New MBTN Module on Multiple Correlation and Regression - MBTN Academy
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New MBTN Module on Multiple Correlation and Regression

MBTN Academy announces the beta release of Statistics 3: Multiple Correlation and Regression. The module covers statistics commonly used to describe the relationship between three or more numerically-scaled variables (correlation and regression). Multiple correlation and regression is a generalization of pairwise correlation and regression covered in the Statistics 2 module. In Statistics 3, we expand the analysis to include the relationship of three or more stochastic variables.

The Statistics 3 tutorial includes the following topics and provides a solid introduction to multiple linear regression:

  • The least squares method of estimating the multiple linear regression model
  • Use of Excel to run multiple linear regression
  • Investigation of the quality of a regression model
  • Pair correlation matrix and interpretation
  • Interpretation of the significance of correlation coefficients
  • Multicollinearity and independent variables
  • Prediction based on a linear regression model
  • Economic meaning of regression coefficients

Statistics 3 consists of 35 slides, creation of a multiple linear regression model from start to finish, and 4 problem sets consisting of 28 total questions. The module also comes with an additional dataset, answer key, and explanation. MBTN faculty may add this module to Spring/Summer classes at no additional cost for students. Learn more about this module and all MBTN modules at https://www.mbtn.academy/our-modules/

statistics multiple correlation and regression overview

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1 Comment
  • Alexander Skvorchevsky
    Posted at 17:34h, 26 April Reply

    In economics, as a rule, the resulting factor is influenced by several variables at once. Multiple correlations and regression analysis just make it possible to analyze such influences. That is why this module will be an extremely useful tool for the analytical description of economic processes and forecasting their development.

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