Spss 22 for mac

We calculated precision and accuracy for translations and rotations in the x-, y-, and z-axes. The data was first examined to determine if it followed a normal (Gaussian) distribution by histograms, box, density, and quantile-quantile plots so that the standard deviation (SD) could be used. We calculated the precision for standard RSA and 3D CT as 2.45∗SD (6 degrees of freedom (d.o.f.)) of the difference between the double examinations (d_prec). The 95% quantile for the t-distribution with 6 d.o.f. is 2.45, and this was chosen for precision since only random errors are included in precision measurements. We calculated the accuracy for 3D CT using the root mean square error (RMS) as 2.57∗RMS (5 d.o.f.). This gives a measure of the magnitude of a varying quantity and was chosen since the difference between the standard RSA and the 3D CT method could be both positive and negative. The 95% quantile for the t-distribution with 5 d.o.f. is 2.57 and was chosen because accuracy involves both systemic and random errors. SPSS 22 for Mac was used for all statistical calculations.

SPSS 22 for Mac (SPSS Inc., USA) was used for all analyses, and values are expressed as means ± SEM. Baseline values represent the mean of the first value (t = −30 min) and second value (t = 0 min) obtained immediately before each hyperinsulinaemic–hypoglycaemic clamp was started. We calculated blood glucose plateaus for all hormones, using mean values of each hormone at clamp time points 30 and 60, 90 and 120, 150 and 180, and 210 and 240 min to obtain four blood glucose plateaus over the entire clamp time of 240 min. We performed ANOVA for key hormones (glucagon, adrenaline and GH) to compare the course of hypoglycaemic counterregulation (‘time’) during sleep and sleep deprivation (‘condition’). We also included z-transformed results for key hormones (glucagon, adrenaline and GH) during the lowest blood glucose plateau in a supraordinate ANOVA of z scores with the factors hormones, condition and clamp to estimate the general effects of sleep deprivation vs sleep on counterregulation. Subsequently, the results of clamp 3 of the sleep condition were compared with those of the other clamps, i.e. sleep deprivation/clamp 1, sleep deprivation/clamp 3 and sleep/clamp 1 using Helmert contrast tests for orthogonal comparisons to explore first- vs second-level differences (e.g. ‘sleep’ vs ‘sleep deprivation’). A p value <0.05 was considered significant.

We genotyped the RAD51D c.576+1G>A, RAD51C c.93delG, and RAD51C c.837+1G>A mutations with Taqman real-time PCR as described elsewhere [18 , 24 (link)]. The PALB2 c.1592delT, the FANCM c.5101C > T, and the CHEK2 mutations were genotyped with Sanger sequencing using primer pairs described in Additional file 1 for PCR and ABI BigDyeTerminator 3.1 Cycle Sequencing Kit (Life Technologies) for the sequencing reactions. The capillary sequencing was performed at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki.
For the analysis, we used population control frequencies in the Finnish population defined in previous studies in up to 2102 healthy female population controls from Helsinki (n = 1287) and Tampere (n = 815) area from the Finnish Red Cross Blood Transfusion Service for the CHEK2 [8 (link), 12 (link)], RAD51D [24 (link)], FANCM [25 (link)], and RAD51C mutations [18 ], and 1079 healthy population controls from the Helsinki region for the PALB2 mutation [22 (link)].
The statistical analysis was done using the SPSS 22 for MAC. P values for comparisons of male breast cancer patients and population controls were calculated using Fisher’s exact test. All P values are two sided.