May 26, 2024  
2023-2024 Cal State East Bay Catalog 
2023-2024 Cal State East Bay Catalog [ARCHIVED CATALOG]

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PHYS 381 - Advanced Laboratory II: Experimental Methods

Units: 3
A writing-intensive laboratory course focused on experimental techniques and the scientific method. This project-based course places particular emphasis on effective scientific writing using supporting evidence and reasoning, appropriate quantitative/statistical analysis, presentation of data, and convincing argumentation. Discussion Units: 1; Lab Units: 2.

Strongly Recommended Preparation: PHYS 230.
Prerequisites: PHYS 380.
Possible Instructional Methods: On-ground.
Grading: A-F or CR/NC (student choice).
Course Typically Offered: Variable Intermittently

Student Learning Outcomes - Upon successful completion of this course students will be able to:

  1. Modeling : students will be able to (a) quantitatively model the physical system under investigation; (b) model the measurement system and understand issues associated with precision and accuracy of the equipment; (c) use appropriate statistical analysis methods to quantitatively compare experimental results to the physical model.
  2. Design : students will be able to design, construct, and troubleshoot experimental apparatuses.
  3. Communication : students will be able to (a) effectively argue in favor of their conclusions from their experimentation, calculations, and analysis using appropriate methods of discourse adopted by the professional physics community; (b) keep a clear and comprehensive record of their experimental work in a laboratory notebook; (c) present a well-organized, quantitative argument in the form of a written report; (d) write sentences that are well-constructed; (e) use standard writing conventions for grammar, punctuation, and spelling.
  4. Technical skills : students will be able to (a) use both basic and advanced test and measurement equipment (such as multimeters, oscilloscopes, lock-in amplifiers, spectrum analyzers, ADC and DAC electronics); (b) design, construct, and troubleshoot experimental equipment; (c) write basic Labview/Python programs for signal processing and feedback control in experiments; (d) Use computational packages (such as Mathematica/MatLAB/Python) to make computational models and predictions as well as perform statistical analysis of data.

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