Project measure / variable:   Odet2   sperm_prog_10

ID, description, units MPD:53815   sperm_prog_10   percentage of motile sperm that are 'progressive'   [%]  at 10 min  
Data set, strains Odet2   inbred w/CC8   8 strains     sex: m     age: 10-66wks
Procedure microscopy
Ontology mappings

  •   Or, add this measure to your collection.
       
  •     no folder
  •     put it in new folder  

  STRAIN COMPARISON PLOT
Dimensions Width:   px    Height:   px
Download Plot   
Visualization Options

Odet2 - percentage of motile sperm that are 'progressive' at 10 min



  MEASURE SUMMARY
Measure Summary Male
Number of strains tested8 strains
Mean of the strain means48.8   %
Median of the strain means49.2   %
SD of the strain means± 16.3
Coefficient of variation (CV)0.335
Min–max range of strain means29.0   –   80.7   %
Mean sample size per strain14.5   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 7 28147.4533 4021.0648 13.363 < 0.0001
Residuals 108 32498.3373 300.9105


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
Select table page:
Strain Sex Mean SD N mice SEM CV Min, Max Z score
129S1/SvImJ m 42.8 18.1   17 4.4 0.423 9.0, 74.1 -0.37
A/J m 51.8 21.7   17 5.26 0.419 11.2, 77.5 0.19
C57BL/6J m 30.4 18.8   11 5.66 0.618 3.7, 59.9 -1.13
CAST/EiJ m 29.0 8.65   12 2.5 0.299 17.0, 42.5 -1.21
NOD/ShiLtJ m 47.6 17.6   10 5.55 0.369 13.3, 80.6 -0.07
NZO/HlLtJ m 50.9 14.8   16 3.69 0.29 19.4, 73.3 0.13
PWK/PhJ m 56.9 12.3   15 3.17 0.216 34.6, 76.9 0.5
WSB/EiJ m 80.7 20.6   18 4.86 0.255 0.4, 91.4 1.96


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ m 42.8118 4.2072 51.1512 34.4723
A/J m 51.7941 4.2072 60.1335 43.4547
C57BL/6J m 30.3545 5.2302 40.7218 19.9873
CAST/EiJ m 28.9583 5.0076 38.8842 19.0324
NOD/ShiLtJ m 47.63 5.4855 58.5033 36.7567
NZO/HlLtJ m 50.8563 4.3367 59.4523 42.2602
PWK/PhJ m 56.9133 4.4789 65.7913 48.0353
WSB/EiJ m 80.6722 4.0887 88.7767 72.5678




  GWAS USING LINEAR MIXED MODELS