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DS105-Syllabus.md

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DS105 Intermediate Statistics

Course Description: The Intermediate Statistics course is designed to teach students about hypothesis testing under multiple scenarios. Students will be able to determine which hypothesis test to utilize and then be able to perform that specific test. Students will also learn to identify and verify the data requirements for each hypothesis test.
Quarter Credit Hours: 4.5
Course Length: 60 hours
Prerequisites: DS101, DS102, DS108, DS109
Proficiency Exam: No
Theory Hours: 30
Laboratory Hours: 30
Externship Hours: 0
Outside Hours: 15
Total Contact Hours: 60

Required Resources:

  • Ground-based students are required to bring a late model laptop computer (either PC or MacBook) to class every day.

  • Online students are required to have a late model laptop or desktop computer with internet access.

  • Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.

  • Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.

  • Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.

  • It is a requirement that you are able to download programming resources to your laptop/desktop for this class. (This means you need a steady internet high bandwidth connection.)

  • You are required to have a quiet place to study and to be able to focus on the material.

  • You are required to have uninterrupted weekly 1:1 video meetings with your mentor.

  • You are required to log into the Learning Management System (LMS) daily for at least 20 minutes.

  • Please follow and review each lesson page by page coding examples provided as this will ensure you have a full understanding for your final hands-on assignments.

Course Outline:

Module Lesson Number Lesson Name
DS105 Intermediate Statistics 1 Basic Statistics in Python
2 When Data isn’t Normal
3 Advanced Chi-Squares
4 Basic ANOVAs
5 Repeated Measures ANOVAs
6 Mixed Measures ANOVAs
7 ANCOVAs
8 MANOVAs
9 Power Analysis
10 Final Project

Educational Objectives:

Upon successful completion of this course, students will be able to:

  1. Understand Type I and Type II errors
  2. Use one- and two-sample z-test and t-test
  3. Use the ANOVA and MANOVA tests
  4. Use one and two proportion testing
  5. Use Chi-square test for dependence
  6. Determine which test to utilize

Points Distribution:

L1 Practice Hands On – 0 points L1 Practice Hands On – 0 points L1 Practice Hands On – 0 points L1 Practice Hands On – 0 points L1 Practice Hands On – 0 points L1 Hands On – 45 points – Analyze Data L2 Practice Hands On – 0 points L2 Practice Hands On – 0 points L2 Hands On – 45 points – Transform Data in both R and Python L3 Practice Hands On – 0 points L3 Practice Hands On – 0 points L3 Hands On – 45 points – Analyze avocado prices and sales by location in both Python and R L4 Practice Hands On – 0 points L4 Practice Hands On – 0 points L4 Practice Hands On – 0 points L4 Hands On – 0 points L5 Hands On – 45 points – Perform a MANOVA in R

Exam Points Activity
L1 0 Practice Hands On * 5
L1 45 Hands On Analyze Data
L2 0 Practice Hands On * 2
L2 45 Hands On - Transform Data R and Python
L3 0 Practice Hands On * 2
L3 45 Hands On - Analyze avocado prices and sales by location in both Python and R
L4 0 Practice Hands On * 3
L4 0 Hands On
L5 45 Hands On - Perform a MANOVA in R
L6 ? still counting the assignments
L7 ? ?
L8 ? ?
L9 ? ?
L10 ? Final Project

Points Total:

Participation points: 20 (5%) L1-9 Hands On total points: 180 (45%) Final Project points: 200 (50%) Total points: 400

Type Points
Professionalism, Attendance and Class Participation*: 20 points (5%)
Assignment/Hands-On/Homework: 180 points (45%)
Projects/Competencies/Research: 200 points: (50%)
Total points: 400 (100%)

Final Project:

Conduct hypothesis tests on the data supplied. For each test: explain the research taking place and include any graphs that make sense, state the null and alternative hypotheses and include the value of the test statistic and the p-value, and complete the analysis by stating the conclusions in terms of the problem presented (not in mathematical terms). Present results in a manner that encourages understanding of the completed processes by people without any statistical background.