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Provided by: Creascience

Simple and Multiple Linear Regression Techniques

Statistics

Creascience
Training Provided by Creascience

This one-day workshop offers an overview of simple and multiple linear regression techniques, starting with the interpretation of results, strengths and weaknesses and also discusses their practical realization.

Upon completion of this course, participants will be able to:

  • Explain the context of use of simple and multiple linear regression
  • Understand what the underlying assumptions of the techniques are
  • Construct simple/multiple regression models
  • Assess the goodness-of-fit of the model to the data
  • Identify common issues in regression, diagnose problems and fix them
  • Interpret statistical software output
  • This is primarily ilt training
    computer labComputer Lab Work
    group study and discussionThis class may involve group study
    instructor led trainingThis class may be available at a classroom in Montreal, QC,
    Contact Creascience for more information
    Course Level:introductory
    Duration:1 days
    Training Presented in:English
    Simple and Multiple Linear Regression Techniques
    • Simple Linear Regression (SLR)
      • Objectives
      • Terminology
      • What is a model?
      • Model Specification
      • Principle of least squares estimation
      • Interpretation of model coefficients
      • Difference between correlation and regression
      • Example: Data on systolic blood pressure
      • Statistical testing on Beta0 (intercept)
      • Statistical testing on Beta1 (slope)
      • Condition of use and diagnostic tools
      • Prediction in regression analysis
      • Extrapolation
    • Multiple Linear Regression (MLR)
      • Objectives
      • Aspects common to SLR
      • Interpretation of model coefficients
      • Construction of a MLR model
      • Adjusted coefficient of determination (adjusted R2)
      • Checking model adequacy
      • Variable selection
      • Multicollinearity
      • Special terms in MLR models
    • Alternatives to Standard Linear Regression
      • Nonlinear Regression (NLR)
      • Applications of nonlinear regression
      • Other ways to handle multicollinearity
    • Summary
      • Step in model construction
      • Robustness of regression to deviations from conditions of application
      • Some references
    About The Training Provider: Creascience
    Creascience - Creascience offers an wide array of training sessions in statistics aimed primarily at non-statisticians. We also provide specific training courses on statistical methods aimed at statisticians. People attend our training sessions to gain basic knowledge or to deepen some specific aspects. The training sessions are offered both in our offices and on-site, depending on the client s needs. We...
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