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=== Day 4 - Correlation and regression === #:- See also, [[Misunderstood concepts in statistics#Correlation|misunderstood concepts]]

...ful tool in data visualization for observing the relationship (also called correlation) between two variables. In the following we will go through the steps to cr ...r relationship. If there is no pattern visible, it often implies no linear correlation.

...s variables. For R examples on Correlations, please refer to [[Correlation Plots]]. ...ght|'''SCOPUS hits per year for Correlations until 2020.''' Search terms: 'Correlation' in Title, Abstract, Keywords. Source: own.]]

[[Correlations#Reading_correlation_plots|Reading correlation plots]]

...tion|Normal Distribution]]. If the data sample size is big enough and the plots look quite symmetric, we can also assume it's normally distributed. '''If there are no dependencies between your variables, you should do a Correlation.'''<br/>

...n Causality and Correlations, please refer to the entry on [[Causality and correlation]]. Take the example of [https://sciencebasedmedicine.org/evidence-in-medicine-correlation-and-causation/ medical studies] where most studies build on a correlative d

...nSampleSize.png|thumb|500px|right|'''Sample size matters.''' As these five plots show, bigger samples will more likely show a normal distribution.]] ...ts are on this line you are fine, you have normal errors, and you can stop reading here. If you want to know more about the theory behind this please continue