Project measure / variable:   Donahue15   ratio_wean_born

ID, description, units MPD:42405   ratio_wean_born   ratio of pups weaned to pups born   [ratio]  
Data set, strains Donahue15   BXH   11 strains     sex: f     age: 3-55wks
Procedure colony observation
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  STRAIN COMPARISON PLOT
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Donahue15 - ratio of pups weaned to pups born



  MEASURE SUMMARY
Measure Summary Female
Number of strains tested11 strains
Mean of the strain means0.870   ratio
Median of the strain means0.900   ratio
SD of the strain means± 0.0895
Coefficient of variation (CV)0.103
Min–max range of strain means0.663   –   0.963   ratio
Mean sample size per strain8.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 10 0.6405 0.0641 3.1988 0.0018
Residuals 76 1.5218 0.02


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
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Strain Sex Mean SD N mice SEM CV Min, Max Z score
BXH10/TyJ f 0.787 0.146   8 0.0515 0.185 0.6, 1.0 -0.93
BXH14/TyJ f 0.871 0.18   7 0.068 0.206 0.5, 1.0 0.01
BXH19/TyJ f 0.8 0.227   8 0.0802 0.283 0.3, 1.0 -0.78
BXH20/KccJ f 0.663 0.106   8 0.0375 0.16 0.5, 0.8 -2.32
BXH22/KccJ f 0.95 0.107   8 0.0378 0.113 0.7, 1.0 0.89
BXH2/TyJ f 0.925 0.104   8 0.0366 0.112 0.8, 1.0 0.61
BXH4/TyJ f 0.938 0.0916   8 0.0324 0.0977 0.8, 1.0 0.76
BXH6/TyJ f 0.963 0.0518   8 0.0183 0.0538 0.9, 1.0 1.04
BXH7/TyJ f 0.912 0.136   8 0.0479 0.149 0.7, 1.0 0.47
BXH8/TyJ f 0.863 0.207   8 0.073 0.24 0.4, 1.0 -0.08
BXH9/TyJ f 0.9 0.107   8 0.0378 0.119 0.7, 1.0 0.33


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
BXH10/TyJ f 0.7875 0.05 0.8871 0.6879
BXH14/TyJ f 0.8714 0.0535 0.978 0.7649
BXH19/TyJ f 0.8 0.05 0.8996 0.7004
BXH20/KccJ f 0.6625 0.05 0.7621 0.5629
BXH22/KccJ f 0.95 0.05 1.0496 0.8504
BXH2/TyJ f 0.925 0.05 1.0246 0.8254
BXH4/TyJ f 0.9375 0.05 1.0371 0.8379
BXH6/TyJ f 0.9625 0.05 1.0621 0.8629
BXH7/TyJ f 0.9125 0.05 1.0121 0.8129
BXH8/TyJ f 0.8625 0.05 0.9621 0.7629
BXH9/TyJ f 0.9 0.05 0.9996 0.8004




  GWAS USING LINEAR MIXED MODELS