Welcome to MATH/STAT 442!
Welcome to MATH/STAT 442!
MWF 9:30-10:30am
Class Meets: Morken 216
Embedded Files
Learning Objectives*
Learning Objectives*
The main learning goal of this course is that students will be able to build, interpret, evaluate, and critique statistical models
More specifically, students will:*
Examine underlying principles of partitioning variance in bivariate and multiple regression models.
Relate anova results to variable characteristics (e.g., centered, uncentered, orthogonal, co-linear, etc.).
Connect regression output to model features, with particular emphasis on nuances, decisions (whether implied by software or not), and common errors interpreting multiple regression results.
Critique and evaluate different measures for selecting models.
Develop fluency using R Statistical Software to do all of the above, including data preparation and management, transformations of variables to satisfy conditions for linear regression, model diagnostics, and communication of results using a package that weaves results with exposition (e.g., knitr, etc.)
*More learning outcomes from the mathematics department and PLU's general education are given later.
Stuff you need
Stuff you need
Technology
Technology
We will submit HW and receive feedback on HW via Google Classroom (GC). You will need to scan your homework and submit as a pdf into 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 COMPUTING KNOWLEDGE is expected or 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
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.
Book
Book
Most of my other courses use open-source materials, but in this class we will make some use of our primary text: STAT 2 by Cannon et al (2013).
ISBN: 978-1464148262
I will keep a copy either on reserve in the PLU library or in Morken.
A newer edition is available, but I prefer the first one.
Coursework
Coursework
Labs/Assignments: 80%
Labs/Assignments: 80%
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.
Miscellaneous: 5%
Miscellaneous: 5%
You can expect various online reflections, quizzes, and activities. These are *not* designed to be high-stakes, as the 5% weight indicates.
Final Project 15%
Final Project 15%
To support our learning goals, you will create, critique, and select from a series of statistical models in a final course project. More details as the time arises.
Grades
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.).
GENERAL Weekly Schedule
GENERAL Weekly Schedule
I created this table to help you get a sense of what our week will typically look like:
Learning Objectives (Formally)
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 PLU Data Science Major Learning Outcomes:
Design: Be able to critically analyze a problem and to design, implement, and evaluate a solution that meets requirements.
Communication: Be able to effectively communicate technical concepts in oral and written form.
Application: Be able to apply mathematical or statistical concepts to concrete situations.
Disciplinary Citizenship: Develop collaborative skills and independence; have 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 (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.
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