# Contact Information

Email: eroualdes@csuchico.edu

# Content Delivery

Links to course materials and content will be posted to my website. Most content will be delivered asynchronously via videos posted to YouTube, within my YouTube Channel. Please note that there exist videos for other classes on this channel, so I'll post links on my website to specific videos meant for this class.

# Office Hours

Office hours will be held synchronously during the regularly schedule class times, MWF 12 - 12.50pm, on Zoom with passcode purplerain.

Office hours are time for you to productively study and/or work on any course assignments with a coach/cheerleader (me, Edward) right there beside you. The hope is that this will be helpful to those who need more structure in their studies. Don't feel obligated to attend these hours, just know that they are here for you if you need/want them. Eavesdropping on other students' questions and the answers is highly encouraged.

# Course Communication

This semester we will be using Piazza for class discussion amongst all students. Rather than emailing me questions, I encourage you to post your questions on Piazza. Feel free to answer eachother's questions.

Signup for Piazza

If you prefer direct, identifiable communication, email me at eroualdes@csuchico.edu.

If you prefer indirect, 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.

# 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 biology and related disciplines. 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 in decision making
• Begin learning programming language R

# Textbook

• Most material will come from my YouTube videos.
• This free, PDF book is a good resource for a great deal of introductory applied statistical methods. The content is presented as it is most commonly found in the life sciences. This makes for more dense reading than most can tolerate. Nonetheless, our goal is to understand the concepts found in this book.
• This free, online 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.

• Consistent access to a computer will be essential to master the material of this course. If this presents a problem for you, please email me.
• We will learn to code in R using RStudio.

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
5 Reports @ 10% each 50%
Course Notes 50%

# Assignments

All assignments must be created using RMarkdown and compiled into PDF or HTML. 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. All assignments must be emailed to me using a subject line that matches, MATH 315 lastname assignment. So if I were submitting a report about two sample t-tests, the subject line of the email containing the attached assignment would like MATH 315 roualdes two sample t-test.

## Reports

In place of exams, you will write and submit 5 reports, each report will be about a different statistical method. The reports may be submit at anytime throughout the semester, (I'll suggest some general times for those who want a more formal schedule) with a final due date as the end of our scheduled final.

1. 2 t-tests about two different means (or proportions, since proportions are just means by another name)
2. one two sample t-test
3. one ANOVA
4. one Simple Linear Regression
5. one Multiple Linear Regression (also known as ANCOVA)

For full credit, each report must follow the general outline below, use a dataset different than you use for your examples in your Course Notes, contain at least two plots (one plot and one table is OK, but not preferred), and every report from ANOVA forward must address the model assumptions within the analysis using more plots if possible. If you desire you can re-submit at most one time to try to improve your grade after receiving feedback on it.

### Report Outline

#### Introduction

Write a paragraph describing the data, as if you collected the dataset even though you most likely have not. Further, this section must include summary statistics and complementary plots/tables (plural). Use complete sentences to describe your statistics and plots.

#### Analysis

This section will describe what statistical analysis you will perform and why. Then you will perform that analysis, showing all appropriate code. This section should contain at least one plot that helps your reader visualize the statistical method you are using. You should use complete English sentences to describe your statistical analysis.

#### Conclusion

State your results in plain and clear, complete English sentences without the statistical jargon.

# Course Notes

In place of more common homework assignments, you will create Course Notes in a style that I dicate. I will describe the format of the Course Notes in a YouTube video, while guiding you through first steps with RMarkdown.

Of course, you are allowed to take your own notes while watching the YouTube videos however you want, but I want to help you curate a formal set of notes to be used as a reference for all your future work in statistics. I will refer to these notes as your (capitalized) Course Notes.

You will email me your Course Notes at (roughly) the end of every month throughout the semester. Your Course Notes grade will suffer if you email me your Course Notes closer to the end of the next month, or if you skip any months. The notes you email me should be compiled into PDF or HTML format.

There will only ever be one file containing all your Course Notes; you will simply continue adding notes into this one file. You should name your Course Notes as lastname underscore coursenotes. So my Course Notes file will have filename roualdes_coursenotes.html

I will provide feedback on your Course Notes as you submit them. Your Course Notes grade depends on how well you address my feedback and adhere to the format described in the Course Notes YouTube video.

# Tests

There will be no formal tests. Instead you must complete 5 reports as specified above.

# Make-Up Policy

Based on the Course Grading and Assignments, a make-up policy seems moot.

# Diversity Policy

Respect: Students in this class are encouraged to speak up and participate during Zoom meetings and on Piazza. 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).

Students are permitted and encouraged to collaborate on all assignments. 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 assignments 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 at Student Judicial Affairs.

# 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: www.csuchico.edu/title-ix.

# 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