Introduction to Applied Statistics

STATS191

Stanford School of Humanities and Sciences


Description

Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course uses industry-standard applications and software (R and Python) for numerical reasoning and predictive data modeling, with an emphasis on conceptual rather than theoretical understanding.

 

Prerequisites

  • One semester/quarter of an introductory statistical methods course, OR one year of single-variable calculus
  • A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

Topics include

  • Bootstrap
  • Correlated errors
  • Data snooping
  • Interactions and qualitative variables
  • Multiple linear regression
  • Penalized regression
  • Poisson
  • Regression and prediction
  • Simple linear regression
  • Transformations
  • Variance and cross-validation

 

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.

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