\(y\)` and one `\(x\)` variable) linear (relationship between parameters being estimated) regression can be thought of as the simplest form of supervised machine learning and can be used for both prediction and inference. 

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# Regression syntax

The syntax for simple regression looks like this: `\(Y = β0 + β1x + ε\)` 

`\(y\)` = dependent/response/target/outcome variable  
`\(x\)` = independent/input/predictor/feature/covariate variables  
`\(β0\)` = intercept  
`\(β1x\)` = slope for that particula