My externship will be taking place at SAMSI (Statistical and Applied Mathematical Science Institute) which is “a partnership of Duke University, North Carolina State University (NCSU), the University of North Carolina at Chapel Hill (UNC), and the National Institute of Statistical Sciences (NISS), in collaboration with the William Kenan, Jr. Institute for Engineering, Technology and Science. SAMSI is part of the Mathematical Sciences Institutes program of the Division of Mathematical Sciences at the National Science Foundation.” -www.samsi.info

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My mentor is Richard Smith, Director of SAMSI.

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During the externship weeks, I will be conducting statistical analyses of climate data in order to assess which indices are the most significantly linked to the counts of annual tropical cyclones (storms that can potentially become hurricanes).  The annual count of tropical cyclones is rising, and there is data that suggests that climate change is the culprit.  Though as citizens we often judge a hurricane season by the number and magnitude of storms that actually make landfall, there is more that happens beyond our coastline.  Here are some things I’m looking at and learning about in the externship:

  • Statistical analyses using the R programming language,
  • Sea Surface Temperatures (SST) and various Oscillation Indices,
  • standard versus general linear models,
  • Poisson regression,
  • and more!

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After I have gotten my hands dirty in some statistical research, I will then write a curriculum unit using the context of the externship to frame the content.  The curriculum unit will be aligned to the following standards:

  • S.ID.5 Summarize categorical data for two categories in two-way frequency tables.  Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies).  Recognize possible associations and trends in the data.
  • S.ID.6 Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.  (a)Fit a function to the data; use functions fitted to data to solve problems in the context of the data.  Use given functions or choose a function suggested by the context.  Emphasize linear and exponential models.  (b)Informally assess the fit of a function by plotting and analyzing residuals.  (c)Fit a linear function for scatter plots that suggest a linear association.
  • S.ID.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
  • S.ID.8 Compute (using technology) and interpret the correlation coefficient of a linear fit.
  • S.ID.9 Distinguish between correlation and causation.

Some of the components of the curriculum unit I am most excited about are that the students will:

  • apply their knowledge of regression and modeling to make predictions,
  • participate in an Atlantic hurricane forecasting competition throughout the school year,
  • develop tools with which they can answer the question, “is the world around us predictable?”
  • get exposure to the applications of programming in statistics,
  • and more!

To learn more about the Kenan Fellows Program, check out their webpage.