What does this course involve?
The overarching goal of this course is to promote a critical attitude towards analytical inquiry in Economics and Business. It introduces students industry standard tools and fundamental ideas so they can engage with different kinds of data in appropriately rigorous ways.
Other objectives of the course include:
Developing students’ analytical, quantitative reasoning, and communication skills
Encourage critical thinking about data and analytical methods
Prepare students for future careers in data business and economics
To prepare students to engage upper level economics and business courses
Broadly, the course is organized into three sections.
Review of Probability and Statistics The course will begin where Introduction to Statistics (MA005 or equivalent) ended. The review covers descriptive statistics, probability theory, and inferential statistics in one and two variables. It servers also as a platform for learning R, which is a language and environment for statistical computing and graphics.
Linear Regression The main statistical technique in this course is Ordinary Least Squares (OLS), which is a common tool for modeling economic systems and generating forecasts. In addition to building, interpreting, evaluating OLS regressions, this part of the course explores situations where OLS regression is inappropriate or otherwise inadequate.
Calculus The final part of the course will introduce calculus. This section facilitates future classes in the Economics & Business major.
By the end of this course, students will also be able to perform basic data wrangling and visualization tasks. students will also be able to build and interpret OLS regressions using R. Students will appreciate the common failures of the linear model and be able to detect and correct for them. Students will able to use linear regression models to generate forecasts.
Students must have taken MA005, Introduction to Statistics or equivalent in its entirety.
This site Lecture materials and problem sets will be posted to this site. There will be a link to this page on the Westmont Canvas site and another link on my homepage.
Textbooks This class will rely upon two texts.
Lectures The main ideas, techniques, and worked examples will be presented during lectures in a workshop style. Students will need a laptop in each class meeting
Office hours Please feel free to make an appointment
Students will sit three exams (two midterms plus a final) corresponding to the three sections in the course. Examinations are not intended to be cumulative but because the course builds on itself, repetition is unavoidable. Dates, times, locations, and format will be advised although general timing is indicated in the tentative topic schedule below. Examinations are weighted as follows:
Probability & Statistics Review: 25%
Linear regression: 25%
Calculus: 10% (Pass or fail)
Total grade value: 60%
To encourage continuous engagement with the material, students will complete weekly problem sets. Students are encouraged to work on these in groups but you must submit individual work. Submissions should be to Canvas.
Total grade value: 25%
To encourage continuous engagement with material, students will submit weekly visualizations using the weekly Tidy Tuesday dataset. Please post your visualizations to the E&B #Tidy-Tuesday Slack channel
Total grade value: 10%
Students are expected to engage in class by asking/responding to questions. Participation credit (beyond attendance) is awarded at the end of the semester.
Total grade value: 5%
The schedule below indicates the approximate timing of each topic.
Your regular attendance and active participation in class is very important and expected. Following college Significant unexcused absences (3 or more classes) or lack of participation (missed assignments) or some combination may result in penalties or dismissal from the course in accordance with the college’s attendance policies as documented below.
Any absence from a scheduled examination or other assessment must be accompanied by appropriate documentation.
Dishonesty of any kind may result in loss of credit for the work involved and the filing of a report with the Provost’s Office. Major or repeated infractions may result in dismissal from the course with a grade of F. Be familiar with Westmont’s plagiarism policy.
Within the context of this class, you are encouraged to work in groups with your classmates. You are welcome to share code, share ideas, and help each other with problem sets and class exercises. You are also welcome to use the internet to find examples, clarify syntax, or clarify concepts. However, you are not to engage with live sources beyond the people in the class for specific help with assignments. For example, utilizing social media for homework help, posting questions to online boards, or participating in online chat forums/boards for help with problem sets or other assignments is considered cheating. Similarly, utilizing outside resources that provide specific solutions (e.g. Chegg, solutions manuals, or the submissions of past students) is also considered cheating.
Students who have been diagnosed with a disability are strongly encouraged to contact the Office of Disability Services as early as possible to discuss appropriate accommodations for this course. Formal accommodations will only be granted for students whose disabilities have been verified by the Office of Disability Services. These accommodations may be necessary to ensure your equal access to this course. Please contact Sheri Noble, Director of Disability Services. (310A Voskuyl Library, (805) 565-6186, snoble@westmont.edu) or visit the website for more information.