Welcome to Statistical ModelsDr. D’Agostino McGowan2019-08-271 / 46

👋

Lucy D'Agostino McGowan

mcgowald@wfu.edu
Manchester 342
Wed 10:00-11:00a, Thu 10:00-11:00a

2 / 46

Everything you will need will be posted at:

bit.ly/sta-212-f19

3 / 46

"truth … is much too complicated to allow anything but approximations" - John von Neumann

John von Neumann, wearer of funny hats via farkasdilemma.wordpress.com

4 / 46

"Statistics is using data and knowledge about randomness to condense, communicate, and contextualize information and provide insight into the setting from which the data came." - Jo Hardin

Jo Hardin https://research.pomona.edu/johardin/

5 / 46

"All models are wrong, but some are useful" - George Box

DavidMCEddy at en.wikipedia CC BY-SA 3.0 , via Wikimedia Commons

6 / 46

  
  Dr. Lucy D'Agostino McGowan

data = model + error
7 / 46

$y = f (X) + ϵ$

🗣 math speak

8 / 46

$y = f (X) + ϵ$

model

9 / 46

$y = f (X) + ϵ$

data (to build the model)

10 / 46

$y = f (X) + ϵ$

data (outcome)

11 / 46

$y = f (X) + ϵ$

1. Thinking about, visualizing, and wrangling data

12 / 46

$y = β_{0} + β_{1} X + ϵ$

2. Simple Linear Regression

13 / 46

Bob at Easel from Wikipedia

14 / 46

Data: FiveThirtyEight

15 / 46

16 / 46

$y = β_{0} + β_{1} X + ϵ$

17 / 46

$# of paintings with clouds = β_{0} + β_{1} s e a s o n + ϵ$

18 / 46

$# of paintings with clouds = β_{0} + β_{1} s e a s o n + ϵ$

19 / 46

$# of paintings with clouds = β_{0} + β_{1} f (s e a s o n) + ϵ$

20 / 46

$y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + . . . + ϵ$

3. Multiple Linear Regression

21 / 46

$f (y) = β_{0} + β_{1} X_{1} + β_{2} X_{2} + . . . + ϵ$

4. Logistic Regression

22 / 46

$l o g i t (y) = β_{0} + β_{1} X_{1} + β_{2} X_{2} + . . . + ϵ$

4. Logistic Regression

23 / 46

  
  Dr. Lucy D'Agostino McGowan

PlanThinking about, visualizing, and wrangling data
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Data Science-ing
24 / 46

  
  Dr. Lucy D'Agostino McGowan

Let's go!25 / 46

Join RStudio.cloud

Go to bit.ly/sta-212-f19-rstudio-join and sign up.

Once done, place a green sticky on your laptop. If you have questions, place a pink sticky.

26 / 46

Create your first data visualization

Once you log on to RStudio Cloud, click on this course's workspace "STA 212 - Fall 19" then click "Projects"
You should see a project called UN Votes, click it.
In the Files pane in the bottom right corner, spot the file called un-votes.Rmd. Open it, and then click on the "Knit" button. You will likely see an pop-up error, click "Try Again"
Go back to the file and change your name on top (in the yaml -- we'll talk about what this means later) and knit again.
Then, change the country names to those you're interested in. Your spelling and capitalization should match how the countries appear in the data, so take a peek at the Appendix to confirm spelling. Knit again & voila, your first data visualization!

Once done, place a green sticky on your laptop. If you have questions, place a pink sticky.

27 / 46

Join Campuswire

Go to bit.ly/sta-212-f19-discuss-join and sign up.

Once done, place a green sticky on your laptop. If you have questions, place a pink sticky.

28 / 46

Practice answering a question or posting in a channel

Click on "Class feed" on the top left. You should see a post from me titled Hello!
Practice responding to this post, up-voting it, etc.
Click on "Rooms" then #general
Practice posting here - post a fun fact about your self, respond with an emoji 👍, etc

Once done, place a green sticky on your laptop. If you have questions, place a pink sticky.

29 / 46

Let's take a tour - class website

Concepts introduced:
- How to find slides
- How to find assignments
- How to find RStudio Cloud
- How to get help
- How to find policies

30 / 46

  
  Dr. Lucy D'Agostino McGowan

Course structure and policies31 / 46

  
  Dr. Lucy D'Agostino McGowan

Class meetingsInteractive
Some lectures, lots of learn-by-doing
Bring your laptop to class every day
32 / 46

  
  Dr. Lucy D'Agostino McGowan

Diversity & Inclusiveness:Intent: Students from all diverse backgrounds and perspectives be well-served by this course, that students' learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is my intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture. Let me know ways to improve the effectiveness of the course for you personally, or for other students or student groups.
If you have a name and/or set of pronouns that differ from those that appear in your official Wake Forest records, please let me know!
33 / 46

  
  Dr. Lucy D'Agostino McGowan

Diversity & Inclusiveness:If you feel like your performance in the class is being impacted by your experiences outside of class, please don't hesitate to come and talk with me. I want to be a resource for you. If you prefer to speak with someone outside of the course, your academic dean is an excellent resource. 
I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it.
34 / 46

Disability Policy

Students with disabilities who believe that they may need accommodations in the class are encouraged to contact Learning Assistance Center & Disability Services at 336-758-5929 or lacds@wfu.edu as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

35 / 46

How to get help

All course discussion will be via Campuswire: bit.ly/sta-212-f19-help (Sign up).

See course policies for tips on posting questions.
For personal and grade related questions, use email.

36 / 46

How to get help

Math & Stats center

Located in Kirby Hall 117
Study sessions: (beginning Sept 3) Monday 7-9pm
Make an appointment: (beginning Sept 10) https://mathandstatscenter.wfu.edu/

37 / 46

Academic integrity

Adhere to the Wake Forest Honor Code. Academic dishonesty will not be tolerated.

38 / 46

  
  Dr. Lucy D'Agostino McGowan

Sharing/reusing codeThere are many online resources for sharing code (for example, StackOverflow) - you may use these resources but must explicitly cite where you have obtained code (both code you used directly and "paraphrased" code / code used as inspiration). Any reused code that is not explicitly cited will be treated as plagiarism.
You may discuss the content of assignments with others in this class. If you do so, please acknowledge your collaborator(s) at the top of your assignment, for example: "Collaborators: Gertrude Cox, Florence Nightingale David". Failure to acknowledge collaborators will result in a grade of 0. You may not copy code and/or answers directly from another student. If you copy someone else's work, both parties will receive a grade of 0.
Rather than copying someone else's work, ask for help. You are not alone in this course!
39 / 46

  
  Dr. Lucy D'Agostino McGowan

Course components:Application exercises: Usually start in class and finish in teams by the next class period, check/no check
In class quizzes: unannounced, ~10 minutes, no make-ups, lowest two will be dropped
Homework: lowest score dropped
Lab: start in class, lowest score dropped
Exams: 2 in class midterms
Final project: Presentations during the last week of class, you must participate in the project and be in class to present to pass this class
Self paced tutorials: Individual, check/no check, extra credit
40 / 46

  
  Dr. Lucy D'Agostino McGowan

Grading

Component
Weight


Participation & application exercises
5%

Quizzes
10%

Homework
15%

Labs
10%

Midterm exam 1
20%

Midterm exam 2
20%

Final project
20%

Class attendance is a firm expectation; frequent absences or tardiness 
will be considered a legitimate cause for grade reduction.
41 / 46

Component	Weight
Participation & application exercises	5%
Quizzes	10%
Homework	15%
Labs	10%
Midterm exam 1	20%
Midterm exam 2	20%
Final project	20%

  
  Dr. Lucy D'Agostino McGowan

Late/missed work policyLate work policy for homework assignments:late, but within 24 hours of due date/time: -50%
any later: no credit

Late work will not be accepted the final project.
You must complete the final project and be in class to present it in order to pass this course.
42 / 46

  
  Dr. Lucy D'Agostino McGowan

Other policiesPlease refrain from texting or using your computer for anything other than coursework during class.
You must be in class on a day when you're scheduled to present, there are no make ups for presentations.
Regrade requests must be made within 1 week of when the assignment is returned.
43 / 46

a quick survey

bit.ly/sta-212-f19-form

44 / 46

  
  Dr. Lucy D'Agostino McGowan

Introsname
major / intended major
what you hope to get out of this class OR fun fact
45 / 46

RStudio Cloud

If you had issues creating your RStudio Cloud account, opening the project, or running the analysis, stick around to try it again.
- Go to bit.ly/sta-212-f19-rstudio-join and sign up.
If RStudio Cloud worked for you and you were able to run the analysis, you're free to leave.

↑, ←, Pg Up, k	Go to previous slide
↓, →, Pg Dn, Space, j	Go to next slide
Home	Go to first slide
End	Go to last slide
Number + Return	Go to specific slide
b / m / f	Toggle blackout / mirrored / fullscreen mode
c	Clone slideshow
p	Toggle presenter mode
t	Restart the presentation timer
?, h	Toggle this help

Welcome to Statistical Models

Dr. D’Agostino McGowan

2019-08-27

👋

Lucy D'Agostino McGowan

bit.ly/sta-212-f19

"truth … is much too complicated to allow anything but approximations" - John von Neumann

"Statistics is using data and knowledge about randomness to condense, communicate, and contextualize information and provide insight into the setting from which the data came." - Jo Hardin

"All models are wrong, but some are useful" - George Box

data = model + error

🗣 math speak

model

data (to build the model)

data (outcome)

1. Thinking about, visualizing, and wrangling data

2. Simple Linear Regression

Data: FiveThirtyEight

3. Multiple Linear Regression

4. Logistic Regression

4. Logistic Regression

Plan

Let's go!

Join RStudio.cloud

Create your first data visualization

Join Campuswire

Practice answering a question or posting in a channel

Let's take a tour - class website

Course structure and policies

Class meetings

Diversity & Inclusiveness:

Diversity & Inclusiveness:

Disability Policy

How to get help

How to get help

Math & Stats center

Academic integrity

Sharing/reusing code

Course components:

Grading

Late/missed work policy

Other policies

bit.ly/sta-212-f19-form

Intros

RStudio Cloud

👋

Lucy D'Agostino McGowan

Help