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Lectures: Mon/Wed 6:30–8:30 pm NCB 114 Labs: Thurs 6:30–9:30 pm Section 002: HSB 13 Section 003: HSB 14

INSTRUCTOR DETAILS

Name: Jennifer Peter

Departments: Biology and Statistical & Actuarial Sciences

Drop-in hours: Tues 2:00–4:00 pm, NCB 301L ‘Drop-in hours’ are open and collaborative around a large table ,with a smart-

board on which we can create. No appointments needed...just ‘drop-in’!

Contact: Please use the “Messages” tool on our OWL course site instead of email; send mes-sages to Jennifer Peter.

Note: what medium to use for communication depends on what you require; please consult this Venn diagram to identify the relevant method of communi-cation.

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 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.

Summer Evening 2019

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

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 these three main resources, we will occasionally use articles, videos, and ap-plets available freely online to supplement your learning. If you discover any (open access) resources that are help-ful to you for this course, I encourage you to share the details with the rest of the class!

OWL Course Site: Biology 2244B 002 SU19 Login with UWO ID and password at 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 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 rec-ommended integrated develop-ment environment, R Studio available at www.rstudio.com)—to visualize and analyse data. Both software packages are free programs that can be download-ed to your personal computers and are available on campus GenLab computers.

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)

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Component Scheme 1 Scheme 2

Clicker Participation 5% 0%

Quizzes 6% 6%

Activities 3% 3%

5 Lab Assignments Highest lab

2nd & 3rd highest labs 4th & 5th highest labs

20% total 6%

4% each 3% each

25% total 7%

5% each 4% each

Fixed Distribution (35%)

Each student’s final grade will be calculated using both Scheme 1 and Scheme 2. Their final course grade will use whichever schemes results in the highest grade for that student.

Item S1 S5 S2 S4 S3

ICA 1 3% 0% 0% 0% 3%

ICA 2 3% 0% 4.5% 0% 0%

Test 15% 0% 16.5% 16.5% 15%

Final Exam 45% 45% 48% 49.5% 66%

Flexible Distribution (65%)

Each of the following course items are initially weight according to Scenario 1 (“S1”). However, each student’s grade will be calculated according to all five scenarios; whichever scenario results in the highest grade for that student will be used when calculating the final course grade.

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 component/item, see page 5.

Why is this so complicated!?

The Flexible Distribution and success-based weighting of Lab Assignments is set up so you with multiple opportuni-ties to receive feedback on your learning. If you discover your understanding is not complete, and/or you perform below your desired level of success on a particular component of the flexible distribution, you still have future op-portunities to regain some or all marks associated with those components. Because all assessments are cumula-tive, 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:

Activities

Collect data

Explore applet

Answer survey

Preparatory Work

Read text Watch video

Online R Modules

Class (Lecture/Lab)

Clickers Discussion

Q&A

Explore data

Quiz

Try questions

Follow-up tasks

Read text

Practice questions

Assignment

Study/Review

Page 4

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Page 5

ASSESSMENT DESCRIPTIONS

CLICKER PARTICIPATION

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

WHAT? Multiple choice questions during class.

HOW? iClicker app (free); account set-up information under iClicker 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? It’s built into the grading scheme (i.e. the 20% margins) and should cover occasional lapses in participation and/or technical problems. If you have circumstanc-es that cause larger gaps in participation, please speak with 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. Accounta-bility for class preparation.

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

HOW? Quizzes tool on OWL site. Available for ~24 h before deadline; limited time for completion once started.

GRADES. Marked for correct answers. The percentage of quiz questions answered correctly across the entire term places you in one of 7 categories (see table) to determine your final grade.

ACCOMMODATION? It’s built into the grading scheme (i.e. the ~16% mar-gins) and should cover occa-sional missed quizzes and/or technical problems. For more extenuating circum-stances, please speak with Academic Counseling.

% questions correct

Grade /6%

0 0

0 < % < 16 1

16 ≤ % < 33 2

33 ≤ % < 50 3

50 ≤ % < 66 4

66 ≤ % < 83 5

83 ≤ % ≤ 100 6

ACTIVITIES

WHY? Collect authentic data for use in class. Promote active learning of core concepts. Reflect on your learn-ing.

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

HOW? Responses submitted via Quizzes tool on OWL site. Typically available for ~36 h before deadline.

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? It’s built into the grading scheme and should cover occasional missed activities and/or technical prob-lems. For more extenuating cir-cumstances, please speak with Academic Counseling.

% points earned

Grade /3%

0 0

0 < % < 33 1

16 ≤ % < 67 2

67 ≤ % ≤ 100 3

IN-CLASS ASSESSMENTS (“ICAs”)

WHY? Low-weight assessment of understanding, application, and integration of course material.

WHAT? 5 multiple choice questions each; exam conditions scheduled during lab period. Calculators allowed.

HOW? Using IFAT scratch cards that allow up to 3 attempts per question to select correct answer.

GRADES. Marked for correct answers. Each question is worth 3 points; each ’attempt’ (after the 1st) reduces points earned by 1 for each question.

ACCOMMODATION? Part of the “Flexible Distribution” in course (see page 3). Weight from missed ICA(s) is auto-matically redistributed (see page 3).

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TEST and FINAL EXAM

WHY? Assessment of understanding, application, and inte-gration of course material.

WHAT? Multiple choice questions (unless otherwise speci-fied); ~20 questions for Test, ~45 questions for Final Exam.

HOW? Using Scantron sheets that allow 1 attempt per question to select the correct answer. Calculators allowed.

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

ACCOMMODATION? The Test is part of the “Flexible Dis-tribution” in course; weight from the Test is automatically redistributed (see page 3). Students who miss the Final Exam must speak with Academic Counseling to request academic accommodation. See information on Academic Policies under the Policies and Supports section (page 7).

DID YOU KNOW?

LAB ASSIGNMENTS

WHY? Assessment of your application of course concepts in a authentic manner, including use of statistical software (R). WHAT? 5 assignments, each composed of ~4 multiple part, short-answer questions requiring written responses (including graphs and R code). Assignments move progressively through the phases of the PPDAC framework (MacKay & Oldford 2000). HOW? To earn credit, assignments are submitted as a PDF file to both (1) “Assignments” on the OWL site and (2) Grad-escope. GRADES. Marked for correct/valid application of course con-cepts. Clarification on grading must be requested within one week of receiving assignment feedback.

Lab Assignments are an essential course component. That means—to pass Biology/Statistics 2244—you must:

• submit and receive a non-zero grade for at least 3 of the 5 Assignments, and,

• Earn a passing grade for the lab assign-ments component of the course.

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

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

ACCOMMODATIONS? For any missed Lab Assignment, you need to speak with Academic Counseling to request accommodation, otherwise a grade of zero (0) will be awarded. Up to two (2) missed and accommodation assign-ments will be assigned a mark equal to the mean of the other non-accommodated assignments, and will take on the lowest of the possible assignment weightings (i.e. Scheme 1: 3% or Scheme 2: 4%; see page 3). If more than two (2) lab assignments are missed with accommodation, a final course grade of Incomplete (“INC”) will be sub-mitted, and the student will be required to make up the assignments in the next term of the course.

LAB EXEMPTION FOR REPEATING STUDENTS. Students who previously took Biology/Statistics 2244 after Sept 2014 may be eligible for a lab assignment exemption. Eligible students have the option of carrying their previous lab assignment grades to this term. This is an “all or nothing” exemption. Eligible stu-dents must indicate their acceptance of the exemption before the first lab assignment, otherwise, they will be marked on the assignments for this term. More details on the OWL site.

COMMENTS ON 2244 GRADING

The assessment weightings have been set to: • recognize the workload of each component; • highlight the relative importance to the learning

outcomes; • acknowledge that mastery takes time.

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

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 provide valid medical or supporting documentation to the Academic Counselling Office of your home faculty as soon as possible. If you are a Science student, the Academic Counselling Office of the Faculty of Science is located in NCB 240, and can be contacted at scibmsac@uwo.ca.

For further information, please consult the uni-versity’s medical illness policy at http://www.uwo.ca/univsec/pdf/academic_policies/appeals/accommodation_medical.pdf

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 company supported by Western’s Technology Services (WTS) and is free to registered students. A student choosing to use a clicker will be responsible for (a) bringing their own device to use as a clicker, and (b) setting up their iClicker account correctly. Note that the course and instructor is not responsible (and therefore, no accommodation will be made) for WiFi failure.

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 in your absence is an academic offence. In a test, lab, lecture, or tutorial, possession of more than one clicker device, or one associated with the identity of another student, 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 material in an alternate format or if you require any other arrangements to make this course more acces-sible to you. You may also wish to contact Services for Students with Disabilities (SSD) at 661-2111 ext. 82147 for any specific question regarding accommo-dation.

Help with learning strategies? Learning-skills counsellors at the Student Development Centre (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 preparation/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 Disa-bilities 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.

Page 9

CO

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a “

bac

kbo

ne”

bas

ed o

n t

he

Pro

ble

m/P

lan

/Dat

a/A

nal

ysis

/Co

ncl

usi

on

(“P

PD

AC

”) f

ram

ewo

rk f

or

scie

nti

fic

inq

uir

y (M

acK

ay a

nd

Old

ford

200

0). Y

ou

will

be

resp

on

sib

le f

or

cove

rin

g ce

rtai

n t

op

ics

on

yo

ur

ow

n ti

me

(th

rou

gh t

extb

oo

k re

adin

gs a

nd

/or

po

ste

d v

ideo

res

ou

rces

);

thes

e “I

nd

epen

den

t St

ud

y” t

op

ics

hav

e b

een

str

ateg

ical

ly c

ho

sen

bas

ed o

n p

revi

ou

s st

ud

ent

feed

bac

k an

d le

vel o

f co

mp

lexi

ty.

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

tie

s 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/

acti

viti

es a

re a

dd

ed/r

emo

ved

). G

ener

ally

, I a

nti

cip

ate

the

Ass

ign

men

t d

ued

ates

to

rem

ain

as

sch

edu

led

. Th

e In

-cla

ss A

sses

smen

t an

d T

est

dat

es c

an b

e co

nsi

der

ed fi

rm.

Dat

e

Top

ics

Gra

de

d c

om

po

ne

nts

R

eq

uir

ed

‘Re

adin

g’

Mo

n, J

un

e 1

7

PP

DA

C: a

sci

enti

fic

inq

uir

y fr

ame

wo

rk (

“Pro

ble

m”)

Sa

mp

ling

con

sid

erati

on

s (“

Pla

n”)

In

dep

end

ent

Stu

dy:

Sam

plin

g &

Stu

dy

Des

ign

s (“

Pla

n”)

Acti

vity

: Tak

ing

a sa

mp

le (

du

rin

g fi

rst

clas

s)

Ch

6, p

14

6-1

55

plu

s v

ideo

s o

n O

WL

Wed

, Ju

ne

19

Sa

mp

ling

con

sid

erati

on

s (“

Pla

n”)

, co

nt’

d.

Stu

dy

des

ign

co

nsi

der

atio

ns

(“P

lan

”)

Qu

iz 1

—Sa

mp

ling

Qu

iz 2

—St

ud

y d

esig

n

Ch

6, p

14

1-1

44

, p. 1

55

-15

9

Ch

7, p

. 16

8-1

78

plu

s vi

deo

s o

n O

WL

Thu

rs, J

un

e 2

0

Intr

od

ucti

on

to

lab

cu

rric

ulu

m

Ove

rvie

w o

f La

b A

ssig

nm

ent

1 (

“Pro

ble

m”

& “

Pla

n”)

Fl

ow

char

ts f

or

sam

plin

g &

stu

dy

des

ign

In

tro

du

ctio

n t

o R

/R S

tud

io, i

mp

orti

ng

dat

a

Acti

vity

: Pra

ctice

lab

ass

ign

me

nt

Mo

n, J

un

e 2

4

Ind

epen

den

t St

ud

y: G

rap

hic

al &

nu

mer

ical

su

mm

arie

s P

lan

nin

g ah

ead

: Sam

plin

g va

riab

ility

(“P

lan

”) (

50

min

) Su

mm

ariz

ing

& E

xplo

rin

g D

ata

(“D

ata”

)

Qu

iz 3

—Su

mm

ariz

ing

& E

xplo

rin

g D

ata

Acti

vity

: Dat

a co

llecti

on

D

ata:

Ch

1, p

. 4-2

8 (

exce

pt

“Tim

e Se

-ri

es”)

, C

h 2

(al

l se

ctio

ns)

Wed

, Ju

ne

26

Su

mm

ariz

ing

& E

xplo

rin

g D

ata

(“D

ata”

), c

on

t’d

. P

rob

abili

ty M

od

els

& V

oca

bu

lary

(fo

un

dati

on

s)

Qu

iz 4

—O

nlin

e R

Mo

du

le 1

A

ssig

nm

en

t 1

du

e F

rid

ay, J

un

e 2

8

Ch

9, p

. 22

2-2

24

N

ote

: Ch

10

, p. 2

58

-26

4 is

no

t re

-q

uir

ed ,

bu

t sh

ou

ld b

e fo

r st

ud

ents

in

tere

sted

in h

ealt

h r

elat

ed fi

eld

s

Thu

rs, J

un

e 2

7

Scie

nce

Co

mm

un

icati

on

: G

rap

hs

(“D

ata”

) O

verv

iew

of

Ass

ign

men

t 2

(“D

ata”

) R

: su

bse

ts, s

crip

ts, a

nd

rep

orti

ng

cod

e

In-c

lass

ass

ess

me

nt

1 (

6:3

0 p

m in

lab

ro

om

)

Mo

n, J

uly

1

(CA

NA

DA

DA

Y)

Pro

bab

ility

mo

del

s: B

ino

mia

l (fo

un

dati

on

s) (

Vid

eo L

ectu

re)

Pro

bab

ility

mo

del

s: N

orm

al (

fou

nd

atio

ns)

(V

ideo

Le

ctu

re)

Qu

iz 5

—B

ino

mia

l dis

trib

uti

on

s Q

uiz

6—

No

rmal

dis

trib

uti

on

s A

ctivi

ty: S

amp

ling

dis

trib

uti

on

s o

f sa

mp

le m

ean

s

Ch

12

, p. 2

99

-30

1

Ch

11

, p. 2

70

-27

8, p

. 28

8-2

92

Wed

, Ju

ly 3

Sa

mp

ling

dis

trib

uti

on

s (f

ou

nd

atio

ns)

U

nd

erst

and

ing

con

fid

ence

inte

rval

s (f

ou

nd

atio

ns)

Q

uiz

7—

Sam

plin

g d

istr

ibu

tio

ns

Qu

iz 8

– O

nlin

e R

Mo

du

le 2

A

ctivi

ty: U

nd

erst

and

ing

con

fid

ence

Ch

13

, p. 3

24

-32

9

Ch

14

, p. 3

45

-35

2

Thu

rs, J

uly

4

Ass

ign

men

t 2

hel

p/w

ork

per

iod

A

ssig

nm

en

t 2

du

e F

rid

ay, J

uly

5

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

.

Page 10

CO

UR

SE O

UTL

INE,

co

nti

nu

ed.

Dat

e

Top

ics

Gra

de

d c

om

po

ne

nts

R

eq

uir

ed

‘Re

adin

g’

Mo

n, J

uly

8

t co

nfi

den

ce in

terv

als

for

a m

ean

(“A

nal

ysis

”)

Larg

e sa

mp

le c

on

fid

ence

inte

rval

s fo

r a

pro

po

rtio

n (

“An

alys

is”)

Q

uiz

9—

t co

nfi

den

ce in

terv

als

Qu

iz 1

0—

Larg

e sa

mp

le c

on

fid

ence

inte

rval

s C

h 1

5, p

. 37

6-3

79

C

h 1

7, p

. 41

7-4

20

C

h 1

3, p

. 33

6-3

39

C

h 1

9, p

. 47

3-4

75

Wed

, Ju

ly 1

0

Un

der

stan

din

g P

-val

ue

s (f

ou

nd

atio

ns)

La

rge

sam

ple

te

st f

or

a p

rop

orti

on

(“A

nal

ysis

”)

Qu

iz 1

1—

Intr

od

ucti

on

to

hyp

oth

esis

tes

tin

g

Qu

iz 1

2—

On

line

R M

od

ule

s 3

& 4

C

h 1

4, p

. 35

6-3

58

Thu

rs, J

uly

11

Sc

ien

ce C

om

mu

nic

atio

n: r

epo

rtin

g co

nfi

den

ce in

terv

als

(“C

on

clu

sio

n”)

A

ssig

nm

ent

3 (

“An

alys

is”

& “

Co

ncl

usi

on

”) w

ork

/hel

p p

erio

d

Ass

ign

me

nt

3 d

ue

Fri

day

, Ju

ly 1

2

Sat,

Ju

ly 1

3

TEST

(1

5%

) -

sch

ed

ule

d f

or

10:

00

am

Mo

n, J

uly

15

t-

test

fo

r a

mea

n (

“An

alys

is”)

(P

ote

nti

al)

Ind

epen

den

t St

ud

y: U

nce

rtai

nti

es in

hyp

oth

esis

te

stin

g (p

ow

er, e

rro

rs, e

ffe

ct s

ize)

(“A

nal

ysis

”)

C

h 1

5: p

. 38

3-3

95

Wed

, Ju

ly 1

7

t-te

st f

or

diff

ere

nce

bet

wee

n m

ean

s (“

An

alys

is”)

In

dep

end

ent

Stu

dy:

Lar

ge s

amp

le t

est

for

diff

eren

ce in

pro

po

r-ti

on

s (“

An

alys

is”)

Qu

iz 1

3—

two

sam

ple

tes

t fo

r d

iff in

mea

ns

Qu

iz 1

4—

On

line

R M

od

ule

s 5

&6

C

h 1

8, p

. 44

5-4

50

C

h 2

0, p

. 49

5-5

00

, p. 5

04

-50

7

Thu

rs, J

uly

18

Sc

ien

ce C

om

mu

nic

atio

n: r

epo

rtin

g h

ypo

thes

is t

est

s (“

Co

ncl

usi

on

”)

Ass

ign

men

t 4

(“A

nal

ysis

” &

“C

on

clu

sio

n”)

wo

rk/h

elp

per

iod

In-c

lass

ass

ess

me

nt

2 (

6:3

0 p

m in

lab

ro

om

) A

ssig

nm

en

t 4

du

e F

rid

ay, J

uly

19

Mo

n, J

uly

22

In

dep

end

ent

Stu

dy:

Co

rrel

atio

n (

“An

alys

is”)

R

egre

ssio

n (

infe

ren

ce o

n s

lop

e) (

“An

alys

is”)

Q

uiz

15

—R

egre

ssio

n

Ch

3 (

all s

ecti

on

s)

Ch

4, p

. 93

-10

1, p

. 10

7-1

15

Wed

, Ju

ly 2

4

On

e-fa

cto

r A

NO

VA

an

d f

ollo

w-u

p a

nal

yses

(“A

nal

ysis

”)

Infe

ren

ce b

eyo

nd

22

44

(“A

nal

ysis

”)

Qu

iz 1

6—

AN

OV

A

Qu

iz 1

7—

On

line

R M

od

ule

s 7

& 8

C

h 2

4, p

. 60

5–

61

1

Thu

rs, J

uly

25

Sc

ien

ce C

om

mu

nic

atio

n: d

iscu

ssin

g re

sult

s (“

Co

ncl

usi

on

”)

Ass

ign

men

t 5

(“A

nal

ysis

” &

“C

on

clu

sio

n”)

wo

rk/h

elp

per

iod

A

ssig

nm

en

t 5

du

e F

rid

ay, J

uly

26

A

ctivi

ty: R

eflec

tio

n

July

29

-30

Fi

nal

Exa

m (

45

%)

du

rin

g e

xam

pe

rio

d (

dat

e/ti

me

se

t b

y R

egi

stra

r)