Introduction to Regression Models and Analysis of Variance (Summer)

Regression modeling, when used with understanding and care, is one of the most widely useful and powerful tools in the data analyst’s arsenal. This course aims to build both an understanding and facility with the ideas and methods of regression for both observational and experimental data. You will develop competency in choosing the right set of data analysis tools depending on the nature of data along with their limitations.

Note: This course is offered remotely only via video segments (MOOC style). Lectures will not be recorded on-campus.

Topics Include

  • Introduction, summary statistics and simple liner regression
  • Multiple regression: geometric characterization, goodness of fit, unbiasdness
  • Inferences, t and F tests
  • Confidence intervals for prediction
  • Regression diagnostic
  • Variable selection
  • Shrinkage methods
  • ANOVA: regression based analysis
  • Modeling and interpretation of data

You Will Learn

  • How to study the relationship between variables
  • The assumptions underlying regression analysis
  • How to distinguish between random and fixed variables
  • How to choose the right statistical model for the data

Course Page
$4,056.00 Subject to change
Online, instructor-led
10 weeks, 9-15 hrs/week
Statistics Graduate Certificate
Stanford School of Humanities and Sciences