The goal of the assignments is to practice the concepts and vocabulary we have been modeling in class and implement some of the techniques we have learned. You may work on your own or collaborate with one (1) or two partner(s). Please make sure that you engage in a full, fair and mutually-agreeable collaboration if you choose to collaborate. If you do collaborate, you should plan, execute and write-up your analyses together, not simply divide the work. Please make sure to indicate clearly when your work is joint and any other individual or resource (outside of class material) you consulted in your responses. Per the syllabus, please clearly state if you have relied on AI tools to generate your original text. You need not do so if you have used these tools only to help with coding tasks and/or light editing.
Each of the four assignments during the term is worth 15 points, and the final project is worth 30 points. Assignments are due at 11:59pm on their due date. If circumstances arise that require an extension on an assignment, please communicate with the lead instructor prior to the assignment’s due date. We will publish the answer keys to assignments prior to class the following day; thus, late assignments will be penalized by two (2) points. While it is acceptable to consult the answer keys if you submit an assignment late, submissions that re-iterate word for word (or nearly so) the answer key will not be accepted. All late assignments must be turned in by the last day of classes. Late final projects may result in a final grade of I or D/F. Assignments scoring below a 13 may be revised and can receive a maximum score of up to 13.
Please upload below two files on Canvas:
Categorical data 1: data structure and descriptives
Objectives of this assignment:
Describe and summarize categorical data
Create visualizations of categorical data
Deadline: October 14, 11:59pm
Download: html file | cat.csv dataset
Key:
Categorical data 2: chi-square analysis
Objectives of this assignment:
Describe relationships between categorical variables
Calculate an index of the strength of the relationship between two categorical variables, the chi-squared (\(\chi^2\)) statistic
Formulate and describe the purpose of a null hypothesis
Conceptually describe the criteria to make a statistical inference from a sample to a population
Interpret and report the results of a contingency-table analysis and a statistical inference from a chi-squared statistic
Deadline: October 25, 11:59pm
Download: html file | cat.csv dataset
Key:
Continuous data 1: summarizing continuous data and \(t\)-tests
Objectives of this assignment:
Describe and summarize continuous data
Create visualizations of continuous data
Conduct and interpret a one-sample \(t\)-test
Deadline: November 8, 4:59pm
Download: html file | cont.csv dataset
Key:
Continuous data 2: intro to bivariate regression
Objectives of this assignment:
Create bivariate scatterplots and bivariate regression estimates
State null hypothesis and interpret results of linear regression test of bivariate continuous relationship
Test (some) assumptions of bivariate regression estimation
Deadline: December 2, 11:59pm
Download: html file | cont.csv dataset
Key:
Final assignment
Deadline: December 11, 4:59pm
Download: html file | ah01.csv dataset; ah02.csv dataset
Key: