The differences in biomass allocation parameters among central, transition and marginal zones were analyzed by multiple comparison tests. Two-way ANOVA was used to examine the effects of clonal patch size and position within the patch on reproductive characteristics. For the ≤ 5m2 patch size category, transition zone is not available, and its corresponding data were treated as missing values. The relationships between reproductive characteristics and patch area were analyzed with regression analysis, and the best fit model was selected. These data analyses were performed with SPSS 17.0 statistics software. The model Y = βXα was used to analyze the reproductive allometric growth of a tiller. The equation was log transformed as follows: log10{Y} = log10{β} + α log10{X}; where X was biomass of a reproductive tiller, spike biomass or reproductive tiller biomass, and Y was spike biomass, biomass of mature seeds or mature seed biomass, α was the scaling slope and β was the allometric intercept. Slopes and intercepts were determined with Standardized Major Axis (SMA) regression using the SMATR software. Firstly, the slopes were tested to to determine whether they differedfrom 1. The difference in allometric slopes among treatments was tested [7 (link)].
Spss 17.0 statistics software
Manufactured by IBM
Sourced in United States
SPSS 17.0 is a comprehensive statistical software package used for data analysis, data management, and data documentation. It provides a wide range of statistical procedures for analyzing and presenting data. The core function of SPSS 17.0 is to enable users to perform statistical analysis on data, including descriptive statistics, bivariate analysis, regression analysis, and more.
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Lab products found in correlation
46 protocols using spss 17.0 statistics software
1
Allometric Growth Analysis in Clonal Patches
2
Comparison of Near Visual Acuity Measures
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The logMAR VA between ETDRS near chart and TC near charts was compared and analyzed by the one-way repeated measurement analysis of variance (ANOVA). The correlation analysis between the number of strokes of TC characters and the SLFS of TC characters was also performed by Pearson r. Data are presented as mean ± standard deviation. All statistic assessments were two-sided and evaluated at the 0.05 level of significant difference. The discrepancy of the SLFS between ETDRS charts and three groups of TC near charts were scored and converted logMAR units into lines of magnification requirement in commonly-used Snellen acuity chart. Statistical analyses were performing using the SPSS 17.0 statistics software (SPSS lnc., Chicago, IL, USA).
3
Clinicopathologic and Survival Analysis in Cohort
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Comparisons among clinicopathologic features, EBV status, TILs, and TLS were performed by the Pearson Chi-Square test or Fisher’s exact test. Pearson correlation analysis was used to examine the correlation between TILs and TLS. Survival distribution was compared using the Kaplan-Meier method and the log-rank test. Prognostic variables associated with overall survival were examined by univariate analyses using a Cox proportional hazards regression model. Only those variables which were significantly associated with survival were enrolled into multivariate regression analyses. A nomogram was generated by R software 3.3, with the discriminative ability assessed by the concordance index (C-index), which ranges from 0.5 (no discrimination at all) to 1.0 (perfect discrimination). Calibration plots were generated to compare the predicted probability of overall survival with the observed outcome. Furthermore, the precision of survival predictions was evaluated using the area under receiver operating characteristic (ROC) curve (AUC) in the validation cohort. Two-sided P < 0.05 was considered statistically significant. Statistical analyses were performed using SPSS 17.0 statistics software (SPSS Inc., Chicago, IL, USA).
4
Cadmium Uptake and Translocation in Plants
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Data were exhibited as the means ± standard deviations (SDs) of at least three independent biological experiments. Statistical analysis was performed using SPSS 17.0 statistics software. To test significant changes in mRNA relative expression and Cd concentration, time and Cd treatment were regarded as the main factors. Tukey- HSD method was used to correct all P-values of these multiple comparisons. In addition, one asterisk (*) or two asterisk (**), significantly different from control at P = 0.05, 0.01, respectively.
The translocation factor for Cd within a plant was expressed by the concentration in the aerial parts (µg·g−1DW)/the concentration in the roots (µg·g−1DW), which showed the Cd-translocation properties from roots to aerial parts49 (link).
The translocation factor for Cd within a plant was expressed by the concentration in the aerial parts (µg·g−1DW)/the concentration in the roots (µg·g−1DW), which showed the Cd-translocation properties from roots to aerial parts49 (link).
5
Statistical Analysis of Biological Experiments
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Means and standard deviations were calculated based
on at least three biological experiments. Statistical
analyses of the quantitative variables with normal
distribution were carried out using one-way analysis
of variance (ANOVA). For multiple comparisons, we
used Tukey’s test. A mixed-model analysis was used
to compare the variables between time points. P<0.05
was considered to indicate statistical significance.
All of the statistical analyses were performed using
SPSS 17.0 statistics software (SPSS Inc., Chicago, IL,
USA).
on at least three biological experiments. Statistical
analyses of the quantitative variables with normal
distribution were carried out using one-way analysis
of variance (ANOVA). For multiple comparisons, we
used Tukey’s test. A mixed-model analysis was used
to compare the variables between time points. P<0.05
was considered to indicate statistical significance.
All of the statistical analyses were performed using
SPSS 17.0 statistics software (SPSS Inc., Chicago, IL,
USA).
6
Statistical Analysis of Experimental Data
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Statistical analysis was performed using the SPSS 17.0 statistics software. All values in the present study were expressed as mean ± standard deviation (SD) from at least three independent experiments. Statistical analysis was performed using one-way analysis of variance (ANOVA) or Student's t-test to investigate if the differences were significant among the mean values of different groups. P-values of <0.05 are considered significant.
7
Metabolomics Data Analysis Pipeline
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XCMS online [10 (link)] (https://xcmsonline.scripps.edu ) was applied for data preprocessing. First, the raw data from the MS were converted to mzData files and then analyzed using XCMS. The important XCMS parameters are shown below: polarity, positive mode; retention time format, 60 min; ppm, 30; minimum peak width and maximum peak width, 10 and 60, respectively; signal/noise threshold, 6; mzdiff, 0.01; prefilter intensity, 500; and profStep, 0.5. Metabolomics data were preprocessed through peak discrimination, filtering, and alignment, and normalized using probabilistic quotient normalization (PQN), log transformation, and Pareto scaling based on MetaboAnalyst 3.0 [11 (link)] (http://www.metaboanalyst.ca/MetaboAnalyst/ ). Pattern recognition was performed using orthogonal partial least squares discriminant analysis (OPLS-DA) with SIMCA-P 13.0 software (Umetrics, Sweden). Significance analysis was performed on SPSS Statistics17.0 software. Heatmap of specific differential metabolites was visualized using MeV software, and the classification performance (specificity and sensitivity with the highest accuracy) was assessed by the receiver operating characteristic (ROC) curve based on the SPSS 17.0 statistics software.
8
Correlates of T1 Sagittal Angle
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Statistical analyses were performed using SPSS 17.0 statistics software (SPSS Inc., Chicago, IL). Descriptive statistics were listed in the form of mean and standard deviation. T1 sagittal angle and its correlation with radiographic parameters were analyzed by correlation coefficient test. Unadjusted multiple regression analysis was performed to detect primary contributors to T1 sagittal angle using parameters that were significantly correlated with T1 sagittal angle in the correlation coefficients analysis, and adjusted multiple regression analysis was conducted to find out the regression equation using morphologic parameters (maxTK and maxLL) to predict T1 sagittal angle. P<0.05 was selected as significant level.
9
Validating Written Test Protocols
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Written tests were validated by calculating Cronbach’s Alpha. Data of the written tests were analyzed for changes between pre- and post-tests as well as post-test 1 and 2 within the groups of users and non-users using unpaired student’s t-test. To detect differences in the evaluation between NESTOR users and non-users a chi-square test was performed for each question. A p-value less than 0.05 was considered to indicate a significant (< 0.01: highly significant) difference between the observations and the expectations based on the null-hypothesis. Statistical analysis was performed with SPSS® 17.0 statistics software (SPSS Inc., Chicago, IL, USA) and GraphPad Prism®5 (GraphPad Software Inc., San Diego, Ca, USA).
10
Statistical Analysis of Research Data
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Data acquired from only those individuals who participated throughout the study was considered for statistical analysis. Mean ± SEM was calculated for each group. Statistical analysis was performed using ANOVA with Duncun's Post Hoc test for comparisons between groups using SPSS 17.0 Statistics software (SPSS, Chicago, IL, USA). Relations between variables were analyzed by calculating the Pearson product-moment correlation coefficients. P-values<0.05 (two tailed) were considered to be significant. All the raw data was archived in the laboratory and a copy of the same was submitted to central records keeping centre of DIHAR.
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