Probability and Statistics I
Education, Teaching, Introductory statistics ·Welcome to the course page for Probability and Statistics I, a course that I am teaching in Spring 2026.
Course Information
- Course: Probability and Statistics I (CC1105) - Theory-Laboratory - 10
- Credits: 4
Schedule
- Monday (in person): 11:00 AM - 2:00 PM, M604 (6th floor)
- Friday (virtual): 6:00 PM - 8:00 PM
Instructor Information
- Instructor: Sashwat Tanay
- Email: stanay@utec.edu.pe
- Office: P309 (third floor)
- Office hours: By appointment. Book appointment here
Materials
- Lecture Notes
- OpenIntro Statistics, Fourth Edition — David Diez, Mine Cetinkaya-Rundel, Christopher D. Barr (freely available here)
- Advanced Engineering Mathematics, 10th Edition — Erwin Kreyszig
- Mathematical Methods for Physics and Engineering, 3rd Edition - Riley, Hobson and Bence
- Probability & Statistics for Engineers & Scientists, 9th Edition - Walpole, Myers, Myers, Ye
- Required software: R and RStudio
Homework 1
Homework 1 is posted here. Due May 4th, 7 am (to be submitted via Gradescope)
Quiz 1
We will have the first quiz in class (with pen and paper) on May 4th.
Assessment and Grading
| Component | Weight | Due |
|---|---|---|
| Homework 1 | 5% | Week 6 |
| Quiz 1 | 5% | Week 7 |
| Partial Exam | 25% | Week 8 |
| Quiz 2 | 5% | Week 13 |
| Homework 2 | 5% | Week 14 |
| Project | 30% | Week 15 |
| Final Exam | 25% | Week 16 |
Course Content
1. Descriptive Statistics
- Classification and types of data
- Graphical representation of data (e.g., histograms, bar charts, scatter plots)
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation, range)
- Interpretation of relationships: correlation vs. causation
2. Probability
- Discrete probability distributions
- Venn diagrams
- Permutations and combinations
- Joint, marginal, and conditional probabilities
- Independent events
- Addition and multiplication rules for mutually exclusive and independent events
- Law of total probability
- Bayes’ theorem
- Special discrete random variables: binomial and geometric distributions
- Introduction to continuous distributions: normal distribution
3. Inferential Statistics
- Point estimation
- Sampling distributions and the Central Limit Theorem
- Confidence intervals
- Hypothesis testing