# [[Variables ]] ### Type of data variable contains - **Quantitative Variables** representing Amounts - Discrete aka Integer Variables - individual items or values - eg. no. of students in a class - Continuous aka Ratio Variables - Measurement of continuous or non-finite values - eg. Distance - **Categorical Variables** representing Groupings - Binary variables - yes / no outcomes - eg. heads or tails / win or lose - Nominal variables - Groups with no rank or volume - eg. colours, species names - Ordinal variables - Groups ranked in a specific order - eg. race standings #### Part of the problem it represents - **Independent  Variables** the cause - Treatment variables - manipulate in order to affect the outcome of an experiment - eg. salt you add to plants water to impact growth - **Dependent  Variables** the effect - Response variables - represent the outcome of the experiment - eg. any measurement of plant health and growth - **Control  Variables** constants - Constant variables - held constant throughout the experiment - eg. temperature and light ##### Other - **Confounding  Variables** - Hides the true effect of another variable in your experiment - potentially hold them constant - eg. pot size and soil type - **Latent Variables** - can’t be directly measured, but that you represent via a proxy - could be inferred / observed - eg. Salt tolerance - **Composite Variables** - made by combining multiple variables in an experiment - created when analyzing not measuring data - eg. plant health score ---- Tags: #tech Links: [[Problem Formulations]]