What are the differences between simple, multiple, univariate, and multivariate linear regression?

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
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From my understanding:

  • Simple linear regression: one input feature with one or more output variables
  • Multiple linear regression: multiple input features with one or more output variables
  • Univariate linear regression: one or more input features with one output variable
  • Multivariate linear regression: one or more input features with multiple output variables

Is this correct?

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What does this wikipedia page tell you?

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.[1] This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.[2]