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1  acid made a significant contribution to the regression equation.
2  to PA or vLTF were input into a multilinear regression equation.
3 n of TPC was modeled by using a second-order regression equation.
4 centrations and dietary modifiers by using a regression equation.
5 n how volume is defined and entered into the regression equation.
6 /-SD of 3+/-1.5 of the variables entered the regression equation.
7 riability in heart rate was explained by the regression equation.
8 ing the observed summary statistics into the regression equation.
9  each patient based on a previously reported regression equation.
10 eled amounts were calculated with the use of regression equations.
11 l values of these tests can be computed from regression equations.
12 lysis (CoMFA) prediction, to form predictive regression equations.
13  in separate path analyses by using logistic regression equations.
14 evere toxicity grade was estimated using the regression equations.
15 ly significant difference among sites in the regression equations.
16     Using the best-fitting multiple logistic regression equation, a 100-point increase in pAkt staini
17                         The finding that the regression equation accounts for only 39% of the variabi
18 's comorbidity index, and length of CPB in a regression equation allowed for a prediction of postoper
19                                    A derived regression equation allowed reasonable discrimination of
20 estimated from SPECT HMR via a simple linear regression equation, allowing use of the new cardiac-ded
21 ofluid could be back-calculated based on the regression equation and response factor of KIC to KIC-d(
22                                   Using this regression equation and reverse prediction, we quantifie
23 ethods tend to be time-consuming and costly; regression equations and constitutive models usually hav
24 f intervals) appeared most frequently in the regression equations and were the first or second variab
25 ratio with the use of a previously described regression equation, and the 95% confidence limits of ag
26 xperimentally according to the corresponding regression equation, and the results agree well with cli
27               Multilevel modeling, combining regression equations at two levels, was used to examine
28 rly intervention strategies using a logistic regression equation based on this model for patient recr
29                                              Regression equations based on 3-5 descriptors are able t
30   The position of the HJC is determined from regression equations based on anthropometric measurement
31                                              Regression equations based on doubly labeled water measu
32 s called "no rejection" by Banff guidelines, regression equations based on histology features identif
33                                              Regression equations, based on scutal index (body length
34                                   The linear regression equation between TSD (the dependent variable)
35 d effect modification was assessed in linear regression equations by modeling the product and margina
36          Pharmacogenetic algorithms based on regression equations can predict warfarin dose, but they
37                                            A regression equation containing the quantitative contribu
38                                              Regression equations correlating bioaccumulation (CL) an
39                                   The linear regression equation could be used in a clinical setting
40 arance from 24-h urine collection, and a new regression equation derived from the pilot study data.
41                    We propose a range of new regression equations, derived from the largest dataset c
42                                          The regression equation developed by conducting the kinetic
43                                          The regression equation developed by conducting the kinetic
44  number of aromatic rings in multiple linear regression equations enabled improved correlations up to
45  acute event were based on negative binomial regression equations estimated from the placebo arm of t
46                The resulting gender-specific regression equations explained about 40% of the variance
47  GFR (eGFR), where eGFR was estimated from a regression equation for GFR depending primarily on serum
48 nsitive colorimetric process produced linear regression equation for H(2)O(2) as A = 0.00105C + 0.063
49                                   The linear regression equation for predicted exercise capacity (in
50                             The slope of the regression equation for REE and FFM was significantly gr
51                                          The regression equation for the lowest VE/CO2 output ratio w
52                                    Published regression equations for infants consistently gave highe
53                                              Regression equations for mortality rates for each cluste
54                                  Several REE regression equations for SCA were developed.
55  of appropriate descriptors to form accurate regression equations for the compounds under study.
56                                   The linear regression equations for uric acid substrate was stated
57  4 to 16, were selected in two sets, and the regression equation formed with one set was used to pred
58                               We developed a regression equation from 20 mixtures to predict the rate
59                                 By using the regression equation generated from this model, we could
60                                          The regression equations generated from the four calibration
61 hat made use of values based on the logistic regression equation had an area under the receiver opera
62                                The resulting regression equations had a high prediction rate calculat
63                                  A number of regression equations have been established between parti
64 ther a fixed ratio unaffected by age or as a regression equation in which the ratio varies as a funct
65                           Using lesion-based regression equations in addition to Banff histology guid
66                   At the baseline level, the regression equation including age and DeltaMRT of negati
67 eas pharmacogenetic-guided dosing followed a regression equation including the 3 genetic variants and
68 he index values are calculated with a single regression equation instead of the Van den Dool and Krat
69                                          The regression equation is Y = 0.0014 X + 1.2036, where Y an
70                                     A set of regression equations is developed from the theoretical r
71  tools based on distances and residuals from regression equations, is appropriate for authenticating
72 to Amplicor-equivalent units by using linear-regression equations [log(10) HCS-1 result = 0.49 (log(1
73 nd "true" RBF when we predicted SFOL using a regression equation obtained from a subset of samples (a
74                                          The regression equation obtained in the laboratory-based stu
75                                 The logistic regression equation obtained was used to determine the p
76  in the test samples was calculated from the regression equation of a standard curve that was generat
77  of 5 x 10(-7) ~50 x 10(-7)mol/L, the linear regression equation of IPL = 1226.3-13.6[CCu(2+)] (R = 0
78  over the range of 0.370-2.570 mug/mL with a regression equation of Y = ax + b and correlation coeffi
79 skinfold thicknesses, and estimated with the regression equations of Durnin and Womersley.
80                                          The regression equations of Slaughter and Dezenberg, which a
81 le ratio of SM to creatinine and is based on regression equations of the form SM = b + a x creatinine
82                                          The regression equations of these parabens exhibited good li
83 es among sessions in the slope of the active regression equation (P = 0.005).
84 ress through the event scale, described by a regression equation predicting duration of development i
85                                          The regression equations predicting the SI the glucose and i
86 ndards on Altona had nearly identical linear regression equations (primary standard, Y = 1.05X - 0.28
87                                          The regression equation (RAP=21.6-24 systolic filling fracti
88  TaqMan assays produced the following Deming regression equation: RealTime = 0.940 (TaqMan) + 0.175 l
89                                          The regression equation relating mean TDEE to demographics a
90             The slopes and intercepts of the regression equations relating LBM to average daily creat
91                                 For PLS, the regression equations showed high predictive capacity, wi
92                                     Multiple regression equations showed that only the number of ciga
93                                          The regression equations showed that oxygen consumption foll
94                             The best-fitting regression equation specific for this sample by using an
95 nts could be very well described by a single regression equation that included DOW of the sorbate and
96                                      The Cox regression equation that predicts survival based on thes
97  whole genome shotgun projects, we construct regression equations that relate coverage to a normalize
98 en height was included as a covariate in the regression equation, the association with total body BMD
99                                            A regression equation to calculate iron absorption was der
100 d as the study group to derive a multilinear regression equation to estimate LA dP/dt(max) from Doppl
101                                   A specific regression equation to measure TBW in a VLBW population
102 n time or WMSI or LV mass was entered into a regression equation to predict follow-up LVEF, the LVEF
103                                         1) A regression equation to predict HRmax is 208 - 0.7 x age
104         Recovery biomarker data were used in regression equations to calibrate self-reports; the pote
105 ed to explore the applicability of published regression equations to estimate stature of Puerto Rican
106 Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, s
107                    We explored whether using regression equations to interpret the features as well a
108 the HJC within the pelvis was determined and regression equations to locate the HJC were developed us
109                                  None of the regression equations to predict the 24-h lipid content o
110 n this retrospective study, the multivariate regression equations using these four variables provided
111     The root mean square error (RMSE) of the regression equation was close to 0, and the coefficient
112 ly proportional to the change in urine pH; a regression equation was generated to relate these variab
113                                     A viable regression equation was obtained for each data set using
114                                     A viable regression equation was obtained using only four descrip
115                                            A regression equation was then derived; it reproduces the
116 del for predictive biomarkers, the resulting regression equation was then inverted to develop a calib
117  suitability and applicability of determined regression equations was confirmed by the analysis of 13
118 t considered multiple risk factors to derive regression equations was used to determine which clinica
119                         A new dimensionless (regression) equation was proposed that predicts the alph
120                              Rearranging the regression equation, we determined the performance score
121    In this external validation study, FAMCAT regression equations were applied to a retrospective coh
122                                              Regression equations were applied to scattergram plots o
123                                        Three regression equations were developed and verified for acc
124 uscles, and the slopes and intercepts of the regression equations were not significantly affected by
125 nverted to principal components and manifold regression equations were then developed with body mass,
126                           ANOVA and multiple regression equations were used in the analysis.
127                                              Regression equations were used to determine significant
128                          Hierarchical linear regression equations were used to explain variance in re
129                                   Predictive regression equations with adequate coefficients of deter
130                                       Linear regression equations with similar slopes and y-intercept
131 sing standardized LC-tandem MS and resulting regression equations yielded predicted standardized seru

 
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