Spring Semester 2022 Room HBH 254; 1:00pm - 1:50pm

**Instructor:** Dr. Patrick Hartigan, Hermann Brown Rm. 350 , Phone: X2245

**Office Hrs:** After class and by appt.

**Texts:**

*Modern Statistical Methods for Astronomy (with R applications)*by E. Feigelson and G. Babu

This will be our main text, and students should own a copy, and bring it to class.*REA's Statistics Problem Solver*A quite nice set of worked-out examples for various statistical tests that may be useful to you as a reference.

- Research Journals in Physics and Astronomy

https://ui.adsabs.harvard.edu/ is a good source for the astronomy journals - There are many statistics reference books listed at the end of each chapter in the Feigelson and Babu book

**Scheduling:** Several Wednesday classes meet during the Rice Faculty Senate meetings, and as a Senator I
should attend these meetings. We will discuss options for these classes when we meet the first day of class.

**Grading:** Based on short presentations (30%), homework creation, grading, and
leading the discussion for the problems (25%), long presentations (25%), completing
homework problems (10%), and preparation (10%). The preparation portion is based on whether
or not the student has read the material beforehand and has otherwise prepared properly for class.
The class format only functions well if everyone has their responsibilites
completed on time. If a homework set is not created or a long presentation is not done
the student will receive a 0 for that grade.
The problem-creation+long-presentation
grade is 50% of the total, and there are only one or two such events for each student during the
semester, so it is important to have these ready on time. A student who
otherwise gets straight A's on all other work who does not have a long presentation
or homework-creation ready when it is scheduled will drop a full letter grade to a B.

**Course Version:** The course is also offered for graduate credit as *ASTR 508*. The
course content is the same, but a larger fraction of the presentations are required of the
graduate students.

**Curriculum Fit:** This class is one of the options for 4xx courses required for the
B.S. Astrophysics degree, and also will count towards a minor in ASTR.
ASTR 408 is not currently within the data sciences rubric (major or minor).
However, the DSCI curriculum is under development, so interested students should meet with faculty in
charge of those degrees to determine whether or not ASTR 408 may substitute for a DSCI requirement.

**Prereqs:** There are no statistical prerequisites. We will cover material as we go.

In this class we will identify the statistical methods commonly used by research papers in physics and astrophysics. We will begin study of each method by having each student present a short summary of a paper they have found that uses the method. The subject area of the paper is completely up to the student. In the following class there will be a lecture on the mathematics of the method, and in the next class we will hear from students who have chosen this method for deeper study as they dive into their paper in depth. To solidify this knowledge, students will construct problems that will be distributed to the remainder of the class, and after a period of time we will discuss the solutions. A final lecture on the topic follows where we go over homework problems for the previous topic and discuss any outstanding questions about the material covered in the book and the methods we saw in the papers. Then we will move on to a new topic. By learning this way, we'll see how statistics is used in different areas. This experience broadens our toolset for use in our own studies.

The topics will be guided by the professor, but the papers are chosen by the students. Topic examples include hypothesis testing and confidence intervals, extracting periods and signals from time-series data, using principal component analysis, maximum likelihood methods, finding groups and clusters in multidimensional data, maximum entropy reconstructions, non-parametric ranks, optimized profile extractions, Bayesian analysis with priors and so on. Most statistical methods used in astronomy and physics will fall under one of the broad topics in this course, but if you have seen a technique and wondered how it works, we can substitute it for one of the other topics if there is interest. Find a paper that uses it and we will study it.

As described above, in class we will, (*) find a broad range of papers chosen by the students that use a particular method, (*) study the mathematics of the method, (*) go in-depth into two papers chosen by the students, and (*) work through some problems to understand how the method works in practice. The students will pick the papers, and will be expected to lead the class through an overview of the science objectives. The main focus, however, will be on the method, which we will then all study together to try to understand. The problem sets may apply the method to some other case, perhaps with contrived data, so we are all sure we could use it if the need arose in another context. There will be a few lectures at the beginning and then interspersed as needed throughout the semester to provide some mathematical structure, and we will follow the overall format of the textbook. While the textbook examples tend to come from astronomy, the student-chosen papers need not. In the past students have chosen papers from the fields of biology, geology, economics, linguistics, social sciences, oceanography, medicine, and political science. All of these are acceptable. In this class the application is less important than the method.

Analysis for this class is done in the R programming language. The book contains many examples of R code. R is a free package, and has convenient structure for loading and manipulating large data sets.

**Tyipcal Work Load, Absence and Late Policies: **

- Every 1.5 weeks: ~ 8-minute presentation to class on an article that uses the current technique we are studying
- Every 1.5 weeks: Complete a homework problem devised in part by a classmate
- Once or twice during the semester: A 50-minute presentation to the class that goes through the details of a paper, re-analyzing data or some subset of it if possible.
- Once or twice during the semester: Work with prof to create a homework problem (and solutions!) for the technique we are using. Check answers for classmates (grades actually assigned by the prof)
- Absences: The class relies heavily on presentations and on homework problems that the students create and grade so these must be ready when needed. This is especially true for long presentations. Makeups may not be possible given the schedule. Homeworks must be turned in on time to receive credit, because we discuss them on the date they are due. Refer to the grading policy section for more information.

(This section is now required by the Rice Administration for all Syllabi)

Students completing this class should be able to do the following:

- Understand the fundamentals of statistics, including probability distributions, means, variances, the Central Limit Theorem, hypothesis testing, error propagation, Bayesian analysis, jacknife, and bootstrap
- Understand modern statistical methods that relate to curve fitting, hypothesis testing, cluster analysis, principal component analysis, and time-series data
- Assess the veracity of statistical conclusions drawn from any observations or data in the natural sciences, social sciences, or humanities (subject areas chosen by the students)
- Evaluate whether or not the best statistical methods were used to analyze data in papers published in recent refereed journals
- Apply the statistical concepts covered in class to their own sets of data using, in part, the statistics software package R
- Create homework problems that relate to one of the mathematical techniques studied in class
- Improve speaking and presentation skills while leading class discussions
- Learn to summarize complex papers in a fixed amount of time

Students will be assigned to specific sections in '[ ]' the first week of class

DATE | Topic | Class Type | Subject and Items Due |

M Jan 10 | Mathematical Foundation | Lecture | Probability Concepts; CHAPTER 2 |

W Jan 12 | " | Lecture | Probability Concepts; CHAPTER 2 |

F Jan 14 | " | Lecture | Probability Distributions; CHAPTER 4 |

M Jan 17 | Holiday | --- | --- |

W Jan 19 | " | Lecture | Confidence Intervals and Hypothesis Testing; CHAPTER 3 |

F Jan 21 | Nonparametrics | Paper Discussions | Short Presentations [Everyone] |

Math HMWK handout [pmh] |
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M Jan 24 | " | Lecture | Nonparametrics; CHAPTER 5 |

W Jan 26 [5:15pm] | " | Lecture | Nonparametrics; CHAPTER 5 |

F Jan 28 | " | Long Paper | Long Presentation [Mingjian] |

M Jan 31 | Regression | Paper Discussions | Short Presentations [Everyone] |

Nonparametrics HMWK handout [Kyle] |
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Math HMWK due [Everyone] |
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W Feb 2 | " | Lecture | Regression; CHAPTER 7 |

F Feb 4 | " | Lecture | Regression; CHAPTER 7 |

M Feb 7 | " | Long Paper | Long presentation [Brandon] |

W Feb 9 | Data Smoothing | Paper Discussions | Short Presentations [Everyone] |

Regression HMWK handout [Mingjian] |
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Nonparametric HMWK due [Everyone but Kyle] |
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F Feb 11 | Recess, NO CLASS | --- | --- |

M Feb 14 | " | Lecture | Smoothing; CHAPTER 6 |

W Feb 16 | " | Lecture | Smoothing; CHAPTER 6 |

F Feb 18 | " | Long Paper | Long presentation [Kyle] |

M Feb 21 | Multivariate Analysis/PCA | Paper Discussions | Short Presentations [Everyone] |

Data Smoothing HMWK handout [Yucheng] |
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Regression HMWK due [Everyone but Mingjian] |
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W Feb 23 [5:15pm] | " | Lecture | Multivariate Analysis; CHAPTER 8 |

F Feb 25 | " | Lecture | Multivariate Analysis; CHAPTER 8 |

M Feb 28 | " | Long Paper | Long presentation [Yucheng] |

W Mar 2 | Time Series Analysis | Paper Discussions | Short Presentations [Everyone] |

F Mar 4 | " | Lecture | Time Series; CHAPTER 11 |

Data Smoothing HMWK due [Everyone but Yucheng] |
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Multivariate/PCA HMWK handout [Brandon] |
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M Mar 7 | " | Lecture | Time Series; CHAPTER 11 |

W Mar 9 | " | Long Paper | Long presentation [Preston] |

F Mar 11 | Clustering | Lecture | Clustering; CHAPTER 9 |

M Mar 14 | Spring Break | --- | --- |

W Mar 16 | " | --- | --- |

F Mar 18 | " | --- | --- |

M Mar 21 | " | Paper Discussions | Short Presentations [Everyone] |

Time Series Analysis HMWK handout [Brandon] |
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Multivariate/PCA HMWK due [Everyone but Brandon] |
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W Mar 23 [5:15pm] | " | Lecture | Clustering; CHAPTER 9 |

F Mar 25 | " | Long Paper | Long presentation [Mingjian] |

M Mar 28 | Truncated Data | Paper Discussions | Short Presentations [Everyone] |

Clustering HMWK handout [Kyle] |
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Time Series HMWK due [Everyone but Brandon] |
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W Mar 30 | " | Lecture | Truncated Data; CHAPTER 10 |

F Apr 1 | " | Lecture | Truncated Data; CHAPTER 10 |

M Apr 4 | " | Long Paper | Long presentation [Preston] |

W Apr 6 | Spatial Point Processes | Paper Discussions | Short Presentations [Everyone] |

Truncated Data HMWK handout [Yucheng] |
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Clustering HMWK due [Everyone but Kyle] |
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F Apr 8 | " | Lecture | Spatial Processes; CHAPTER 12 |

M Apr 11 | " | Lecture | Spatial Processes; CHAPTER 12 |

W Apr 13 | " | Long Paper | Long presentation [Yucheng] |

F Apr 15 | TBD... Gaussian Processes? | Paper Discussions | Short Presentations [Everyone] |

Truncated Data HMWK due [Everyone but Yucheng] |
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Spatial Processes HMWK handout [Preston] |
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M Apr 18 | " | Lecture | Gaussian Processes; CHAPTER 12 |

W Apr 20 [5:15pm] | " | Lecture | Gaussian Processes; CHAPTER 12 |

F Apr 22 | " | Long Paper | Long presentation [Kyle] |

Spatial Processes HMWK due [Everyone but Preston] |
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W May 4 | Course Summary due [Everyone] |
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**Honor Code: **
A general description of the honor code is avilable on-line.
Students should turn in their own work and analysis on the homework sets, but may discuss the general nature
of the problems with one-another.

**Disability Accommodation: **
If you have a documented disability that will impact your
work in this class, please contact me to discuss your needs.
Additionally, you will need to register with the Disability
Support Services Office in the Ley Student Center.