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More about adding a collaboratorgenerate multivariate correlated pseudorandom scalar attributes
in order to quickly create complex domains
a world creator should be able to create populations with multivariate correlated pseudorandom scalar attributes
 Scenarios

 weaklycorrelated scalar feature pair
 stronglycorrelated scalar feature pair
 arbitrarilycorrelated scalar feature pair
 multiple features with covariance matrix

 Given
 a feature "height" with mean 80 and standard deviation 20
 And
 a feature "weight" with mean 150 and standard deviation 15
 And
 "height" and "weight" are weakly correlated
 When
 a large population is sampled
 Then
 "height" and "weight" should be weakly correlated
 And
 the empirical rule should hold

 Given
 a feature "height" with mean 80 and standard deviation 40
 And
 a feature "weight" with mean 150 and standard deviation 30
 And
 "height" and "weight" are strongly correlated
 When
 a large population is sampled
 Then
 "height" and "weight" should be strongly correlated
 And
 the empirical rule should hold

 Given
 a feature "height" with mean 80 and standard deviation 45
 And
 a feature "weight" with mean 160 and standard deviation 50
 And
 "height" and "weight" are 30% correlated
 When
 a large population is sampled
 Then
 "height" and "weight" should be 30% correlated
 And
 the empirical rule should hold
 multiple features with covariance matrix

 Given
 a feature "height" with mean 80 and standard deviation 25
 And
 a feature "weight" with mean 150 and standard deviation 30
 And
 a feature "foot_size" with mean 10 and standard deviation 3
 And
 "height" and "weight" are strongly correlated
 And
 "weight" and "foot_size" are weakly correlated
 And
 "height" and "foot_size" are 90% correlated
 When
 a large population is sampled
 Then
 "height" and "weight" should be strongly correlated
 And
 "weight" and "foot_size" should be weakly correlated
 And
 "height" and "foot_size" should be 90% correlated
 And
 the empirical rule should hold
Last published over 7 years ago by jweissman.