Note: The final letter grades will be based on the curve of students' performace.
| Grade | Score |
| A | 93.0% -100% |
| A- | 90.9%-92.9 |
| B+ | 87.0%- 89.9 |
| B | 83%-86.9% |
| B- | 80.0%-82.9% |
| C+ | 77.0%-79.9% |
| C | 73.0% -76.9% |
| C- | 70.0%-72.9% |
| D+ | 67.0%-69.9% |
| D | 63.0%-66.9% |
| D- | 60.0%-65.9 |
| F | 59.9% and below |
Note that we use Canvas as a tool for grading and it is a relative measure of your grade, however, it is not the only consideration we use to grade.
With that said, here are the key points to note regarding class collaboration and submitting original work:
If you are unsure if you need assistance, free online mental health screenings can be found at: https://screening.mentalhealthscreening.org/UCCS.
If you are experiencing a mental health emergency (i.e., you do not feel physically safe):
Remember, we care about your wellbeing, so if you are struggling (even if this is NOT an emergency) please reach out for help.
For confidential mental health services, visit the Wellness Center located inside the Gallogly Recreation and Wellness Center. We welcome and encourage students to contact the following on-campus services for assistance regarding their physical, mental, and fitness needs:
If you are struggling with your sense of belonging and/or diversity, equity, and inclusion challenges on campus, please consider reaching out to MOSAIC and the LGBTQ+Resource Center.
| Date | # | Lecture Topic | Assignment | Lecture Notes and Textbook Chapter Reading | Extra Reading | |
|---|---|---|---|---|---|---|
| Out | Due | |||||
| Fri, Jan 19 | 1 | Course Overview and Introduction to Statistics for Data Analytics (R, Rstudio, R Packages) | [PPT] & Chapter 1 | Giorgi et al., 2022: Read Section 2 for History of the R Programming Language | ||
| Fri, Jan 26 | X | |
||||
| Fri, Feb 02 | 2 | Data Structure in R (I) & Data Visualization | HW01 | [PPT] & Chapter 2 - 2.5 | PS02_pre_lecture on ggplots | |
| Fri, Feb 09 | 3 | Data Structure in R (II) & Data Visualization (cont'd) | HW01 | [PPT] & Chapter 2.6 - 2.9 | booksales.csv data , salaries.csv, Other Salaries file type | |
| Fri, Feb 16 | 4 | See make up Video Recording |
HW02 | [PPT] & Chapter 3 - 3.5 & 4.4 | Wide data.csv , Lecture 4 Discussion Slides | |
| Fri, Feb 23 | 5 | Summary Statistics & Regression I: Regression with one Numerical Variable | HW02 | [PPT] & Chapter 5 - 5.1.2 | Lecture 5 Rdata | |
| Fri, Mar 01 | 6 | Regression I (cont'd) | HW03 | [PPT] & Chapter 5.3.1, 5.1.3 & 5.3.2 | Section 5.2 | |
| Fri, Mar 08 | 7 | Sampling Distribution & Regression II: Regression with one Categorical Variable | HW03 | [PPT] & Chapter 5.2 - 5.2.3 | Chapter 7 for sampling distribution | |
| Fri, Mar 15 | X | CP Proposal | Proposal Format Template | |||
| Fri, Mar 15 | X | |||||
| Fri, Mar 22 | 8 & 9 | Multiple Regression & Confidence intervals and the bootstrap | [PPT] & Chapter 6 [PPT] & Chapter 8 |
|||
| Fri, Mar 29 | X | Spring Break: No class | CP (Mon: 4/3) |
CP Proposal (Mon: 4/3) |
||
| Fri, Apr 05 | 10 | Midterm Exam | ||||
| Fri, Apr 12 |
11 | Hypothesis Testing I | HW04 | [PPT] | ||
| Fri, Apr 19 | 12 | Hypothesis Testing II (z-test & t-test) | HW04 | [PPT] & Chapter 9 - 9.2 | Hypothesis Testing Rdata, standard normal table, t distribution table | |
| Fri, Apr 26 | 13 | Hypothesis Testing III (Types of t-tests) | [PPT] & Chapter 9.2 - 9.3 | Table of the chi square distribution , ChiSquare Example_Rdata | ||
| Fri, May 03 | 14 | Hypothesis Testing IV (Chi-Square tests) & Hypothesis Testing V (ANOVA Test) & Inference from Regression | CP | [PPT] & Chapter 9.4 - 9.6 | ||
| Fri, May 09 | X | Finals Week | ||||
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