Course Description¶
Stat 131A is an upper-division course that follows Data 8 or STAT 20. The course will teach a broad range of statistical methods that are used to solve data problems, including group comparisons, standard parametric statistical models, multivariate data visualization, multiple linear regression and classification, classification and regression trees, and random forests. Students will be introduced to the widely used R statistical language and they will obtain hands-on experience in implementing a range of statistical methods on numerous real world datasets.
Basically this course covers the fundamental concepts, practical “hammers”, and accompanying implementation skills that will inevitably surface in any real-world data science project and for any domain.
Topics and Semester Schedule¶
[The section references at the end of each lecture are to sections in the Purdom textbook]
Week 1: The First Lecture ! (Introduction)
Week 2: Central Tendencies: Mean, Median, Mode | Visualization: The Boxplot (2.1)
Week 3: Probabilistic Distributions (2.2, 2.3)
Week 4: Kernel Density Estimation | The Central Limit Theorem (2.4, 2.5)
Week 5: Hypothesis Testing I & II (3.1-3.6)
Week 6: Hypothesis Testing III & IV (3.6-3.10)
Week 7: Hypothesis Testing V | Linear Regression - Introduction (3.10, 4.1)
Week 8: Linear Regression - Least squares | Linear Regression - Local fitting (4.1-4.4)
Week 9: Multiple Regression | Revision & Discussion for MIDTERM Exam (6.1)
Week 10: 10/28 - NO Lecture | 10/30 - MIDTERM Exam (from Week 2, up to and including Linear Regression - Least squares: Lectures 4-13)
------ POST MIDTERM & THROUGH END of COURSE -------
Nov 6: Multiple Linear Regression II
Nov 13: Final Project Overview & Discussion
Nov 18: Logistic Regression I (this will be a RECORDED lecture/NO in-person class)
Nov 20: Logistic Regression II (this will be a RECORDED lecture/NO in-person class)
Nov 25 & 27: NO CLASS (Please work on the Final Project !)
Dec 2: Final Project Mid-flight Discussion | Decision Trees (Benign Introduction)
Dec 4: STAT131A Full Course Recap - Our Takeaways
Dec 9: Final Exam Description & Discussion
Dec 11: Final Exam prep questions (online)
Dec 17: FINAL EXAM
Grades¶
Late Policy¶
There will be weekly lab assignments throughout the semester to help students learn to implement the concepts from lecture in R code. These will generally be assigned Tuesday mornings and due Sunday nights, and will be graded for correctness. If you miss the assignment deadline, you will have 24 hours to turn your work in late for partial credit; late assignments will receive a 30% deduction. Extensions can be granted under extenuating circumstances--please reach out to course staff to request an extension.
Lab Drop Policy¶
Each student will have 1 lab dropped automatically--the lab with the lowest score will not count toward your final grade.
Academic Honesty Policy¶
Homework and projects must be completed independently, with the following exceptions:
You may discuss specific issues/questions you have about the homework at a high level, but you must not sit down and do the assignment jointly.
Giving advice about code or coding tips is also not cheating, but you cannot directly share code with other classmates.
For exams, cheating includes, but is not limited to, using electronic materials in an exam beyond that allowed, copying off another person’s exam or quiz, allowing someone to copy off of your exam or quiz, and having someone take an exam or quiz for you.
Requesting, obtaining, and/or using solutions from previous years or from the internet or other sources, if such happen to be available, is considered cheating. In fairness to students who put in an honest effort, cheaters will be harshly treated.
Any evidence of cheating will result in a score of zero (0) on the entire assignment or examination, and perhaps a failing grade in the class.
We will always report incidences of cheating to the Office of Student Conduct, which may administer additional punishment.