Krista can be tough! She goes through all the stats theory properly and equips you well for anything graded. With that said, she can be tough and stern with expectations of the class, so make sure to follow up to her standard and you'll be fine.

#tCFF23 This class is pretty easy. Like some other STAT classes, Prof. Varanyak drops the lowest grade in almost every category so you can take the L quite a few times and still end up with an A. There's a "lab" for every unit, that's more like an extended version of the classroom and just tests your ability to actually replicate the stuff she goes over in class. The material is really useful, if a bit dry, and the lectures aren't the most engaging... but honestly Krista is doing her best with what she has. Linear regression just be like that. No exams, just a big project that's due in two chunks plus a presentation with your poster, oh and it's a group project btw. She grades very nicely, though the TAs do vary a bit in their niceness. Pretty lowkey class all in all.

EASY CLASS TO GET AN A BUT HOLD ON FOR THE RIDE OF A TERRIBLE PROFESSOR, ESPECIALLY IF YOU NEED TO REACH HER!!! -----> Read below to see how bad she is: I am currently a fourth year and in all of my semesters at UVA, I have never had a worse professor than Krista. One the first day of classes she urged us not to email her as she would not be checking her emails and instead ask the TAs or ask our classmates via Piazza. I assume that the thousands of dollars spent on my education are not enough for her to even care about my questions. If that were not unprofessional enough, she wears sweatshirts and leggings to class. To make matters worse, I emailed her about a personal matter that required further attention and not a matter for the TAs or Piazza. After about a week, my Dean got involved, we finally met via Zoom and remedied the situation. However, once my situation worsened and I emailed her about it, she did not respond for a week. I then followed up and waited another 3 days before a response. Her response asked about setting up a meeting where she asked for my availability that week... despite responding within two hours on that Monday, I did not get a response until the following Tuesday. This response was a meeting later in the week that she ended up 'ghosting' me at and never showing up for our zoom call. Eventually, we met in person, where I had to defend myself for needing more time for a family member passing away rather than her compassion and understanding during these troubling times. Inside of the classroom, she is genuinely a questionable professor at best. Having taken an engineering mathematics course prior using R and in statistics, I thankfully had a handful of background knowledge on the topic. However, she fails to discuss the meaning of the p-values, alpha thresholds, and all of the other values we get from our code. Instead, she finds it rather of extreme importance that we are able to "Knit" our documents into a pdf so it is aesthetically pleasing. This shows absolutely no knowledge of the content and rather harps on can you copy, paste, and edit code correctly from Classwork to Homework. In class, we sit in groups and strictly conduct groupwork as if we are in high school. Lecture teaches absolutely nothing other than talking to the groupmates around you and maybe her taking attendance that day. I do not believe she is fit to be a professor at this prestigious university at all. She would however make a wonderful subpar high school teacher in a district desperate for faculty members that have a PhD in Education and not the actual topic of Statistics.

Honestly, Professor V isn't that bad of a professor. She isn't exceptional, but can explain the content decently well. I mostly self-learned the content from her slides and occasionally looked at the textbook. make sure you know how to use R in this class, either by learning some R beforehand (or in a previous class) or asking help from the TAs. R is heavily used for most things in this class. There are no exams, only 4 in person labs (basically similar to the classworks) which involve R. Regression analysis is quite interesting, and isn't too difficult if you've encountered it before (linear regression, MLR, logistic, ANOVA). There are also nearly weekly quizzes which are hit or miss in difficulty, some easy, some worded confusingly. Then there's a semester-long group project which is pretty fun and worth a solid amount of your grade. It isn't too hard to get an A-/A, although at the beginning of the semester, an A = 95 (which was reduced to a 94).

I enjoyed this class although the class is not set up for everyone. It's pretty easy to get an A as long as you do the work. The majority of the grade comes from the final project. This project takes a lot of time, and it can also be frustrating if you don't have someone in your group who is good with R. However, it's pretty easy to get a good grade on it if you follow the rubric, just takes time. The hardest part of this class comes from the labs, but if you study the classwork questions beforehand, then you'll be fine. She also drops 2 quizzes, 1 lab, and gives some extra credit opportunities which really helps the grade. As a professor, I like her but I know my friends did not enjoy her. It is frustrating that during class she just reads from her notes that you can read, but she is very good at answering questions, and she is always very nice. However, I accidentally forgot to submit a classwork one time, and she refused to accept it at all, which was frustrating. All in all, not a bad class definitely recommend for stats. #tCFF23

I am very torn about this class. On one hand, if you do the work and follow instructions you are almost guaranteed an A. There are no tests and Krista has a very generous drop policy for the short quizzes. But, this class is not taught well at all. Krista does reverse classroom where she makes you watch a 30 min video before each class, and then during class you just do problems with your classmates. There's really no reason to actually go to lecture, and I stopped after the 2nd week. My bigger complain is that this class is based on the programming language SAS. SAS is a totally out of date, obscure language that is basically never used in the real world. Krista never teaches it to you, and instead tells you to go take an online tutorial and figure it out yourself. I see absolutely no reason why this class wouldn't be taught in R or Python (both significantly more popular) except that Krista doesn't know these languages and refuses to learn them. This combined with Krista's horrible email response rate along and inflexibility with meeting with students leads me to believe she is not a highly motivated professor. TLDR; very easy A- class, don't need to go to lecture. You will learn a little about statistics and a programming language 99.9% will never use again. All things considered not a bad class, just taught in a very weird way.

Recorded videos and in person lecture were really important to my learning in this class. I would watch the recorded videos and take notes, and this was helpful because the videos had built in learning check points that I would gain even more information from. The videos also worked through some examples that were similar to what we would see in classwork. In class lecture would summarize what I learned from the videos and Professor Varanyak would then go through the code in SAS. These two activities, rather than the class textbook, were how I completed most of my learning. Labs, quizzes, and classwork assignments are the bulk of the class with a final project. For the project, you work in a group of 3-4 people and have to come up with a research question and find a data set to design a model for linear or logistic regression. Classwork and examples from the class are more than enough to prepare for this assignment, but you can go to office hours to get feedback on it while you work on it. Definitely start early with this project! #tCFfall22

I don't understand the criticism for this course and instructor. This class is almost a guaranteed A if you just do the assignments and work as it's laid out. Yes, the organization of the course was sometimes lacking, and, yes, she does grade somewhat critically without much feedback on the final project. However, I felt that she was very clear in her expectations communicated via the rubrics (and they were pretty high), and if you needed clarification as to why points were lost, she was readily available to explain during office hours. I think people just blew this class and a lot of the assignments off as easy A's and didn't take her rubrics/expectations seriously. Really no fault of hers. Granted, the whole course is pretty easy and low maintenance up until the final project, which is much more in-depth and intense than any of the other coursework. However, I think the project was a very fair demonstration of everything that we learned. That being said, it's incredibly important to choose your project group carefully as you will most likely be working with them for the better part of the semester. Some people hate SAS, but it's SO easy to use and most (basically all) of the coding can be done by directly referencing or copying and editing code that she literally gives you. Learning and actually having to write your own code in SAS is a very small fraction of the coursework.

This class doesn't involve a lot of work in my opinion and the stuff you learn is very useful and most likely applicable to your career if you're pursuing a statistics related one. There are no tests which is pretty great. However, Krista is a very harsh grader which ended up docking a lot of points for the final project. Speaking of that, make sure you pick good partners for that as it can really make or break your grade. Don't forget to do the weekly quizzes like I did a couple time because gradebook unfortunately doesn't send reminders. Also the grading scale is so dumb and makes no sense why a 93 is not an A.

Understanding the concepts of regression is absolutely crucial to statistics, and if taught well, can very effectively be used as a "bridge" to connect introductory statistics courses to higher-level "machine learning" ideas (e.g. the sigmoid function used in logistic regression is also a common neural network activation function). The idea of a hands-on regression course with plenty of practice with linear, multiple linear, and logistic regression is very sound, and when I was actually doing the coursework for this class, I found it to be extremely relevant and useful to future statistics courses I may take. The in-depth exploration of the regression assumptions in STAT 3220 stood out as one of the course highlights — understanding concepts like homo- vs heteroscedasticity & interpreting Q-Q plots is VERY handy when actually applying regression techniques in the field. I am very glad that UVA offers this class & think the overall material is incredibly valuable. That being said... the actual experience of this class was very frustrating, for a few reasons: 1. The entire course is taught using SAS, a dinosaur of a programming language that is almost totally obsolete outside of the field of biostatistics and is a total pain to learn. It's so convoluted that the instructor required us to complete a 20-hour course on the basics of the programming language and beyond the basic proc... and data... syntax I'd be hard-pressed to pass any sort of programming exam on the language. It's inflexible, doesn't really support functional or OO programming, and has horrible documentation. I legitimately cannot imagine the justification for teaching a statistics course in SAS instead of R or Python in the year 2022, especially because SAS is not open source and charges an excessive amount of $$ for access to an online web editor (that's right, you can't even program in SAS on your actual computer, you have to use an unresponsive and buggy code editor in the browser). 2. The feedback on assignments ranges from "pretty useless" to "totally nonexistent." Points are taken off arbitrarily on labs and even MORE arbitrarily on the final project, which — despite having multiple full class days devoted to it — was never really clearly defined. Some cautions on the labs: while during the lectures and homeworks, the professor emphasizes the arbitrariness of the regression model building process and the lack of black-and-white answers in variable selection (the "garden of forking paths" is a familiar concept to students with a statistics background), do NOT deviate from whatever the graders have on their answer key — no matter how far you go to try and explain your reasoning, it won't be far enough! If you're worried you don't understand the content covered in video lectures, some copy-pasting of the HW code + changing of numbers is good enough to earn As on the labs.