

Su

mm

ariz

ing

& E

xplo

rin

g D

ata

(Ass

ign

me

nt

2)

du

e F

rid

ay

Ch

12

, p2

99

–30

1

Lab

s n

on

e

Page 10

CO

UR

SE O

UTL

INE,

co

nti

nu

ed.

Lect

ure

To

pic

(s)

Gra

de

d c

om

po

ne

nts

R

eq

uir

ed

‘Re

adin

g’

Feb

17

—2

1

Rea

din

g w

eek

Feb

25

& 2

6

Sam

plin

g d

istr

ibu

tio

ns

(fo

un

dati

on

s)

Acti

vity

: Sam

plin

g d

istr

ibu

tio

ns

of

sam

ple

mea

ns

Qu

iz 9

—Sa

mp

ling

dis

trib

uti

on

s C

h 1

3, p

32

4–3

29

Feb

27

& 2

8

Un

der

stan

din

g co

nfi

den

ce in

terv

als

(fo

un

dati

on

s)

Acti

vity

: Un

der

stan

din

g co

nfi

den

ce

Qu

iz 1

0—

Intr

od

ucti

on

to

co

nfi

den

ce in

terv

als

Ch

14

, p3

47

–35

2

Lab

s

n

on

e

Sun

, Ma

r 1

Te

st 1

: 2

:00

–3:3

0 p

m;

see

OW

L G

rad

ebo

ok

for

you

r a

ssig

ned

ro

om

Mar

3 &

4

t co

nfi

den

ce in

terv

al f

or

the

mea

n (

“An

alys

is”)

Q

uiz

11

—t

con

fid

ence

inte

rval

s C

h 1

5, p

37

6–3

79

& C

h 1

7, p

41

7–

42

0

Mar

5 &

6

Larg

e sa

mp

le c

on

fid

ence

inte

rval

fo

r th

e p

rop

orti

on

(“

An

alys

is”)

Q

uiz

12

—la

rge

sam

ple

co

nfi

den

ce in

terv

als

Ch

12

, p3

08

–30

9; C

h 1

3, p

33

6–

33

9; &

Ch

19

, p

47

3–4

75

Lab

s n

on

e

Mar

10

& 1

1

Un

der

stan

din

g P

-val

ue

s (f

ou

nd

atio

ns)

Q

uiz

13

—In

tro

du

ctio

n t

o h

ypo

thes

is t

ests

C

h 1

4, p

35

6–3

58

Mar

12

& 1

3

Larg

e sa

mp

le t

est

fo

r th

e p

rop

orti

on

(“A

nal

ysis

”)

Lab

s n

on

e

Fri,

Ma

r 1

3

Test

2:

7:0

0–9

:00

pm

; se

e O

WL

Gra

deb

oo

k fo

r yo

ur

ass

ign

ed r

oo

m

Mar

17

& 1

8

t-te

st f

or

the

mea

n (

“An

alys

is”)

Q

uiz

14

—O

nlin

e R

mo

du

les

3 &

4

Mar

19

& 2

0

t p

roce

du

res

for

diff

eren

ce b

etw

een

mea

ns

(“A

nal

ysis

”)

Ind

epen

den

t St

ud

y: L

arge

sam

ple

pro

ced

ure

s fo

r d

iffer

-en

ce b

etw

een

pro

po

rtio

ns

(“A

nal

ysis

”)

Qu

iz 1

5—

t p

roce

du

res

for

diff

eren

ce b

etw

een

m

ean

s Q

uiz

16

—O

nlin

e R

mo

du

les

5 &

6

Ch

18

, p4

45

–44

9

Ch

20

, p4

95

–50

0, p

50

4–

50

7

Lab

s A

ssig

nm

ent

3 w

ork

/hel

p p

erio

d

Mar

24

& 2

5

Un

cert

ain

ties

in h

ypo

thes

is t

esti

ng

(“A

nal

ysis

”)

Ind

epen

den

t St

ud

y: C

orr

elati

on

(“A

nal

ysis

”)

An

alys

ing

Dat

a an

d W

riti

ng

Co

ncl

usi

on

s (A

ssig

nm

en

t 3

) d

ue

Mo

nd

ay

Ch

15

: p3

83

–3

95

C

h 3

(al

l se

ctio

ns)

Mar

26

&2

7

Lin

ear

Reg

ress

ion

(“A

nal

ysis

”) -

par

t A

Q

uiz

17

—R

egre

ssio

n

Ch

4, p

93

–1

01

, p1

07

–11

5

Lab

s A

ssig

nm

ent

4 w

ork

/hel

p p

erio

d

Mar

31

& A

pr

1

Lin

ear

Reg

ress

ion

(“A

nal

ysis

”) -

par

t B

Q

uiz

18

—O

ne-

fact

or

AN

OV

A

Ap

r 2

& 3

O

ne-

fact

or

AN

OV

A &

fo

llow

-up

(“A

nal

ysis

”)

Qu

iz 1

9—

On

line

R M

od

ule

s 7

& 8

A

nal

ysin

g D

ata

and

Wri

tin

g C

on

clu

sio

ns,

par

t 2

(A

ssig

nm

en

t 4

) d

ue

Fri

day

A

ctivi

ty: R

eflec

tio

n

Ch

24

, p6

05

–61

1

Ap

ril 6

—3

0

Fin

al E

xam

Per

iod

(d

o n

ot

bo

ok

tra

vel d

uri

ng

th

is ti

me

un

til e

xam

sch

edu

le is

fin

aliz

ed o

n F

ebru

ary

7, 2

02

0)