Syllabus Math 315-03 Statistical Methods I

Section 03 Holt Hall 257 MWF 12 - 12.50pm

Contact Information

Edward A. Roualdes (call me Edward)


Office hours: Holt 204 on MW 09 - 09.50am and Th 2 - 3pm 3 - 4pm

Course Description

Single and two sample inference, analysis of variance, multiple regression, analysis of co-variance, experimental design, repeated measures, nonparametric procedures, and categorical data analysis. Examples are drawn from various life sciences and occassionally economics. The statistical programming language R is used. Appropriate for biology, agriculture, nutrition, psychology, social science and other majors. 3 hours discussion.

Student Learning Objectives / Goals

  • Understand basic concepts of experimental design and applied statistics
  • Accept and work with uncertainty/variation
  • Communication of statistical results, acknowledging uncertainty/variation
  • Begin learning programming language R


We will primarily use OpenIntro Statistics, 4th Edition (OS4). A pdf of this book is free, just slide the amount you are willing to pay to $0.00. Alternatively, you can order a paperback version of this book for $25.

The following book is more concise, but a bit too progressive for many of the disciplines this class seeks to serve. So I’ll rely on it as a reference for keywords, ideas, and concepts. Simply put, this book is easier to read.

C. Ismay and A Y. Kim. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Creative Commons Attribution-NonCommercial-ShareAlike 4.0. 2020.

Additional Requirements

  • Access to a computer will be essential to master the material of this course. If you don’t have immediate and consistent access to a laptop, please speak to me as soon as possible.

  • We will learn to code in R using the programming environment RStudio Desktop, both of which are free software.

Content Delivery

Lectures are in person at the times listed above. No recordings will be available. As Gil Scott-Heron says, the revolution will not be televised; this class will be live.

All course materials will be posted to my website:

Course Communication

The absolute best place to ask a question is during lecture. I understand, though, that not all students feel comfortable asking questions publicly.

If you prefer more private and in person communication, come to office hours.

If you prefer written and identifiable communication, email me at . If your questions become too complex for email, as judged by me, I reserve the right to ask you to come visit my office to receive your answers in person.

If you prefer written and anonymous communication, I have created an anonymous Google form named ask. If you intend to ask a question anonymously, please remember that this form is anonymous. The implications of this anonymity are greater than you might at first think; take a minute to think through how you want me to address you specifically, if I don’t know who you are. Further, there might be some questions I deem to not deserve a response. If you intend to give me feedback, please give constructive and respectful feedback. If at any point this form goes poorly, as judged by me, I reserve the right to take it down.

If for any reason I need to address everyone in the course, I will send you an email to your student email account, eg

Course Grading

Your final grade for this course will be given according to the \(+/-\) grading system, based on the following percentages and scale: \(90 - 100\), A; \(80 - < 90\), B; \(70 - < 80\), C; \(60 - < 70\), D; \(<60\), F.

Component Percentage
Homework 60%
Quizzes 10%
Tests: 3 @ 10% each 30%

Grades will be posted to a shared (between me and each of you, individually and exclusively) Google Sheets file.


All assignments must be created using Quarto, compiled into HTML (preferred) or PDF, and uploaded to our shared Google folder. If you prefer to compile your assignments, for your own records into Microsoft Word, you are more than welcome to, but I don’t want Word documents.

There will be so much time to work on homework in class, that homeworks will be part at home and part in class. This is part of the reason that access to a laptop is essential to this course.

Each homework assignment should be uploaded into its own subfolder, called say homework 01 which itself is located within our shared Google folder. Tests and quizzes should also go into their own subfolders named as say test 02 and quiz 05.

Notice how I’m attempting to force you on proper computer organization. Folders should provide the context, not file names.


There will be three tests. Two mid-terms and one final. These too will be submit via a shared Google drive folder. Each test should be uploaded into its own subfolder, called say test 03, which itself is located witihin our shared Google folder.

Make-Up Policy

Homework assignments can be submit late for a maximum of 70% credit. You can submit a homework assignment as late up until the next test, but not after.

You can make up a quiz or a test so long as you missed the test for an unavoidable emergency. Please contact me within 24 hours of the quiz/test to let me know of your unintended absence, and so that we can schedule your make up.

Diversity Policy

Respect: Students in this class are encouraged to speak up and participate during class meetings. Because the class will represent a diversity of individual beliefs, backgrounds, and experiences, every member of this class must show respect for every other member of this class (this includes me).

Academic Integrity Policy

Students are permitted and encouraged to collaborate on all assignments other than tests. However, each student must turn in their own work. Further, it is the expressed expectation of this instructor that all students demonstrate integrity and individual responsibility in all actions related to this course. Unethical behavior of any kind is unacceptable and will be prosecuted vigorously. Any sign of cheating in any way on any course assignment will be addressed directly, according to University standards. If you do not understand what plagiarism is, or what cheating entails, you must seek information regarding this matter from the current University Catalog and from me. The consequences of plagiarism begin with a failing grade on the work, and possibly a failing grade in the course, depending upon University action. More information is found on the Student Conduct, Rights, and Responsibilities campus webpage.

Disability Support

If you have any disability related needs, please contact Disability Support Service (Colusa Hall 898-5959 or campus information 898-INFO for directions) on campus to obtain the appropriate documentation. Afterwards, email me to identify your needs within the first two weeks of class so that any necessary arrangements can be made.

Confidentiality and Mandatory Reporting

As an instructor, one of my responsibilities is to help create a safe learning environment on our campus. I am required to share information regarding sexual misconduct with the University. Students may speak to someone confidentially by contacting the Counseling and Wellness Center (898-6345) or Safe Place (898-3030). Information on campus reporting obligations and other Title IX related resources are available here:

Course Outline

  • Introduction to Data
    • Data Basics
    • Data Collection
    • Observations and Experiments
    • Types of Variables
  • Summarizing/Visualizing Data
    • Categorical Data
    • Contingency Tables
    • Summary Statistics
    • Box Plots / Histograms
    • Plots by Group
    • Scatter Plots
    • Group Summary Statistics in R
  • Distributions
    • Discrete
    • Continuous
    • Normality and Its Approximations
  • Introduction to Inference
    • Point Estimates
    • Sampling Distribution
    • Confidence Intervals
    • Central Limit Theorem
  • Hypothesis Testing – One and Two sample tests
    • Testing Means
    • Testing Proportions
    • Testing Differences
  • ANOVA, Correlation, and Linear Regression – ANOVA and Simple Linear Regressiopn
    • ANOVA
    • Correlation
    • OLS
    • OLS’s Assumptions
    • OLS Diagnostics
    • Inference About OLS Parameters
  • Multiple Linear Regression
    • Inference and Interpretation About Parameters
    • Assumptions
    • Diagnostics
    • Transformations – if we have time