Project measure / variable:   Gould1   immob400

ID, description, units MPD:37404   immob400   duration of immobility   [s]  dose: 400 mg/kg  
lithium study
Data set, strains Gould1   inbred   11 strains     sex: m     age: 7-8wks
Procedure forced swim test
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  STRAIN COMPARISON PLOT
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Gould1 - duration of immobility dose: 400 mg/kg



  MEASURE SUMMARY
Measure Summary Male
Number of strains tested11 strains
Mean of the strain means85.9   s
Median of the strain means77.8   s
SD of the strain means± 77.0
Coefficient of variation (CV)0.895
Min–max range of strain means1.50   –   210   s
Mean sample size per strain7.7   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 10 459646.5186 45964.6519 40.3882 < 0.0001
Residuals 72 81941.0476 1138.0701


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
129S6/SvEvTac m 83.2 48.0   8 17.0 0.577 -0.04
A/J m 181.0 30.0   8 10.6 0.166 135.0, 207.0 1.24
BALB/cJ m 210.0 15.2   8 5.38 0.0723 185.0, 228.0 1.61
C3H/HeNHsd m 23.6 20.4   8 7.22 0.865 4.0, 57.0 -0.81
C57BL/6J m 192.0 16.1   8 5.69 0.0839 174.0, 215.0 1.38
CBA/J m 39.6 26.1   8 9.21 0.658 9.0, 89.0 -0.6
Crl:CD-1(ICR) m 77.8 37.8   6 15.5 0.486 26.0, 122.0 -0.11
DBA/2J m 103.0 74.9   7 28.3 0.724 33.0, 232.0 0.22
FVB/NJ m 1.5 1.22   6 0.5 0.816 -1.1
Hsd:NIHS m 32.2 32.8   8 11.6 1.02 1.0, 90.0 -0.7
NTac:NIHBS m 1.5 1.85   8 0.655 1.23 -1.1


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S6/SvEvTac m 83.25 11.9272 107.0265 59.4735
A/J m 181.25 11.9272 205.0265 157.4735
BALB/cJ m 210.375 11.9272 234.1515 186.5985
C3H/HeNHsd m 23.625 11.9272 47.4015 0.0
C57BL/6J m 191.875 11.9272 215.6515 168.0985
CBA/J m 39.625 11.9272 63.4015 15.8485
Crl:CD-1(ICR) m 77.8333 13.7724 105.2881 50.3786
DBA/2J m 103.4286 12.7507 128.8467 78.0104
FVB/NJ m 1.5 13.7724 28.9547 0.0
Hsd:NIHS m 32.25 11.9272 56.0265 8.4735
NTac:NIHBS m 1.5 11.9272 25.2765 0.0




  GWAS USING LINEAR MIXED MODELS