单因素方差分析

各组数据均以均数±标准差（x±s）表示，做单因素方差分析及各组间的两两比较。

All data be expressed by mean ± standard deviation ( x ± s ) , and be treatment by one-way ANOVA and compare between every group .

单因素方差分析的结果显示，不同水平的英语学习者在相同信息修正和恰当修正方面存在显著差异。

The results of ANOVA indicate that there exists significant difference in the use of S-repair and A-repair among the subjects at different proficiency levels .

计量资料采用x±s表示，组间比较采用t检验或单因素方差分析。

Measurement data were expressed by x ± s , groups comparison using t-test or ANOVA .

应用SPSS10.0统计软件进行分析，用单因素方差分析进行游离DNA量的比较，以P0.05为差异有显著性。

Difference of cell-free DNA concentrations was analyzed by one-way analysis of variance using SPSS 10.0 software .

用单因素方差分析筛选出的危险因素，再用多因素Logistic回归分析方法对筛选的危险因素进行分析。

The available data were analyzed by monofactorial variance and multivariate Logistic regression mode .

各测定数据以x±s表示，多组间比较用单因素方差分析。

The measured data indicated x ± s that among the groups using single factor analysis of variance .

经单因素方差分析，各浓度组间榄香烯对细胞周期的影响有统计学意义（P0.05）。

By one-way ANOVA , cell cycle distribution had statistical significance among concentration groups ( P0.05 ) .

计量资料采用均数士标准差（X±s）表示，应用单因素方差分析进行统计学分析。

The standard deviation and measurement data mean differences said , the application of single factor analysis of variance statistical analysis .

实验数据用x±s表示，行单因素方差分析，P0.05为差异具有统计学意义。

Experimental data is expressed with x ± s , Statistical analysis using ANOVA , P0.05 as statistically significant difference .

运用单因素方差分析的理论，在SPSS软件对测试结果进行分析。

The testing results were analyzed in SPSS software by using One-Way ANOVA method .

使用GenSpring7.0进行标准化及单因素方差分析，通过Venn图分析差异基因在癌变不同阶段的变化，并进一步通过GO术语进行注释。

The differentially expressed genes were classified using Venn diagram and annotated with gene ontology .

统计学分析采用单因素方差分析、q检验和q′检验。

The data were analyzed statistically by one-way analysis of variance , q - test , q ′ - test .

单因素方差分析F检验表明，除分株数外的4个形态性状在14个居群中均表现为差异极显著。

Variance analysis of single factor indicates that there are significant differences in morphological indexes except branch number in 14 natural populations .

单因素方差分析显示3个地区间多个形态性状存在极显著差异（P0.01）；

ANOVA analysis showed that there was significant difference in most morphological traits among the three localities .

病例各组BG浓度与对照病例组两两比较，各组均有明显统计学差异（单因素方差分析，P0.05），各病例组间BG浓度差异也具有统计学意义（多重比较，P0.05）。

BG concentrations in all cases between groups have statistically significant different ( multiple comparison , p0.05 ) .

组内不同时间点比较采取单因素方差分析、组间比较采取独立样本t检验。p0.05为差异具有统计学意义。

Within group at different time points compared to the single factor variance analysis , comparison between groups take the independent samples t test .

测量不同b值时正常腺体组织、良性病变、恶性病变的表观弥散系数（ADC值），使用单因素方差分析比较三组数据。

Apparent diffusion coefficients ( ADC value ) were measured in normal tissues , benign and malignant lesions and statistical difference was compared among three groups .

应用SPSS10.0统计软件包进行统计分析，统计方法包括t检验，X~2葡萄膜黑色素瘤局部切除预后影响因素及血清肿瘤标记物探讨检验·one一wayAnova单因素方差分析、logistic回归。

The software package SPSS 10.0 was applied for statistical analyses , including t test , x2 test , one-way Anova method of square-deviation ( SD ), and logistic regression .

采用单因素方差分析比较多组间计量资料差异，进一步两两比较采用q检验；

The single factor variance analysis was used to compare the difference of measurement data among multiple groups . Pairwise comparison was performed with q test .

应用单因素方差分析（One&wayAnova）和组间比较采用LSD检验进行统计处理。

Comparisons between groups were tested by One-Way ANOVA analysis and LSD test .

与半年期分类法：对IPO发行量与初始收益率做出了单因素方差分析检验。

With half - year classification : Have made single factor analysis of variance to examine the IPO circulation and initial return ratio .

通过单因素方差分析或两独立样本t检验得出，个人基本资料中文化程度、职业、家庭人均月收入、吸烟状况、合并糖尿病对患者自我效能或相应项目的影响有统计学差异（P0.05或P0.01）。

One-way ANOVA and independent t-test revealed that , education level , occupation , family income , smoking and whether diabetes were related factors of self-efficacy .

对调查结果进行单因素方差分析及多因素多元Logistic回归分析。

Make exchange to check the square bad analysis in single factor in proceeding in result and many the diverse Logistic in factor returns to return the analysis .

该研究主要采用定量分析和定性分析相结合的研究方法，利用SPSS统计软件对回收的数据进行T检验、单因素方差分析、相关分析和回归分析。

The author uses a computer statistical software package SPSS to perform t-test , single factor variance analysis , correlation analysis and regression analysis .

组间差异采用单因素方差分析中的LSD法。方差不齐时用秩和检验中的Mann-whitneyU方法。

One-way ANOVA with LSD or rank sum test with Mann-Whitney U was performed for analysis among groups .

分析数据时采用了描述性统计、单因素方差分析、双因素方差分析、Pearson相关检验等方法。

Descriptive statistics , one-way ANOVA , two-way ANOVA and Pearson correlation coefficient , were employed to analyze the data .

运用信度、效度检验，因子分析，T检验、单因素方差分析，person分析和多元线性回归等方法对收集到的数据进行分析。

The believe function value , validity analysis , factor analysis , T party distribution , single factor variance analysis , person analysis and multi-factors linear regression analysis were used to process the data which is collected by questionnaire .

结果：（1）单因素方差分析表明，不同年龄组的HOMA-IR、HOMA-βcell有显著性差异（P<0.05）。

Results : ( 1 ) ANOVA showed that there were significant differences in Homa-IR and Homa - β cell among different age groups ( P < 0.05 ) .

用单因素方差分析（One-wayAnova）、SNK-q检验及Spearman等级相关分析进行统计处理。

Experimental data were statistically analysed by One-Way ANOVA , SNK-q test and Spearman rank correlation analysis .

对三组CD42a单因素方差分析：P＜0.05，说明三组间至少有两组差异有显著性。

Analysing CD42a with one way ANOVA , we could come to the results : at least two groups of the three were different significantly .