Data were measured using one-way variance analysis (ANOVA) and Duncan's Multiple Range Test) through SAS Computer Program (SAS, 2003) . Various mean values (P < 0.05) and other measurements are shown as mean ± SD.
Sas computer program
Manufactured by SAS Institute
Sourced in United States
The SAS Computer Program is a comprehensive software suite for statistical analysis, data management, and predictive modeling. It provides a robust and flexible platform for processing, analyzing, and reporting on large and complex data sets. The core function of the SAS Computer Program is to enable users to access, manipulate, and analyze data, as well as to develop and deploy predictive models, without the need for extensive programming knowledge.
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Lab products found in correlation
6 protocols using sas computer program
1
Statistical Analysis of Experimental Data
2
Demographic Analysis of P. solenopsis
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Development data of P. solenopsis males and females were submitted to homoscedasticity tests and to the analysis of variance (ANOVA) and the means compared by LSD.
The P. solenopsis reproductive data were organized for life table calculations (Silveira et al., 1976) (link). The reproductive rate net values (Ro= Σmx.lx -mx: total eggs/female number; lx: alive individuals/total), gross reproductive, also expressed in units of number of females per female (GRR= n x 0 mx = ∑ generation time (T= mx.lx.x/mx.lx Σ), intrinsic rate (r m = log Ro/T.0.4343), and finite (λ= antilog r m ) rate of increase were calculated. The confidence intervals of the means were estimated with these parameters determined using the SAS computer program (SAS Institute, 2008) applying the Jackknife resampling technique (Maia et al., 2000) (link).
The P. solenopsis reproductive data were organized for life table calculations (Silveira et al., 1976) (link). The reproductive rate net values (Ro= Σmx.lx -mx: total eggs/female number; lx: alive individuals/total), gross reproductive, also expressed in units of number of females per female (GRR= n x 0 mx = ∑ generation time (T= mx.lx.x/mx.lx Σ), intrinsic rate (r m = log Ro/T.0.4343), and finite (λ= antilog r m ) rate of increase were calculated. The confidence intervals of the means were estimated with these parameters determined using the SAS computer program (SAS Institute, 2008) applying the Jackknife resampling technique (Maia et al., 2000) (link).
3
Statistical Analysis of Experimental Parameters
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The mean values of all the parameters were analyzed by one-way analysis of variance (ANOVA) followed by the Duncan's Multiple Range Test. The mean values were considered significantly different when P < 0.05. All statistical analysis were performed by SAS Computer Program. Data are presented as means ± standard deviation.
4
Statistical Analysis of Experimental Data
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Data were measured using one-way variance analysis (ANOVA) and Duncan's Multiple Range Test) through SAS Computer Program (SAS, 2003) . Various mean values (P < 0.05) and other measurements are shown as mean ± SD.
5
Normality Test and ANOVA Analysis
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After testing and confirming the normality of the data through using Kolmogorov-Smirnov test. We analyzed the significant differences among treatment given the application of one-way analysis of variance (ANOVA) and Duncan's Multiple Range Test) via the SAS Computer Program [37] . Significant different mean values (P < 0.05) and other data are displayed as means ± standard deviation.
6
Statistical Analysis of Experimental Data
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Statistical analyses were performed using GLM and REG procedures available in the SAS computer program (SAS, Cary, NC, USA). Comparisons between mean values were carried out using one-way ANOVA and Fisher's least significant difference (LSD) test. For these analyses, P-values <0.05 were considered significant.
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