Spring 2020 Math 58 & 58B HW Schedule
Introduction to Statistics / Biostatistics
Math 58 & 58B, Spring 2020
Jo Hardin
2351 Millikan
jo.hardin@pomona.edu
Office Hours: Mon 911am, Wed 1:304pm, or by appointment
Mentors: Sophia Sun & Ethan Ong
See Sakai for Zoom address:
Sunday (Sophia & Ethan) 89pm; Monday (Ethan) 89pm; Wednesday (Sophia) 56pm; Thursday (Sophia & Ethan) 810pm
LiCMath Mentor: Ethan Ashby
Ask Ethan Ashby for Zoom address
Important Websites:
 Main course page
 Schedule / Homework Assignments
 Course Notes
 Main text: ISRS
 Applets: ISCAM
 Supplementary text / examples: ISCAM
 infer help
 rstudio.campus.pomona.edu (Pomona WiFi); rstudio.pomona.edu (nonPomona WiFi)
Exam 1 (Thursday, March 12)
Exam 2 (takehome, last week of classes)
Final Data Analysis Project:
 Math 58 — due Monday, 5/11/20, 2pm
 Math 58B — due Thursday, 5/14/20, 2pm
 Seniors are now in line with all students (grades are due on the same day)
Text: I will follow Introductory Statistics with Randomization and Simulation by Diez et al. reasonably closely (https://www.openintro.org/book/isrs/). The examples are quite insightful, and if you read them closely, you will gain a deep understanding of the course material. I have posted the daily sections on the course homework page. It would do you well to read over the text before we cover the material in class.
We will start with chapter 2 (and proceed forward), and we will use chapter 1 as a reference. The material in chapter 1 will be covered throughout the semester, so be sure to go back to chapter 1 as you have questions or start studying for exams.
Many of the examples and all of the applets will be taken from Investigating Statistical Concepts, Applications, and Methods, by Chance & Rossman (if you want to see the details of the examples, you can purchase the R version of the text (.pdf) here for $5: http://rossmanchance2.myshopify.com/collections/r1).
ISCAM website with homework & applets
Handouts:
 Youtube videos on getting started with R and RStudio: Introduction to RStudio
 Some notes I wrote on R / RStudio / tidyverse / ggplot.
 R documentation / help
 Great tutorials through the Coding Club
 swirl package
 Google for R: http://www.rseek.org/
 R tutorial
 An Introduction to R, Venables & Smith
 R Language Definition, R Core Team
 Another tutorial, with exercises & solutions
 Mosaic Reference Guide, need to install the mosaic package
 A Student’s Guide to R; Horton, Pruim, Kaplan (click on “Raw” to download)
 Data Wrangling Cheatsheet: http://www.rstudio.com/resources/cheatsheets/
 Clicker Questions
 Reflection Questions
 Confidence Intervals and Hypothesis Testing Cheat Sheet
 Advice for Exam 1
 Advice for Exam 2
 Advice for the Final
 When to use what
Homework:
 Homework will be assigned from the text and due every Thursday.
 One homework grade will be dropped.
 HW is graded on a scale of 5/4/3/2/1. See the first HW assignment for more information. One point will be deducted if superfluous information is printed or if assignment is excessively long.
 All R HW problems should be turned in as pdf documents compiled from an R Markdown file. Remember: knit early and often!
Date  topic  ISRS section
(base text with HW and reference material) 
ISCAM investigation
(mostly inclass examples & applets) 
Lab due:
Tuesday (5pm) 
HW due: Thursday (5pm) 
Tue 1/21  test infants  Inv 1.1  
Th 1/23  structure of hyp testing  2.1 & 2.2 & 2.3  syllabus quiz  
Lab:
Wed 1/22 Fri 1/24 
R & the tidyverse  
Tue 1/28  central limit theory  2.4 & 2.5  Inv 1.8  Lab#1 as .pdf
What is the final write up supposed to look like? Click here 

Th 1/30  normal probabilities  2.6 & 2.7  1.9  In class:  HW#1 as .pdf 
Lab:
Wed 1/29 Fri 1/31 
ggplot  
Tue 2/4 
confidence intervals  2.8  Inv 1.10  
Th 2/6 
HW#2 as .pdf  
Lab: Wed 2/5 Fri 2/7 

Tue 2/11 
sampling  1.31.4  Inv 1.12  
Th 2/13 
errors & power  3.1 & 2.3  Inv 1.7  HW#3 as .pdf  
Lab: Wed 2/12 Fri 2/14 

Tue 2/18 
58: Binomial / 58B: RR & OR  no ISRS  58: Inv 1.2 & 1.3 / 58B: Inv 3.9 & 3.10  Lab#4 as .pdf  
Th 2/20 
HW#4 as .pdf  
Lab: Wed 2/19 Fri 2/21 

Tue 2/25 
2 categorical variables  3.2  Inv 3.1  Math 58: Lab#5 as .pdf


Th 2/27 
experiments  1.4 & 1.5  Inv 3.3 & 3.4  Math58: HW#5 as .pdf


Lab: Wed 2/26 Fri 2/28 

Tu 3/3  chisquare — goodnessoffit  3.3  Inv 5.1  Lab#6 as .pdf  
Th 3/5 
chisquare —
test of independence 
HW#6 as .pdf  
Lab: Wed 3/4 Fri 3/6 
Inv 5.2  
Tue 3/10 
catchup / review  Lab#7 as .pdf  
Th 3/12 
exam1  NOT DUE (ever!)  
Lab: Wed 3/11 Fri 3/14 
4.5  Inv 2.9  
Tue 3/17 
spring break 1  
Tue 3/24 
spring break 2
census & COVID19 

Tue 3/31 
one mean  4.1  Inv 2.4  no lab due  
Th 4/2 
Inv 2.5  Take home due
(posted on Sakai) 
no HW due  
Lab: Wed 4/1 Fri 4/3 
wrangling & graphing quantitative data  
Tue 4/7 
prediction intervals  Inv 2.6  Lab#8 as .pdf  
Th 4/9 
two means  4.3  Inv 4.2 (maybe: 4.3)  due FRIDAY April 10 HW#8 as .pdf  
Lab: Wed 4/8 Fri 4/10 
R code for inference on one quantitative variable (one group & two groups)  
Tue 4/14 
two means  4.3  Inv 4.5 & 4.6  no lab due  
Th 4/16 
correlation  5.1  Inv 5.6 & 5.7  due FRIDAY April 17 HW#9 as .pdf  
Lab: Wed 4/15 Fri 4/17 
plotting & wrangling two quantitative variables  5.1  
Tue 4/21 
SLR inference  5.4  Inv 5.10 & 5.11  no lab due  
Th 4/23 
transformations  5.3  Inv 5.14  due FRIDAY April 24 HW#10 as .pdf  
Lab: Wed 4/15 Fri 4/17 
R code for inference on two quantitative variables  
Tue 4/28 
58: multiple regression / 58B: logistic regression  58: 6.1 – 6.3 / 58B: 6.4  no lab due  
Th 4/30 
due FRIDAY May 1 HW#11 as .pdf  
Lab: Wed 4/29 Fri 5/1 
no lab  
Tue 5/5 
catchup / review  
Math 58 — Mon, 5/11/20, 2pm
Math 58B — Th, 5/14/20, 2pm 
seniors are now on the same grading schedule as all students  Take Home Project based on a regression data analysis 