Syllabus

Math 201: Elementary Statistics

Course Information

Term: Fall 2023

Instructor: Isaac Quintanilla Salinas

Email: isaac.qs@csuci.edu

Office Location: BTE 2840

Office Hours:

OH Course R Programming TEA Math/Stat Hour
Day MW Tue Th
Location BT 1462 LRC BTE Courtyard
Time 4:30-6:00 PM 10-12 AM 12-1:30 PM

Or by Zoom appointment.

Lecture: Monday/Wednesday 10:00-11:50 AM (Sec 01) or 2:00-3:50 PM (Sec 02) in BT 1462

Course Description

Critical reasoning using a quantitative and statistical, problem-solving approach to solving real-world problems. Topics include: probability and statistics, sample data, probability and empirical data distributions, sampling techniques, estimation and hypothesis testing, ANOVA, and correlation and regression analysis. Students will use standard statistical software to analyze real-world and simulated data. GenEd: B4

Learning Outcomes

  • Prepare students for advanced courses in data-management and statistics
  • Reason both inductively and deductively with quantitative information and data
  • Use statistical software for complex statistical analysis of real-world and simulated data
  • Empower students to apply computational and inferential thinking to address real-world problems
  • Write the results of a statistical study and draw conclusion in reports

Textbook

This course will use Coursekata, a Canvas-embedded textbook with built-in R functionality.

Embedded Peer Educator (EPE)

This course will have two embedded peer educators:

  • Devin Frost (Sec 01)

    • Email: devin.frost091@csuci.edu

  • Amanda Parks (Sec 02)

    • Email: amanda.parks743@myci.csuci.edu

An EPE is there to help you understand the material and succeed in the course. They will be leading in class assignments. hold special tutoring sessions, and support you in your R programming. These educators are trained with study skills that will set you up with success in college.

Course Grading

Category Percentage
Reading Assignments 20%
Lab Assignments 15%
Weekly Quizzes 15%
Participation 5%
Exam 1 15%
Exam 2 15%
Exam 3 15%

At the end of the quarter, course grades will be assigned according to the following scale:

A+ 98 – 100 B+ 87 – <90 C+ 77 – <80 D+ 67 – <70
A 93 – <98 B 83 – <87 C 73 – <77 D 63 – <67 F < 60
A– 90 - <93 B- 80 – <83 C– 70 – <73 D– 60 – <63

Participation

Participation is based on short writing assignments conducted in class. There will be no make ups for these writing prompts.

Lab Assignments

Lab assignments are designed to expand your statistical knowledge. These will be completed in JupyterHub which can be accessed from Canvas. There are approximately 2 lab assignments every week that you can complete during class time. The lab assignment will be due either on Tuesday or Thursday at 11:59 PM following the lab day it was assigned.

Reading Assignments

Reading assignments are designed to teach you different statistical concepts and R Programming. As the course progresses, many of the concepts build on each other. Therefore, the assignments encourage you to read each chapter in an appropriate amount of time. You must read the chapter and answer the questions by the Sunday night at 11:59 PM. The 2 lowest reading assignments will be dropped.

Quizzes

Starting on Week 2, there will be a weekly quiz every Friday. The quiz will become available for you to take on Canvas at 12AM to 11:59 PM every Friday. The quiz is designed to test whether you recall the material from the previous week. You are expected to complete the quiz with out using any material such as your notes, textbook, or internet. Additionally, you will get 90% of the quiz grade for simply taking the quiz. The remaining 10% is based on getting the question correct. There are a total of 12 quizzes in the semester. The lowest 3 quizzes will be dropped from your class grade. There are no make up quizzes.

Exams

There will be three in exams. Exam #1 will on Sept 25, Exam #2 will be on Nov 8, and Exam #3 will be on Dec 6 at 8-10 AM (Sec 01) or 1-3 PM (Sec 02). While the exams are not considered cumulative, the material builds on each other. Developing a strong understanding of the material through out the course is important for your success. At the end of the semester, your lowest exam grade will be replaced by your median average exam grade. This course will operate under a zero-tolerance policy. Talking during the time of the exam, sharing materials, looking at another students’ exam, or not following directions given will be subject to the University’s academic integrity policy.

Extra Credit

There will be 3 extra credit opportunities worth a total of 10% of your overall grade. (There are no make-ups for missed extra credit assignments!) More information will be provided on the extra credit assignments on a later date. Information on the extra credit can be found here.

Class Schedule

The following outline may be subject to change. Any changes will be announced in class.

Week Topic Ch. Due Lab Due Quiz
8/21-8/25 Welcome to College/Course
8/28-9/1 Welcome to Statistics: A Modeling Approach Ch 1 1A and 1B Quiz 1
9/4-9/8 Labor Day/Understanding Data Ch 2 2A Quiz 2
9/11-9/15 Examining Distributions Ch 3 3A and 3B Quiz 3
9/18-9/22 Explaining Variation Ch 4 4A and 4B Quiz 4
9/25-9/29 Exam #1/ A Simple Model
10/2-10/6 A Simple Model Ch 5 5A and 5B Quiz 5
10/9-10/13 Quantifying Error Ch 6 6A and 6B Quiz 6
10/16-10/20 Adding an Explanatory Variable to the Model Ch 7 7A and 7B Quiz 7
10/23-10/27 Digging Deeper into Group Model Ch 8 8A Quiz 8
10/30-11/3 Models with a Quantitative Explanatory Variable Ch 9 9A and 9B Quiz 9
11/6-11/10 Exam #2
11/13-11/17 Logic of Inference Ch 10 10A and 10B Quiz 10
11/20-11/24 Model Comparison with F Ch 11 11A Quiz 11
11/27-12/1 Parameter Estimation and Confidence Intervals Ch 12 12A and 12B Quiz 12
12/4-12/8 Exam #3 Ch 13

University Policies

  1. Academic Honesty:

    Please conduct yourself with honesty and integrity. Do not submit others’ work as your own. For assignments and quizzes that allow you to work with a group, only put your name on what the group submits if you genuinely contributed to the work. Work completely independently on exams, using only the materials that are indicated as allowed. Failure to observe academic honesty results in substantial penalties that can include failing the course.

  2. Disabilities:

    If you are a student with a disability requesting reasonable accommodations in this course, you need to contact Disability Accommodations and Support Services (DASS) located on the second floor of Arroyo Hall, via email accommodations@csuci.edu or call 805-437-3331. All requests for reasonable accommodations require registration with DASS in advance of need: https://www.csuci.edu/dass/students/apply-for-services.htm. Faculty, students and DASS will work together regarding classroom accommodations. You are encouraged to discuss approved.

  3. Emergency Procedure Notice to Students:

    CSUCI is following guidelines and public orders from the California Department of Public Health and Ventura County Public Health for the COVID-19 pandemic as it pertains to CSUCI students, employees and visitors on the campus. Students are expected to adhere to all health and safety requirements as noted on the University’s Spring 2023 Semester website or they may be subject to removal from the classroom.

Important note about a possible work stoppage during the semester

The California Faculty Association (the labor union of Lecturers, Professors, Coaches, Counselors, and Librarians across the 23 CSU campuses) is in a difficult contract dispute with California State University management. It is possible that we will call a strike or other work stoppage this term. I promise to promptly inform you of any schedule disruption. Our working conditions are your learning conditions; we seek to protect both. For further information go to www.CFAbargaining.org.