Welcome to MATH/STAT 242!

MW 12:15-1:45 PM, or

MW 2:00-3:30 PM

Class Meets: Morken 214

I love this course because it has real applications to everyday life and is still grounded in basic principles of logic and scientific thinking.

Learning Goals*

The main learning goal of this course is that students will be able to... (drumroll)

...navigate data-based claims that permeate our society and develop expertise to make informed data-based decisions

more specifically, students will:

Gain insight into the statistical process as they execute a basic statistical study, and
Develop expertise interpreting and critiquing a statistical study.

These learning goals work well together; as you learn how to critique a statistical study, you'll better be able to critique other studies, and visa versa.

All the assignments and lessons in this class are geared toward these learning goals.*

*More learning outcomes from the mathematics department and PLU's general education are given later.

Stuff you need

Technology

After many years trying different platforms, I have landed on using Google Classroom (GC). You will need to turn your completed assignments into a pdf and submit into GC. Quizzes will also be held through GC.

To use your phone to scan homework to a pdf & submit to GC:

Submit with iPhone: https://youtu.be/EIUpRE_xPKc

Submit with Android: https://youtu.be/AbLFIgJq5sc

You may wish to type assignments using LaTeX but I don't especially recommend it unless you're quick with that program.

This course uses R statistical software with R Studio as a graphical interface (both of which are vailable FREE!) Instructions for downloading and getting started are in ModernDive, here.

Why use R?

It is free
It is powerful
It is one of the more commonly used programs for statisticians
It is open-source (always growing and improving)
It is great on your resume!

A note on technology:

Some students in this course are computer science majors, but others are not. Regardless of your major, no prior comupting knowledge is required.

R can be intimidating (and at times frustrating) at first, but remember that no computing expertise is expected of students entering this course. That means you can reach out to me and your peers for help with ANY aspect of the computing. We will strengthen our computing muscles together!

PLU email

To be successful in this course, you will need to activate and use your PLU email (ending with @plu.edu). Your email will contain an invitation to the course website that we will use daily, as well as occasional announcements and notifications about the course.

Although we will use Google Classroom for delivering and submitting assignments and forums, in general we will use this website for communicating course content.

Prereq Knowledge & Skills

The catalog says that the prerequisite is Math 145. If you meet that prerequisite, you belong in this class! Welcome! I'm so glad you're here!

Ok but students often still ask what do you really need to know to be successfull in this course? You should be comfortable calculating percentages based on raw data, and visa versa. (For example, you should feel comfortable with #7 a,b,c and #8 a,b,c, in the Exercises in Section 1.4 of our IMS textbook).

You also need to have patience and grit; some aspects of the course will require you to persevere, ask questions, and hang in there until it "clicks."

Katyal, Prateek. (2019) "You Got This Lighted Signage." Licensed under Pexels. Retrieved from Pexels.

Books

You don't need to buy a textbook for this course. Here are a few I may occasionally refer to or recommend:

ISI: If you're the type of person who needs a textbook to be successful in a course, the book that most closely matches our coursework is Introduction to Statistical Investigations by Tintle et al., which is available to rent electronically for about $50. You do *not* need this book to succeed in our class.

ModernDive: We will occasionally use this FREE online book: A ModernDive into R and the Tidyverse by Chester Ismay and Albert Y. Kim

IMS: Introduction to Modern Statistics (IMS) by Çetinkaya-Rundel and Hardin can be accessed online for FREE. I like IMS because it uses the most cutting-edge tools to teach introductory statistics, and it covers the most relevant topics.

(In other words it is not a crusty outdated old book.)

Course Format & What to Expect

Our course will remain in virtual format, despite how the rest of PLU may adjust to blended learning. Here is what you can expect from me:

Communication: I will make it clear what the tasks are each day and what upcoming deadlines are.
Purposefulness and Respect for students' time: I have carefully examined what topics can be cut so that our course is focussed with each lesson worth your time. I've also re-examined the homework problems that I usually assign, making sure each homework problem is relevant and aligned with the learning goals for the course.
Equity and Access: Some students in our class have siblings to care for, unpredictable work schedules, unreliable Wi-Fi, and other challenges that make it very difficult—if not impossible—for them to be able to participate in synchronous (i.e., live, real-time) class sessions. Other students want/need the live, real-time help with their learning. Colleagues have advised me not to record our live real-time sessions because students will become shy and hesitate to participate. What a conundrum! So how do I serve all students, including those who want live in-person time as well as those who need access outside of class? Well, I have designed a course that blends both types of learning so that students can access course content outside of class and then meet in-person to ask questions and participate in activities that bring concepts to life. The live sessions will give us a chance to learn together in community. Students who miss our real-time class sessions will unfortunately not get the full experience and will most certainly miss out, however, in this blended approach those students might still be able to be successful in the course.
Transparency: Last semester, students were generally really positive about my teaching, but offered suggestions for how to make the course better. I have implemented many of these and will continue to try to make it clear what I'm thinking, why I'm doing it, and asking you how its going.

GENERAL Weekly Schedule

I created this table to help you get a sense of what our week will typically look like:

Coursework

Labs/Assignments: 40%

These include homework assignments, and in-class assignments (which can become homework if unfinished during class). Some may require or allow you to work together in small groups.

Project Components 25%

To support our learning goals, I will walk you through the steps of statistical analysis and you will have the chance to apply it to a group project.

Exams: 30%

Midterm: 10%

Final Exam: 20%

Weekly Check-up Quizzes and Miscellaneous: 5%

You can expect weekly check-up quizzes administered online, and designed to help you receive quick feedback on whether you are grasping the main ideas of the week. There is no time limit, and you are welcome to work with other students on the quizzes. NO quiz retakes are given, even for good reasons, such as illness. Instead, I will curve heavily so that missing 1 quiz will not affect your grade, or I will allow some sort of retake/extra credit for missed problems (method to be determined later). Notice this is only worth 5% of the grade, and is designed to provide feedback but not a lot of stress.

Grades

Grades of A, A—, B+, B, B—, C+, C, C—, D, and F are assigned at cutoffs of 93%, 90%, 87%, 83%, 80%, 77%, 73%, 70%, 60%, and 0%, respectively.

Please see your PLU Catalog for general policies on grading, incomplete, P/F, and W grades. Online you can also find the PLU policy in case of an academic emergency (e.g., natural disaster, epidemic, etc.).

Learning Objectives (Formally)

This course is aligned with the following PLU Math Department Learning Objectives:

Communication: Be able to read, interpret, write about, and talk about mathematics (or statistics).
Application: Be able to apply mathematical concepts to concrete situations.
Disciplinary Citizenship: Develop collaborative skills, independence, perseverance, and experience with open-ended inquiry.

This course is aligned with the following Statistics Minor Objectives:

Develop novice-level statistical thinking, particularly with respect to linking appropriate inferences to study design (e.g., correlation does not imply causation).
Demonstrate the ability to appropriately select and use statistical models (e.g., normal distribution, t-distribution, binomial distribution) and statistical methods (e.g., regression, resampling).
Develop facility with one or more professional statistical software programs.

This course is aligned with the following General Education Learning Outcomes (QR):

Students will solve problems by interpreting quantitative information in context.
Students will demonstrate the ability to work with mathematical notation, techniques, tools, and concepts.
Students will create and critique logical arguments supported by quantitative evidence or symbolic relationships.

This course is aligned with the following General Education Learning Outcomes (NS):

Students will understand and apply basic concepts from a particular discipline of the natural sciences.
Students will identify and explain organizing models of a discipline.
Students will identify social and ethical issues pertaining to a discipline.

Things we will not cover in this class (but you can take Stat 342, 348, or 442 to learn more!) include:

- Heavy lifting with data cleaning/data manipulation in R (348)- Deeper discussions about data and ethics (348)- Principles of good Data Visualization (348)- Sampling methods (in particular: random sampling methods (348) - Qualitative Methods (348)- Bias implicit in any data collection or machine learning technique; ultimately learn about - how the decisions you make affect the results of your analysis; there is no “unbiased” analysis. (348)- Mathematical probability underlying the statistical techniques we use (342, capstone)- Actuarial exam prerequisite concepts (442)- Regression with multiple predictors, interactions, indicator variables, and linear algebra applications (442)- Methods that don’t fit into these contexts (348, 442, capstone).

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