Samples were analyzed in this study with three replicates, and the mean ± SD of the data was displayed. Average value and standard deviation were analyzed using SPSS Statistics 23. Pearson’s correlation coefficients analyzed by SPSS Statistics 23 were also applied in this study to identify the relationships among variables. A statistical significance level was determined within 0.05 among all analyses. Nonlinear regression estimation for the model parameters was conducted by Sigma-Plot 10.0 and dynamic fit wizard function. The concentrations parameters in samples were drawn in the figures by using OriginPro 9.0.
Spss statistics 23
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SPSS Statistics 23 is a software package used for statistical analysis. It provides a wide range of statistical and graphical techniques to analyze and visualize data. The core function of SPSS Statistics 23 is to enable users to perform data management, analysis, and presentation tasks.
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Lab products found in correlation
Prism 8, by GraphPad (51 mentions)
Prism 6, by GraphPad (36 mentions)
Prism 7, by GraphPad (33 mentions)
Prism 5, by GraphPad (19 mentions)
Spss statistics for windows version 23, by IBM (17 mentions)
Sas version 9, by SAS Institute (10 mentions)
Spss 23, by IBM (10 mentions)
Prism 9, by GraphPad (9 mentions)
Spss statistics for window, by IBM (7 mentions)
Spss amos 23, by IBM (6 mentions)
Sas 9, by SAS Institute (6 mentions)
Prism 8, by IBM (5 mentions)
Prism v8, by GraphPad (5 mentions)
Prism 7, by IBM (5 mentions)
Origin 2021, by OriginLab (5 mentions)
Unscrambler x version 10, by CAMO Software (4 mentions)
Sigmaplot 12, by Grafiti LLC (4 mentions)
Sas university edition, by SAS Institute (4 mentions)
Excel 2010, by IBM (4 mentions)
Originpro 2021, by OriginLab (4 mentions)
2 711 protocols using spss statistics 23
1
Statistical Analysis of Research Data
2
Cricket Weight and Nutrient Analysis
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The individual weight (g/cricket) and SP (%) were analyzed with a Linear Mixed Model Analysis (LMM), which considered both the irradiation regime and the time (days) as fixed terms and the (irradiation*time) as a random term. The SP (%) did not follow a normal distribution and therefore was normalized with IBM SPSS Statistics 23 (IBM Corp., Armonk, NY, USA) prior to the analysis. The constant parameters of the models, as well as the values of the composition analysis were compared with an Analysis of Variance (ANOVA) with a level of significance of 0.05. Levene’s test was used to test homogeneity. The software used for all analyses was IBM SPSS Statistics 23 (IBM Corp., Armonk, NY, USA). The tables of the output of the statistical analysis are given as supplementary material .
3
Randomized Experimental Design and Analysis
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All experiments were designed using a completely randomized block design (CRBD) with two or three replicates. The data were statistically analyzed using analysis of variance (ANOVA) and Duncan’s multiple range test (p-value < 0.05) by IBM SPSS Statistics 23.0 (IBM, Armonk, NY, USA). Chi-square analysis was used to compare marker segregation ratios of progenies produced after self-pollination of some transgenic lines to expected values using IBM SPSS Statistics 23.0 (IBM, United States).
4
Statistical Analysis of Neurotransmitter Genes
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The statistical analysis was performed using the Statistical Package for the
Social Sciences, version 23.0 for Windows (IBM SPSS Statistics 23.0). The
figures were performed using GraphPad Prism 7 and IBM SPSS Statistics 23.0
software. The normality assumption was conducted by the Shapiro–Wilk test.
Differences between the two independent groups were evaluated using the
nonparametric Mann–Whitney U test. To investigate the SLC5A8
(Slc5a8 gene) and NKCC1 (Slc12a2 gene) RNA
expression changes in the VPA-treated and control groups, the threshold cycle
(CT) value was normalized with the control Gapdh gene, and ΔCT value was
obtained. For the gene expression study, the ΔΔCT (2−ΔΔCT) Livak
method was used to calculate the expression level between the VPA-treated (test)
and the control conditions of the target gene when compared to the reference gene.26 (link)
The Spearman’s rank correlation coefficient (r) was used to assess
relationships between the Slc5a8 and Slc12a2genes (ΔCT values were used). R2 linear was estimated by performing correlation plots. Differences at the
value of p < 0.05 were considered significant.
Social Sciences, version 23.0 for Windows (IBM SPSS Statistics 23.0). The
figures were performed using GraphPad Prism 7 and IBM SPSS Statistics 23.0
software. The normality assumption was conducted by the Shapiro–Wilk test.
Differences between the two independent groups were evaluated using the
nonparametric Mann–Whitney U test. To investigate the SLC5A8
(Slc5a8 gene) and NKCC1 (Slc12a2 gene) RNA
expression changes in the VPA-treated and control groups, the threshold cycle
(CT) value was normalized with the control Gapdh gene, and ΔCT value was
obtained. For the gene expression study, the ΔΔCT (2−ΔΔCT) Livak
method was used to calculate the expression level between the VPA-treated (test)
and the control conditions of the target gene when compared to the reference gene.26 (link)
The Spearman’s rank correlation coefficient (r) was used to assess
relationships between the Slc5a8 and Slc12a2genes (ΔCT values were used). R2 linear was estimated by performing correlation plots. Differences at the
value of p < 0.05 were considered significant.
5
Correlation of Lysine-derived AGEs
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Experiments were conducted in triplicate. Results were expressed as mean ± standard deviation. Statistical significances among the different samples were assessed by IBM SPSS Statistics 23.0 (SPSS Inc., Chicago, IL, USA) using one-way analysis of variance (ANOVA) and Duncan's test. The significant differences were defined at a level of p-value < 0.05. The analysis of the correlations between lysine-derived AGEs and fructoselysine and α-dicarbonyl compounds was conducted on IBM SPSS Statistics 23.0 by Pearson's correlation test, and p-value below 0.05 or p-value below 0.01 demonstrated the different levels of statistical significance.
6
Predicting Urban Plant Diversity Dynamics
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To compare UPD of different types of primary and secondary UFUs, we drew boxplots with AGB and the number of trees, shrubs, herbs, and total UPD in each UFU with the R package “ggplot2” [25 (link)]. We calculated z-scores for all variables using IBM SPSS Statistics 23.0 and removed outliers (z-score greater and lower than 3 and −3, respectively) [26 (link)]. The mean and standard deviation of AGB and the number of trees, shrubs, herbs and total UPD in each UFU were calculated using IBM SPSS Statistics 23 Chicago, USA. Then, we used AGB and UPD of trees, shrubs, and herbs species as dependent variable, and socioeconomic (construction age, housing price, population density), geographical factors (longitude, latitude,) and management factors (maintenance frequency, watering frequency, fertilization frequency) as independent variables. We used stepwise regression analysis and Akaike Information Criterion (AIC) to output the best model information. We first built two models using data from 2015 and 2021. Then, we built a third model to determine if UPD and AGB responses to socioeconomic and management factors had a time lag effect by comparing whether past socioeconomic factors better explain UPD than present socioeconomic factors. We built this model using UPD and AGB data from 2021 and socioeconomic and management factors from 2015.
7
Smoking Effects on Newborn Characteristics
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Data on newborns were included in the database created in IBM SPSS Statistics 23.0. Primary characteristics (obtained directly from the reports on mothers at childbirth) and transformed characteristics (recategorized) were used to process the data. The analysis contains significant findings linked to the obtained empirical data when the vast majority of the findings have been statistically significant. Two groups of mothers were classified according to the smoking habit during pregnancy (smokers and non-smokers) and statistically processed in IBM SPSS Statistics 23.0. In order to compare the frequency of individual variations of properties in the examined groups and subgroups, the χ 2 independence test was used. Continuous variables were processed by comparing the arithmetic mean by Student's t-test. To compare the frequency of social and anamnestic factors, the odds ratio (OR) was used as the ratio of a certain factor present in the smokers and non-smokers populations.
8
Comparative Analysis of Antimicrobial Activities
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The qRT-PCR data were analyzed by one-way analysis of variance followed by Duncan’s multiple comparison test using IBM SPSS Statistics 23 software (IBM SPSS Statistics 23 for Windows, Chicago, IL, USA) and antimicrobial activities were analyzed by one-way analysis of variance followed by Dunnett’s multiple comparison test. The antimicrobial activity of disulfide and linear LEAP-2 was analyzed by the student’s t-test using IBM SPSS Statistics 23 software. The data were expressed as means±standard error of the mean and differences were considered statistically significant at p<0.05.
9
Statistical Analysis of Biological Data
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The comparison of data sets resulting from a minimum of three independent experiments were analyzed by using the GraphPad Prism 6 software package and monitored for significance by applying two-tailed Student's t-tests with P < 0.05 considered significant. The data sets derived from two independent experiments done in biological replicate in HER.TLRKRAB were analyzed by using the IBM SPSS Statistics 23 software package and monitored for significance by employing a three-way ANOVA test with P < 0.05 considered significant. One-way ANOVA combined with Bonferroni tests were used for the statistical analysis of the data corresponding to the screening of the high-specificity Cas9 variants. P < 0.05 was considered significant. The IBM SPSS Statistics 23 software package was used in this analysis.
10
Ecological Factors and Active Ingredients
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The results are presented as the mean ± standard deviation (SD). The data were analysed by one-way ANOVA and Duncan’s multiple comparison test using IBM SPSS Statistics 23 software. Statistical significance was considered as P < 0.05. Pearson’s two-tailed correlation analysis (CA), principal component analysis (PCA), and stepwise multiple linear regression (SMLR) analysis were performed using IBM SPSS Statistics 23 software18 . The complex relationships between ecological factors and active ingredient contents were intuitively displayed by a heat map using HemI 1.0 software28 .
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