This activity uses data provided by the Ocean Research College Academy*. Water chemistry and weather data — including parameters such as wind speed, water temperature, dissolved oxygen, and chlorophyll — are provided over a year-long period (2019) so that students can investigate seasonal patterns in primary productivity. Data were logged every 15 minutes using a conductivity, temperature, depth (CTD) sensor resulting in a robust dataset with over 14,000 observations.
Documents included in this module:
- Biology background information
- Statistics background information
- Student spreadsheet with data and activity instructions
- Printable student instructions (same instructions given in spreadsheet)
- Teacher spreadsheet with instructor notes and possible student answers
- Graph Choice and Constructions resource (Angra & Gardner 2016)
- Overlapping: Statistics and Biology
- Create and analyze time-series plots
- Identify independent and dependent variables
- Use Excel to organize, visualize, and analyze data
- Efficiently use iterated formulas in Excel
- Identify outliers, understanding that there is no concrete cutoff for what makes an observation an outlier
- Statistics outcomes
- Calculate and interpret descriptive statistics: mean, median, standard deviation, quartiles
- Determine which descriptive statistics are useful/meaningful in context
- Discuss possible confounding variables
- Biology outcomes
- Understand the key drivers of primary productivity: light and nutrients
- Identify potential predictors and markers of primary productivity: wind, turbidity, temperature, dissolved oxygen, chlorophyll
- Interpret annual trends in primary productivity and recognize complexity of real environmental data
*The Ocean Research College Academy (ORCA) is a full-time Running Start program offered through Everett Community College. Learn more about ORCA online, and read about the data provided in this module in the "Biology Background Info" document.
This material is based upon work supported by the National Science Foundation under Grant No. 1919613. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.