Project Examples

Statistical Analysis of Chloride Levels in Wells in Dunstable

Course: 14.286- Probability & Statistics for Engineers
Semester: Fall 2006, Fall 2007
Instructor: Oguz Gunes
Partner:Town of Dunstable

Fall 2006

Students in the Probability and Statistics course for Engineers collaborated with the City of Dunstable to investigate and analyze levels of chloride in local wells. The project involved a statistical analysis of chloride levels in wells in the Town of Dunstable, and provided a correlation between the well type and its proximity to highways. Each student produced a report providing details of their analysis, findings, conclusions and further recommendations. Positive feedback was generated from the UML students indicating that this project was a valuable learning experience. One student asserted, “It was definitely interesting!” The project provided the UML professor with an opportunity to cover the correlation topic in detail and teach beyond the classical linear correlation such as power-law and logarithmic curve fitting. The community partner is likely to benefit from the recommendations made by the student reports. The reports indicated a need for more data from both deep and shallow type wells in different times around the year.

Learning objectives met by the S-L project:

  • Reinforced student abilities to apply knowledge of math, science & engineering
  • Enhanced students’ ability to design experiments, analyze & interpret data
  • Increased students’ ability to communicate technical & professional information in written & oral formats

Community objectives met by S-L project:

  • Provided investigation of the chloride levels in wells
  • Provided baseline information to asses potential health hazards
  • Recommendations and research provided information to formulate remedial measure

Fall 2007

Students in the Probability and Statistics course for Engineers collaborated with the City of Dunstable to investigate and analyze levels of chloride in local wells. The project is continued from last year with new data obtained from the same wells. A correlation between the well type and its proximity to highways was investigated with emphasis on potential improvement of correlation with the new data obtained in 2007.

Each student produced a report showing the correlation between the chloride content in shallow wells and their proximity to highways (which includes the impact of new data obtained in 2007). The reports also provided details of their analysis, findings, assessment of potential improvement in correlation with new data, conclusions and further recommendations. Positive feedback was received from the students regarding the ‘real-life’ characteristic of the project, affecting the students to such an extent that one report stated its findings as coming from “A concerned citizen of Massachusetts …”.

The project provided the UML professor with an opportunity to cover the correlation topic in detail and teach beyond the classical linear correlation such as power-law and logarithmic curve fitting. The community partner is likely to benefit from the recommendations made by the student reports, which indicated a need for more data from both deep and shallow type wells in different times around the year.