Statistical analyses. Means are reported with standard deviations. For all statistical tests, normality was evaluated using the Kolmogorov-Smirnow test (when n > 50) and the Shapiro Wilk test (when n < 50). For normal distributions, we used a t-test (paired or unpaired, t). For non-normal distribution, non-parametric tests were used: (a) The Wilcoxon signed-rank test (Z) for within-subject comparisons; (b) the Wilcoxon Mann Whitney test (U) for two-sample comparisons. Finally, a Chi-square test (X2) was used to compare gender between participants having chosen French and those having chosen German to measure the independence of both variables in a 2-by-2table. Behavioral results were reported in the format: Test (degrees of freedom) = Statistic, p = value. All tests were two-tailed and a p-value < 0.05 was considered statistically significant. All statistical analyses on epidemiological and behavioral data were performed with SAS System V9.4 (SAS Institute, Cary, NC, US).
Statistical analyses. Means, standard deviations, percentages, and correlation coefficients were calculated. Five hierarchical regression analyses were conducted where Facebook addiction, stress, general health, sleep quality, and quality of life were dependent variables. Independent variables introduced in subsequent steps can be found in Tables 2 and 3. For all linear regression analyses, preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity, and homoscedasticity. All tests were two-tailed and the significance level was set to α = .05.
Statistical analyses. Means and standard deviations of all measured values were calculated for each condition. General Linear Model was applied to all set of data, followed by a multiple comparison, the Tukey’s test. Significance value was p<0.05. Minitab 17 (Minitab Inc., USA) statistical program was used for data analyses. Sensory scores were represented as the mean of each panelist’s scores. Means and standard deviations of the peak areas of volatile organic compounds were calculated for each condition. General Linear Model was applied to a set of VOC data, followed by a multiple comparison, the Tukey’s test. Significance value was p<0.05. The data of the peak areas of VOC and sensory scores of overall acceptability were submitted to Principal Components Analysis (PCA) for qualitative classification. For the quantitative estimation of the biochemical changes and the microbial population of total viable counts, Pseudomonas spp., LAB and sensory quality, PLS regression (PLS-R) models were calculated using the volatile compounds as input variables (predictors) and the quality parameters as output variables (response). The leave-one-out cross validation (LOOCV) technique was applied to evaluate the performance of the models. The Minitab 17 (Minitab Inc., USA) statistical program was used for all mathematical data treatments and statistical analyses.The PLS model is a bilinear regression model that extracts a small number of factors, which are a combination of the independent variables, and uses these factors as a regression generator for the dependent chemically and biologically measured variables(Maleki et al, 2006). PLS regression is known for its simplicity, robustness, predictability, precision, and clearly quantitative explanations. Despite this, PLS regression does not present a quantitative relationship between predictor variables and response variables, and it does not support re-use of model algorithms between different instrumentations (Li et al, 2012). Some validation parameters of model are evaluated such as X Variance, Error, R-Sq, PRESS and R-Sq (pred.). Prediction errors (PE) or standardized residuals for each of individual prediction point were calculated with the equation (3.5) below and it can be served as the overall performance of the model (Oscar, 2009). The percentage of PE above 70% present that the prediction model can be used for prediction of test data in the acceptable range (Oscar, 2005). %PE = (PEin/PEtotal) x 100 (3.5) Where, PEin : The number of PE...
Examples of Statistical analyses in a sentence
Statistical analyses were conducted by FDA to determine whether the financial interests/arrangements had any impact on the clinical study outcome.
Statistical analyses must be conducted in accordance with international statistical reporting standards (Altman DG, Gore SM, Gardner MJ, Pocock SJ.
Statistical analyses for possible exposure-related effects12 on survival used Cox’s (1972) method for testing two groups for equality and Tarone’s (1975)13 life table test to identify exposure-related trends.
Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations.
Statistical analyses of the responses to LHRH injection, and melatonin and prolactin concen¬ trations during development consisted of one- and two-way analyses of variance.
More Definitions of Statistical analyses
Statistical analyses. Means and medians of continuous periodontal variables and percentage of sites with PD≥5mm or CAL≥4mm were calculated for each subject and group. Considering the frequency distribution of the values above the threshold of detection, undetectable serum IL-6 values were recorded as 0.5 pg/ml. Natural logarithm transformation was applied to non-normally distributed variables. Continuous and dichotomous variables were compared among groups using a two-sample student t-test or χ2 test, respectively. Multivariate linear regression analysis determined predictors of serum IL-6 and CRP. Our transplant group sample size permits detection of an effect size of 0.18 at a p=0.05, in a multivariate analysis with 6 predictors and β≤0.2. Pearson correlation tested associations between serum IL-6, CRP and continuous periodontal variables. Partial correlations test was used to test the association between serum IL-6 and periodontal tissue IL-6 mRNA levels while controlling for diabetes. p≤0.05 was considered statistically significant. 5 iCycler iQ PCR detection system software, Bio-Rad.
Statistical analyses. Means of yellow, brown and friable calli with the six treatments and 10 varieties were calculated and compared by Duncan’s Multiple Range Test and two-way Analysis of Variance (ANOVA)using RStudio and R software edition x64 3.4.4 software. Plots showing the means of yellow, brown and friable calli as well as those depicting differences in the means with the six treatments were made using RStudio software.Table 7. The callus induction medium types under trial denoted with letters A-F Medium typeConstituentsA½ MS -2.2 g/l MS powder (MS0222), 2 mg/l 2,4-D, 20 g/l sucrose, 2.8 g/l Gelrite, pH 5.7B½ 190-2 Cu (Zhuang and Xu, 1983), 2 mg/L 2,4-D, 20 g/l sucrose, 7 g/lagar, pH 5.8C½ N6 (Chu et al. 1975) with 950 mg/l KNO3 and 825 mg/l NH4NO3, 20 g/l sucrose, 2.8 g/l Gelrite, pH 5.8D½ B5 (Gamborg et al. 1968) powder (1.58 g), 2 mg/l 2,4-D, 20 g/l sucrose,2.8 g/l Gelrite, pH 5.8EA (½ MS -2.2 g/l MS powder (MS0222), 2 mg/l 2,4-D, 20 g/l sucrose, 2.8 g/l Gelrite, pH 5.7), 1.0 g/l KH2PO4, (1.0 g/l L-proline, 1.0 g/l L-asparagine and 0.16 mg/l CuSO4·5H2O) - filter sterilizedF- Control Liu et al. 2015MS- 4.4 g/l MS powder (MS0222), 2 mg/l 2,4-D, 20 g/l sucrose, 2.8 g/l Gelrite, pH 5.7 1.0 g/l KH2PO4, (1.0 g/l L-proline, 1.0 g/l L-asparagine and0.16 mg/l CuSO4·5H2O) – filter sterilized
Statistical analyses. Means and SD were calculated. The relative reliability was calculated using the intraclass correlation coefficients (ICCs). The 95% confidence intervals (CI) were determined and complemented by paired t-test and Bland-Altman graphical plots [22] for limits of agreement (LoA). Standard error of the mean (SEM), effect size (ES) and coefficient of variation (CV) was used as criteria for reliability [23, 24]. For validity analysis, linear regression analyses and Pearson correlation coefficients (r) were used. Statistical significance was set at p≤.05. Statistical analyses were processed using the Package (SPSS 20.0; IBM Corporation, New York, USA).
Statistical analyses. Means were compared by Fully Factorial MANOVA with diet treat- ment as the factor and data on individual larval weight as the depen- dent variable. Statistical significance was determined by the Tukey HSD multiple comparison test (Tukey, 1951). For insect bioassays with multiple doses, POLO-PC was used for probit analyses (Robertson et al.,1980). The Fisher exact test was used to test for significant differences among mortalities (n ¼ 16).
Statistical analyses. Means were calculated for physicochemical properties of soils, composts, amended soils and ion intensities from Py-FIMS. Comparisons between means of ion intensities of compound classes and polydispersity in different treatment were made by a One Way ANOVA test. Multivariate statistical evaluation of Py-FI mass spectra was done by principal com- ponent analysis to test which m/z signals contributed to differences among samples. All statistics were computed using data analysis and graphic software (Origin 8.1G).
Statistical analyses. Means and 95% confidence intervals were obtained from a general linear model for longitudinal measurements with a covariance matrix taking into account the presence of multiple scar sites within a patient and the repeated measures over time. A random patient effect was used to model the correlation between scar sites from the same patient. For the repeated measures over time an unstructured covariance matrix was used. If the distribution of the model residuals was right- skewed, the outcome was log-transformed (natural logarithm), but figures were created after back-transforming to the original scale (in which case they refer to geometric means and their 95% CI).Two different models were used. In the first model the evolution over time was evaluated on all repeated measures. In a second model the post-baseline values were compared between the two groups. The analysis was restricted to post-baseline values and the baseline value and the age of the scar were added as covariates in the model, their effects being allowed to vary over time (by including interactions with time). Hence, the plot depicts the mean value of scar sites of mean age and mean baseline value. The scar age (time in months (mo) between wound closure and baseline assessment) was log-transformed (natural logarithm) to downplay the potential influence of extreme observations.All analyses were performed using SAS software, version 9.2 of the SAS System for Windows.
Statistical analyses. Means and standard deviations were calculated for each of the TCI-R 7 scales and 29 subscales. Their internal consistencies were assessed according to the Cronbach's alpha coefficient. Gender differences in the mean scores of the TCI-R scales and subscales were explored using multivariate analysis of covariance in which age was set as the covariate variable in order to control for its influence. F statistics, p values and effect sizes (partial η2) were estimated. Linear associations among the 7 dimensions of the TCI-R were analyzed using a series of Pearson correlation coefficients.The factor structure of the TCI-R was analyzed through a Principal Component Analysis (PCA) with promax rotation. Temperament and character subscales were factor-analyzed separately because the relationships among the temperament and character dimensions are nonlinear and therefore cannot be adequately specified by the linear assumptions of factor analysis [5,35].Concurrent validity of the TCI-R was examined by calculating the Pearson correlations between the TCI-R dimensions and measures of the Big-Five personality dimensions (IPIP- 50), trait impulsiveness (BIS-11), depressive symptoms (BDI-II), suicidality (SBQ-R), and life satisfaction (SWLS). All statistical analyses were conducted using the SPSS version 19 (SPSS, Chicago, IL). The level of statistical significance was defined as P less than 0.05 (5%).