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A calculus-based introduction to probability and descriptive and inferential statistics. Topics include numerical and graphical summaries of data, conditional probability, Bernoulli trials, normal distribution, the central limit theorem, estimation, t-tests, chi-square tests, type I and II errors, regression and correlation.
This course intends to teach students to identify patterns in data using visualizations and statistical methods. It will focus on building foundational understanding of fundamental statistical techniques, proper interpretation of results, and clear communication of these ideas in written and oral formats.
Specific learning outcomes, with respect ot the core curriculum at Xavier, include:
In the modern era, statisticians (along with most scientific professionals) rely upon software. In this course you will be required to use R (typically through the R Studio GUI). R is an open-source software that is generally regarded as the industry standard amongst professional statisticians. Throughout the course you will work through guided labs which will simultaneously promote proficiency in R programming and applied statistical analysis.
You can install R and R Studio on your personal computer in two easy steps:
R from http://www.r-project.org/R Studio user interface from http://www.rstudio.com/Note that R Studio is also available on some campus computers.
Due the COVID-19 pandemic our classroom capacity is limited to 10 students at a time. This will require the course to follow a hybrid format where half of the enrolled students attend class in-person and the remaining half attend virtually via Zoom on a typical day.
You will receive communication via email regarding which days you will be expected to attend in-person. Please do not attend any class meetings that you are not assigned as there are limited available seats.
In accordance with the values of Xavier University, students are expected to adhere to the following policies:
Students are expected to attend all scheduled lectures assigned to them. The instructor reserves the right to lower final course grades in response to repeated, unexcused absences. Good attendance means more than simply showing up to class, please practice proper etiquette including not being late, refraining from cell-phone use during class, respecting your classmates, and actively participating in class activities to your fullest capacity.
Students will be required to sign an Honor Pledge on certain assignments: “As a student at Xavier University, I have neither given nor received unauthorized aid on this assignment/exam. (Student signature)”. Assignments and exams will explicitly describe what constitutes authorized versus unauthorized aid.
In general, collaborate work is highly encouraged in this course; however, your homework write-ups should be your own individual work. Copying solutions from anyone or any source without full disclosure is considered cheating. If you received help, ideas, or inspiration from anywhere (classmates, online resources, etc.) you should indicate this on your assignment (a simple statement like “I worked with Jack and Jill on problems 1-3” is enough).
MATH 256 is a major-level course, so drop-in tutoring at the Mathematics Lab and one-on-one tutoring through the Office of Academic Support are generally not available. The first place you should go with questions related to course material is the course instructor. Additionally, the Student Support Center has a wide variety of available resources that are not specific to this course but may be beneficial to your overall academic successful. For more information, visit the Student Success Center website.
Exams
All exams will be announced at least 14 calendar days ahead of time and will be expected to take an entire 50-minute class session, excluding the take-home final. Formulas and calculators will be provided as necessary.
These exams are intended to test how fully you understand the key concepts of the course, they are not meant to test your memorization or arithmetic. Consequently, you should expect and be prepared for a substantial amount of writing during exams.
Assignments
There will be approximately 6 bi-weekly problem sets and 8-10 labs during the semester.
Each problem set consists of a handful of exercises from the textbook pertaining to the material we are covering at that time. Problem sets are to be submitted individually, but you are encouraged to discuss them with your peers so long as your submitted answers are your own.
Labs will focus on applied statistical analysis of real datasets and R programming fundamentals. They will be completed and submitted in small groups (randomly assigned).
Project
Towards the end of the semester you will complete a comprehensive statistical analysis demonstrating your ability to properly apply methods from this course to data from other disciplines. Details on this project will be provided later in the semester. You will have the opportunity to work alone or with a partner. You will be allowed to choose your topic (pending instructor approval).
The following grade cutoffs will be used:
The course instructor reserves the right to adjust these thresholds downward, but not upward at the end of the semester.