Skagit Valley College

Catalog Course Search Details

This course has been changed from the previous catalog, the changed field(s) are highlighted in red:

 Course Title:   Quantitative Analysis of the Environment


 Department:    QSCI

 Course #:    318

 Credits:    5

 Variable:     No

 IUs:    5.5

 CIP:    030101

 EPC:    16B

 REV:    2021

 Course Description  

Applications to environmental and natural resource problems stressing the formulation and interpretation of statistical tests. Course includes random variables, expectations, variance, binomial, hypergeometric, Poisson, normal, chi-square, t and F distributions. ANOVA, and regression analysis included.


Prerequisite: MATH& 141 with a grade of "C" or higher; and admission to BASEC or department chair permission.

Additional Course Details

Contact Hours (based on 11 week quarter)

Lecture: 44

Lab: 22

Other: 0

Systems: 0

Clinical: 0

Intent: Distribution Requirement(s) Status:  

Vocational Preparatory N/A  

Equivalencies At Other Institutions

Other Institution Equivalencies Table
Institution Course # Remarks

Learning Outcomes

After completing this course, the student will be able to:

  1. Understand biological data and populations and samples.
  2. Determine measures of central tendency, variability, and dispersion.
  3. Determine and interpret probabilities.
  4. Understand the properties of a normal distribution.
  5. Outline sample hypotheses.
  6. Outline multisample hypotheses and analysis of variance (ANOVA).
  7. Perform data transformations.
  8. Calculate linear regressions.
  9. Conduct testing for goodness of fit.

General Education Learning Values & Outcomes

Revised August 2008 and affects outlines for 2008 year 1 and later.

Course Contents

  1. Biological data, populations, and samples.
  2. Measures of central tendency, variability, and dispersion.
  3. Probabilities.
  4. Properties of a normal distribution.
  5. Sample hypotheses.
  6. Multisample hypotheses and analysis of variance (ANOVA).
  7. Data transformations.
  8. Linear regressions.
  9. Testing for goodness of fit.