An error occurred on this page.
These results could be incomplete or invalid. Staff have been notified.



Project measure / variable:   Donahue12   fecundity_index

ID, description, units MPD:42106   fecundity_index   mean litter size divided by mean litter interval   [n/wks]  
Data set, strains Donahue12   AXB BXA   28 strains     sex: f     age: 3-50wks
Procedure colony observation
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

Donahue12 - mean litter size divided by mean litter interval



  MEASURE SUMMARY
Measure Summary Female
Number of strains tested28 strains
Mean of the strain means1.22   n/wks
Median of the strain means1.12   n/wks
SD of the strain means± 0.339
Coefficient of variation (CV)0.278
Min–max range of strain means0.760   –   1.85   n/wks
Mean sample size per strain7.6   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 27 22.9833 0.8512 3.872 < 0.0001
Residuals 182 40.0114 0.2198


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
AXB10/PgnJ f 1.43 0.631   8 0.223 0.443 0.19, 2.18 0.62
AXB12/PgnJ f 0.888 0.255   8 0.0902 0.287 0.49, 1.23 -0.98
AXB13/PgnJ f 1.73 0.87   6 0.355 0.502 0.67, 2.55 1.51
AXB15/PgnJ f 1.11 0.588   8 0.208 0.529 0.45, 1.91 -0.32
AXB19a/PgnJ f 1.51 0.586   8 0.207 0.389 0.59, 2.24 0.86
AXB19b/PgnJ f 1.49 0.525   8 0.186 0.353 0.51, 2.2 0.8
AXB19/PgnJ f 1.82 0.671   8 0.237 0.368 0.93, 3.17 1.77
AXB1/PgnJ f 0.833 0.396   6 0.162 0.475 0.29, 1.42 -1.14
AXB23/PgnJ f 1.14 0.378   7 0.143 0.33 0.55, 1.66 -0.24
AXB24/PgnJ f 1.02 0.51   9 0.17 0.499 0.3, 1.98 -0.59
AXB2/PgnJ f 1.31 0.41   8 0.145 0.312 0.84, 1.98 0.27
AXB4/PgnJ f 0.917 0.501   7 0.189 0.546 0.24, 1.52 -0.89
AXB5/PgnJ f 0.805 0.172   4 0.0861 0.214 0.57, 0.98 -1.22
AXB6/PgnJ f 0.76 0.337   8 0.119 0.443 0.3, 1.17 -1.36
AXB8/PgnJ f 1.48 0.531   8 0.188 0.358 0.56, 2.24 0.77
BXA11/PgnJ f 0.924 0.258   8 0.0914 0.28 0.33, 1.18 -0.87
BXA12/PgnJ f 1.85 0.334   8 0.118 0.18 1.17, 2.32 1.86
BXA13/PgnJ f 0.786 0.173   8 0.061 0.22 0.61, 1.11 -1.28
BXA14/PgnJ f 1.59 0.444   8 0.157 0.28 1.03, 2.34 1.09
BXA16/PgnJ f 1.45 0.246   5 0.11 0.169 1.19, 1.84 0.68
BXA1/PgnJ f 1.01 0.256   7 0.0967 0.254 0.61, 1.22 -0.62
BXA24/PgnJ f 1.08 0.281   8 0.0992 0.26 0.63, 1.43 -0.41
BXA25/PgnJ f 1.21 0.383   8 0.135 0.317 0.77, 1.81 -0.03
BXA26/PgnJ f 1.27 0.525   8 0.186 0.414 0.39, 2.1 0.15
BXA2/PgnJ f 1.78 0.715   8 0.253 0.403 0.69, 2.75 1.65
BXA4/PgnJ f 0.941 0.493   8 0.174 0.524 0.3, 1.8 -0.82
BXA7/PgnJ f 1.0 0.35   7 0.132 0.35 0.56, 1.47 -0.65
BXA8/PgnJ f 1.02 0.343   8 0.121 0.337 0.71, 1.77 -0.59


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
AXB10/PgnJ f 1.4263 0.1658 1.7533 1.0992
AXB12/PgnJ f 0.8875 0.1658 1.2146 0.5604
AXB13/PgnJ f 1.7317 0.1914 2.1093 1.354
AXB15/PgnJ f 1.1125 0.1658 1.4396 0.7854
AXB19a/PgnJ f 1.5075 0.1658 1.8346 1.1804
AXB19b/PgnJ f 1.4875 0.1658 1.8146 1.1604
AXB19/PgnJ f 1.8225 0.1658 2.1496 1.4954
AXB1/PgnJ f 0.8333 0.1914 1.211 0.4557
AXB23/PgnJ f 1.1443 0.1772 1.494 0.7946
AXB24/PgnJ f 1.0222 0.1563 1.3306 0.7138
AXB2/PgnJ f 1.3113 0.1658 1.6383 0.9842
AXB4/PgnJ f 0.9171 0.1772 1.2668 0.5675
AXB5/PgnJ f 0.805 0.2344 1.2676 0.3424
AXB6/PgnJ f 0.76 0.1658 1.0871 0.4329
AXB8/PgnJ f 1.4838 0.1658 1.8108 1.1567
BXA11/PgnJ f 0.9237 0.1658 1.2508 0.5967
BXA12/PgnJ f 1.8538 0.1658 2.1808 1.5267
BXA13/PgnJ f 0.7863 0.1658 1.1133 0.4592
BXA14/PgnJ f 1.5875 0.1658 1.9146 1.2604
BXA16/PgnJ f 1.454 0.2097 1.8677 1.0403
BXA1/PgnJ f 1.0071 0.1772 1.3568 0.6575
BXA24/PgnJ f 1.0775 0.1658 1.4046 0.7504
BXA25/PgnJ f 1.205 0.1658 1.5321 0.8779
BXA26/PgnJ f 1.2662 0.1658 1.5933 0.9392
BXA2/PgnJ f 1.7763 0.1658 2.1033 1.4492
BXA4/PgnJ f 0.9412 0.1658 1.2683 0.6142
BXA7/PgnJ f 1.0 0.1772 1.3497 0.6503
BXA8/PgnJ f 1.02 0.1658 1.3471 0.6929




  GWAS USING LINEAR MIXED MODELS