Math 155: Introduction to Statistical Modeling

Assignments by Chapter 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18

Chapter Problems with Answers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17

Class Schedule

	M	W	F	M	W	F	M	W	F	M	W	F	M	W	F
Sept		5	7	10	12	14	17	19	21	24	26	28
Oct	1	3	5	8	10	12	15	17	19	22	24	26	29	31
Nov			2	5	7	9	12	14	16	19	21	23	26	28	30
Dec	3	5	7	10	13	14

Final Exam : Sections 01 & 02: Friday 14 Dec; Section 03: Thurday 13 Dec.

End-of-Semester Review

Chapter 15 Exercises: 15.RQ, 15.01, 15.02, 15.04, 15.05, 15.10, 15.11, 15.12, 15.21, 15.22

Review Sessions with faculty

Wed 28 Nov: 3:30-4:30
Thurs 29 Nov: 7:30-8:30
Wed 5 Dec: 3:30-4:30
Thurs 6 Dec: 3:30-4:30
Thurs 6 Dec: 7:30-8:30
Mon 10 Dec: 2:30-3:30 (location to be announced)
Wed 12 Dec: 3:30-4:30

Review Problems

These are typically problems from previous exams or that were written for review. We give no promise that the exam will be just like these, but they are a pretty good guide to the style of many exam problems.

Two versions of the same problems: without answers and with answers.

Mon 10 Dec

Topic: Experiment and Design

Reading: SM Ch. 18

Assignment for today: (deadline: end of exam week)

Car Prices Revisited. Back in September, you made an analysis of used-car prices. Here's a link to the original report site on Moodle.. You, individually, not as a group, should review that report and re-evaluate your claims using the tools you have learned since then: confidence intervals, hypothesis testing, and so on. This should not be a lot of work. You don't need to discover new things about car prices, just review your old claims. It's perfectly fine to conclude that your previous claims are not supported by the evidence. Here's an example written in Rmd format.

You can write your report either on Google Docs (handing in a link to your document after making absolutely sure that you have shared the document with “anyone with the link.” You can also write your new report in R/Markdown for 5 points extra credit — see the instructions here and the sample .Rmd document that you can copy as a starting point. (This may show up as a file download in your browser, so check your “downloads” directory.)

Hand in your individual report here on Moodle

Assignment due by end of exam week:

Exercises from Chapter 15 onward
Revisions to quizzes due .
Hand in analysis and report for your survey project by putting links on Moodle.
Hand in Mac-Weekly style report for your survey project on Moodle. (before end of exam period)

In Class: End-of-semester course survey. This is required but anonymous. Once you've completed the survey (following the previous link), then go to this link at Moodle to tell us that you've done it. Be honest! Thanks.

Notes: i :: Class notes

Class Resources

R: RStudio server :: Revisions to MOSAIC commands (e.g. props and counts) :: Command Crib Sheet

Moodle site :: AcroScore initial passwords ::

Instructions for sharing Google Documents for group work and handing in.

Faculty Office Hours

“Office hours” are regularly scheduled times when faculty are available in their offices.

Danny Kaplan, Thurs 10:30am-12:00, Fri 1-2pm
George Leiter, Tues/Thurs 12:30-3:00

Or just drop by! Or drop us a note and make an appointment at some other time.

Preceptors and Review Sessions

.		.
Ha Song Pham	hpham1@macalester.edu
Xiang Yuan (Henry) Yang	xyang1@macalester.edu	.
Jungwon Lee	jlee9@macalester.edu	.
Jia Gu	jgu@macalester.edu

Preceptor Review Sessions: starting Monday Sept. 10. Held in the MSCS reading room, Olin/Rice 254 (in the middle corridor)

Tues 8:30-10:00 pm, Jia Gu
Wed 3:00-5:00 pm, Jungwon Lee
Thurs 3:00-5:00 every other week, Henry Yang – Oct 4, 18, Nov 1, 15
Sat 3:00-5:00 pm

Preceptor coordination document (restricted)

Wed 5 Sept

Topic: Introduction to the course. Randomness, modeling, and inference.

Reading:

Statistical Modeling textbook §1.1, 1.2, 1.3. The book is available in both new and used forms in the Macalester bookstore. Students on the wait list for the course may, understandably, not want to buy the book until they know they will be joining the course. To make sure that everyone has access to the book, the publisher provides the first few chapters in online format: Chap. 1 and Chap. 2
A recent newspaper report that frames some of the technical issues covered by the course.
An orientation for parents. This is the link for notes for a class the college ran for parents of entering students on Saturday 1 Sept. Feel free to send it to your parents or others who ask what you're going to be doing in statistics.
Login to R. Instructions. The RStudio server

Assignment:

Do the reading!

In Class:

Activities on randomness.
Basics with R.
Using AcroScore a video

On Your Own:

Watch this one-hour documentary: The Joy of Stats with Hans Rossling

Notes: instr

Fri 7 Sept

Topic: Data: cases, variables, tables, and codebooks

Reading:

Statistical Modeling §1.4, §2.1, 2.2, 2.3, 2.4
Recent news report: Sleep and Senility

Assignment:

A video on using AcroScore. Your initial passwords are here on Moodle. Change your password to something more sensible by logging in here and going to the Settings item, then the “Change Password” tab. AcroScore works only on Firefox and Safari browsers, sorry.
Exercises: 1.RQ, 1.01, 1.02, 1.04, 1.05, 1.10
Reading Reflection on Sleep and Senility. Hand in on Moodle.
Quick survey to do before class: link

In-class:

Link to Moodle site for your introductory spreadsheet Starting with R. (Write a description of this activity)

Notes: instr Today's class

Mon 10 Sept

Topic: Organization of data (cont.), descriptive statistics

Reading:

SM §2.5, SM §3.1, 3.2, 3.3
XKCD cartoon

Assignment:

Exercises 1.11, 1.12, 2.RQ, 2.02, 2.04, 3.01, 3.23, 3.53
Elevator experiment: Instructions 3.36. – Upload your file or a link to your file here.
Start-of-course knowledge survey

In-Class:

Table on Marital status of active-duty forces from data.gov and the PDF handout. Hand in your sample data file here on Moodle.

Notes: i :: Class notes

Wed 12 Sept

Topic: Descriptive statistics: variance

Reading: SM §3.4, 3.5, XKCD cartoon

Assignment: Exercises 2.09, 2.14, 2.22, 3.02, 3.03, 3.04, 3.05, 3.06, 3.08, 3.11, 3.12, 3.15, 3.17, 3.18, 3.19

In Class: Measures of a distribution

On Your Own: A RadioLab episode about stochasticity

Notes: i :: Class notes

Fri 14 Sept

Topic: Groupwise Models

Reading: SM Ch. 4

Assignment: 3.14, 3.16, 3.RQ, 4.03, 4.04, 4.05, 4.06, 4.10

In Class:

Weekly Quiz. This week, it's about R. A study-guide is available here.

Notes: i Class notes

Mon 17 Sept

Topic: Model values, residuals, and variance. Sampling and resampling.

Reading: SM Ch. 4 (review). SM §5.1, 5.2

Assignment: 3.54, 4.RQ, 4.07, 4.08, 5.01, 5.02

Notes: i :: Class notes

In-Class: Unfit models activity

Wed 19 Sept

Topic: Confidence intervals

Reading: SM §5.3, 5.4, 5.5, 5.7

Assignment: 5.RQ, 5.03, 5.12, 5.17, 5.20

In Class: Precision and sampling. (Precision is easy. Accuracy will take the rest of the course!)

Notes: i :: Class notes

Fri 21 Sept

Topic: Language of Models

Reading: SM §6.1, 6.2, 6.3, 6.4

Assignment:

Required: 5.13, 5.23, 5.30, 5.31, 6.01, 6.02, 6.04, 6.11, 6.13
Optional, for the theoretically oriented. 5.40

In Class: Friday Quiz

On Your Own:

Bias in sampling: Steven Strogatz on why your friends have more friends than you do
One of our data sets is based on the General Social Survey from 1985. Here's an article based on the ongoing General Social Survey in 2012.

Notes: i :: Class notes

Mon 24 Sept

Topic: From Model Terms to Formulas and Coefficients Reading: SM §6.5, 6.6, 6.7, SM §7.1, 7.2 Assignment:

Required 6.RQ, 6.05, 6.10, 6.12, 7.01, 7.02
Extensions 6.20, 6.21, XKCD cartoon. What regression model for this?

Notes: i :: Class notes

Wed 26 Sept

Topic: Formulas and Coefficients

Reading:

SM §7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9
XKCD cartoon

Assignment:

Exercises 7.RQ, 7.04, 7.05, 7.10, 7.11, 7.12, 7.13, 7.15
Car project (group project): 7.23 Hand in here on Moodle
Instructions for reading Google spreadsheets into R

In Class:

Untangling House Prices

Notes: i :: Class notes

Fri 28 Sept

Topic: Fitting and Least Squares

Reading: SM §7.10, SM §8.1, 8.2

Assignment: 7.14, 7.16, 7.21, 7.22, 7.31, 8.01

In Class: Weekly Quiz

Notes: i :: Class notes

Mon 1 Oct

Topic: Geometry of Least Squares Fitting

Reading: SM §8.3

Assignment: 8.02, 8.04, 8.05, 8.11

In Class:

Help write the R crib sheet for Math 155. Tell us your coolest or hardest to remember R command. Enter here on Moodle
Case versus Variable Spaces

Notes: i :: Class notes

Wed 3 Oct

Topic: Redundancy and Colinearity

Reading: SM §8.4, 8.5

Assignment: 8.RQ, 8.05, 8.12

In Class: Redundancy activity

Notes: i :: Class notes

Fri 5 Oct

Topic: Correlation and Partitioning, \( R^2 \)

Reading: SM Ch. 9

Assignment: 9.01, 9.02, 9.04, 9.05

In Class: Weekly Quiz

Notes: i :: Class notes

Mon 8 Oct

Topic: Correlation and Partitioning (continued)

Reading: SM Ch. 9 (review)

Assignment: 9.RQ, 9.10, 9.11, 9.12, 9.13, 9.21

Notes: i :: Class notes

Wed 10 Oct

Topic: Total and Partial Relationships

Reading: SM Ch. 10

Assignment: 9.22, 9.23, 10.01, 10.04, 10.10

In Class:

Total versus Partial
Form groups for Survey Project and enter the names etc. here on Moodle

Notes: i :: Class notes

On Your Own: Is coffee good for you?

Fri 12 Oct

Topic: Total and Partial Relationships (continued)

Reading: SM Ch. 10 (review)

Assignment: 10.RQ, 10.05, 10.11, 10.12, 10.20

In Class: Weekly Quiz

Notes: i :: Class notes

Mon 15 Oct

Topic: Confidence Intervals on Coefficients

Reading: SM Ch. 12

Assignment: 10.21, 12.01, 12.02, 12.04, 12.05

Notes: i :: Class notes

Wed 17 Oct

Topic: Review for Exam

Reading:

Assignment: Review Problems (to be posted)

Notes: i :: Class notes

Fri 19 Oct

Mid-term Exam

Review Problems: without answers and then again, with answers

Exam Answers and Grading Rubric

Mon 22 Oct

Topic: Confidence Intervals on Coefficients (continued)

Reading: SM Ch. 12

Assignment: 12.RQ, 12.10, 12.12

Notes: i :: Class notes

Wed 24 Oct

Topic: Probability Models

Reading: SM Ch. 11

Assignment: 11.RQ, 11.01, 11.02, 11.04, 11.05, 11.10, 11.21, 11.22

Notes: i :: Class notes

Fri 26 Oct

FALL BREAK

Mon 29 Oct

Topic: Continuous Random Variables. Start Hypothesis Testing

Reading: Review SM Ch. 11, start on SM Ch. 13

Assignment:

11.11, 11.30, 11.35, 11.37, 13.01

On Your Own: Play with this applet written over fall break for Math 155 students to be able to play with the geometry of projection and of confidence intervals. Here's the story:

In the “Deterministic” tab, you choose coefficients on B and a covariate C that generate the “real” or “population” values (marked x) of a response variable A.
In the “Measurement” tab, the “population” is sampled randomly. This results, of course, in a measured response that's likely different from the “real” value.
In the “3-D View” tab, you can look at the situation from above the B,C-place. The “real values” (marked x) have a random component added to them (shown in red). For fitting, this is projected onto the B,C-plane to a sampled version of vector A.
In the “Confidence Intervals” tab, the red area shows the likely locations for random simulations of the residuals residual added to the measured A to simulate a new measured value. Projecting these back onto B gives an estimate for the confidence interval.

Play with the various sliders and such. See what you can make of the simulation. We'll talk about it in class.

Notes: i :: Class notes

Wed 31 Oct

Topic: Hypothesis Testing

Reading: SM Ch. 13

Assignment:

13.RQ, 13.02
Discuss your draft Survey Project with your instructor.

In Class: 13.15

On Your Own: An introduction to Bayes Theorem

Notes: i :: Class notes

On Your Own: The paper that won the 2012 Ignobel prize in neuroscience: On brain activity in a dead salmon

Fri 2 Nov

Topic: Testing Whole Models with \( R^2 \) and F

Reading: SM §14.1, 14.2, 14.3, 14.5

Assignment: 14.01, 14.02

In Class:

Weekly Quiz
14.22

Notes: i :: Class notes

Mon 5 Nov

Topic: Interpreting the p-value

Reading:

SM §14.4
XKCD cartoon and another and yet another

Assignment: 14.RQ, 14.04, 14.05, 14.11, 14.12, 14.24

In Class: 14.21

Notes: i :: Class notes

On Your Own: Journalistic deficit disorder

Wed 7 Nov

Topic: Hypothesis Testing on Parts of Models

Reading: SM Ch. 15

Assignment: 15.RQ, 15.01

In Class: Evaluating your election result model

Notes: i :: Class notes

On Your Own: Dance of the p-values

Fri 9 Nov

Topic: Hypothesis Testing on Parts of Models (continued)

Reading: SM Ch. 15 (review)

Assignment: 15.02, 15.04, 15.05, 15.11

In Class: Weekly Quiz

Notes: i :: Class notes

On Your Own: What is a p-value?

…………..

Saturday 10 November

WagonFest mathematics talks in O/R 250

2:10-2:55 pm: Paradoxical Dice
3:00-3:45 pm “From Filters to Hat Problems”
4:00-4:45 pm “A Drug Induced Random Walk”
4:50-5:20 pm “Mathematics in Snow” (about sculpting snow)

Hand in a paragraph describing one or more of these talks for extra credit in the homework. Link to Moodle …………….

Mon 12 Nov

Topic: Choosing model terms

Reading: XKCD cartoon

Assignment: 15.12, 15.21

Notes: i :: Class notes

Wed 14 Nov

Topic: Non parametrics

Reading: XKCD cartoon on frequentist vs Bayesian

Assignment: *15.22

Get final approval for your survey project from your instructor.

Notes: i :: Class notes

Toward the End of the Semester

You've now seen the core concepts and methods of statistical modeling. The rest of the semester is intended to help you review and solidify your understanding. We'll do this by using statistical models in three contexts:

Yes/No models — logistic regression. The aims here are to provide you with an important technique that employs the logic of statistical modeling but with a slightly different nomenclature.
Causation and the choice of covariates. You've seen how to choose model terms based on ideas from statistical inference and “evidence.” Now you'll see a new criterion for selecting model terms that respects what you believe is true about causation.
Experiment. The basic idea is to impose an intervention on a system and then analyzing the results. Experiments provide the most compelling form of evidence, yet they cannot always (or even often) be performed.

This is a good time to start to review. As we explore these new contexts, do the exercises from Chapter 15 to solidify your understanding of that basic material. These exercises will be due at the last class day.