Anwen Huang, Elaina Li, Avelyn Liang, Anna Khodakovskaia, Katherine Tsvirkunova, Vivian Wang, and Ron Yu

For the third year, the ASA’s Bay Area Chapter (SFASA) hosted its K–12 student summer project program. The program brings together students to design projects under the mentorship of SFASA officers and volunteers.

This year’s program began with a virtual kick-off meeting on June 4. Students were assigned mentors and brainstormed research topics together, providing feedback on everyone’s ideas. Over the summer, the students worked on their projects and met with their mentors weekly to discuss obtaining the data and appropriate data analysis.

In contrast to previous years’ survey-based projects, this year’s students analyzed publicly available large data sets by using RStudio. Students learned to use RStudio from online tutorials and performing statistical analyses under their mentors’ guides. On September 12, the students presented their projects virtually via Zoom. The presentations were open to all SFASA members; roughly 30 attended.

Elaina Li (8th grader) and Avelyn Liang (10th grader) presented their project on COVID-19 vaccination rates in California and their variation based on demographic characteristics and social-economic factors. Under the mentorship of Ray Lin (president of SFASA/Genentech) and Priscilla Yen (Amgen), they found statistically significant differences in vaccination rates across age groups but not across racial groups. Further, they discovered a heterogeneous relationship between vaccination rate and social factors such as median household income within both age and race groups. 

Anna Khodakovskaia (8th grader) and Katherine Tsvirkunova (12th grader) analyzed beach clean-up data in the US under the mentorship of Tao He (past president of SFASA/San Francisco State University). They focused on the amount of plastic found on US beaches from states along the West Coast and East Coast and found the West Coast had a higher proportion of plastic in beach trash. They also determined insufficient evidence to assert that the larger the population, the greater trash collected per mile.

Anwen Huang (12th grader) explored the use of statistical models in breast cancer prediction under the guidance of Ron Yu (public relations director of SFASA/Gilead Sciences). Using RStudio, she compared the misclassification rate of a classification tree, logistic regression, and support vector machine models on the Wisconsin Breast Cancer Data Set, which recorded 10 variable values for malignant and benign breast tumor cells. The rates were calculated through five-fold cross-validation, and Huang determined a support vector machine with six or seven variables produced the highest classification accuracy of 95.1 percent.

Vivian Wang (10th grader), under the guidance of Jerry Ping (AbbVie) investigated the impact of COVID-19 on Recology waste disposal trends in San Mateo County, a project she first started two years ago. She looked at three collection categories—commercial, ­residential, and multi-family—and concluded residential waste had the most noticeable increase since the pandemic. Commercial waste has been gradually increasing in the past year after having a significant decrease in March 2020. Multi-family waste disposal was the least affected by the pandemic. Wang also noticed Hillsborough residents had a significantly higher amount of average waste compared to Foster City and San Mateo residents.

One of the reasons the projects were successful is because of the students’ eagerness and willingness to contact their mentors and seek guidance.

“It was unlike anything I had ever done at school, as I got to focus on a niche topic that interested me and research it to degrees I never anticipated I would be able to do,” said Huang. “I never thought I’d be able to build powerful classifiers, but this project showed me that that was possible.”

The experience also provided a valuable and detailed introduction to statistics. Khodakovskaia and Tsvirkunova recall, “During this project, we learned how to analyze data using R-Project software and how to organize data. We additionally covered the importance of statistical testing, how to perform the Chi-squared test, the proportions test, as well as an F-test in linear regression.”

Along the way, the students encountered challenges, but they overcame them with confidence with the help of their mentors.

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