Analysis of variance (ANOVA) was carried with Statistix 9.0 (Analytical Software, Tallahassee, FL), the mean values were compared based on the LSD test at P< 0.05 (LSD0.05) and the error bar represented standard error (SD). Statistix 9.0 (Analytical Software, Tallahassee, FL) was used to calculate the Pearson correlation coefficient and Sigmaplot 10.0 (SPSS, PointRichmond, CA) perform graphing.
Statistix 9
Manufactured by SAS Institute
Sourced in United States
Statistix 9 is a comprehensive software package for statistical analysis and data management. It provides a wide range of analytical tools, including descriptive statistics, regression analysis, ANOVA, and more. Statistix 9 is designed to handle a variety of data types and can be used for a broad range of applications.
Automatically generated - may contain errors
Lab products found in correlation
Sigmaplot 10, by IBM (18 mentions)
Sigmaplot 10, by Grafiti LLC (15 mentions)
Sigmaplot 12, by Grafiti LLC (15 mentions)
Spss v21, by IBM (6 mentions)
Prism 9, by GraphPad (6 mentions)
Simca software, by Sartorius (3 mentions)
Matlab r2009a, by MathWorks (3 mentions)
Sigmaplot 14, by Merck Group (3 mentions)
Sigmaplot 11, by Grafiti LLC (3 mentions)
Jmp 8, by SAS Institute (3 mentions)
92 protocols using statistix 9
1
Statistical Analysis of Experimental Data
2
Statistical Analysis of Experimental Parameters
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The value of a given parameter was expressed as the mean of four replicates with standard error (SE) using the SigmaPlot 10.0 software package (SPSS Inc., Chicago, IL, USA). The difference significance of averages was evaluated using the least significant difference (LSD) test at a 5% probability level using the Statistix 9 software package (Analytical software, Tallahassee, FL, USA). Pearson correlation analysis was used to estimate the correlation coefficients among parameters.
3
Intestinal Parasitic Infections Analysis
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The frequencies of the overall cases of parasitic intestinal infections from each capture zone, specific parasite, single and multiple-infected samples, and counting of oocysts or eggs per gram (EPG) were calculated. Chi-square (χ²) was calculated to establish associations between parasitic infections and the analyzed variables, and odds ratios (ORs) were also calculated with 95 % confidence intervals. All statistical calculations were performed using the software Statistix 9® (Analytical Software, Tallahassee, FL, USA).
4
Glyphosate Resistance Analysis in Sorghum laxum
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The LD50 and I50 values, which represent the glyphosate concentrations that caused a 50% decrease in the mortality of plants and/or the activity of the EPSPS, respectively, were determined with nonlinear regression analysis using the percentage data from the dose–response curves. The analysis employed the formula y = [d/1 + (x/g)b] [26 (link)], where y is the parameter under consideration relative to the untreated control, d corresponds to the upper limit, b is the slope of the curve, g corresponds to the LD50 or I50 (inflection point of the curve at the midpoint), and x corresponds to the tested glyphosate concentration analyzed as an independent variable. The resistance factors (RF) for each parameter were calculated as R-to-S ratio (RF = R/S).
Pairwise Student’s t-tests were conducted to compare the data of shikimic acid, 14C-glyphosate absorption and translocation, and EPSPS basal activity between the S and R S. laxum populations, using the software Statistix 9 (Analytical Software, Mckinney, TX, USA). Significance was established at p ≤ 0.05.
Pairwise Student’s t-tests were conducted to compare the data of shikimic acid, 14C-glyphosate absorption and translocation, and EPSPS basal activity between the S and R S. laxum populations, using the software Statistix 9 (Analytical Software, Mckinney, TX, USA). Significance was established at p ≤ 0.05.
5
Quantitative Analysis of PR Protein Levels
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Statistical analysis was performed by two-way ANOVA to test for significant differences among groups. When significant differences existed, ANOVA was followed by the Student-Newman-Keul's multiple range test to compare means. Significance was considered at the 0.05 level. Linear regression was used to evaluate the relationship between PR-A and PR-B protein levels obtained in PR downregulation experiments, as well as between PR-B and phosphorylated residues (pSer294 and pSer400). Linear regression was performed considering the significance of the regression parameters and coefficient of determination (R 2 ). Data from all experiments were analyzed by Statistix 9 (Analytical Software, Tallahassee, FL, USA).
6
Categorical Data Analysis of Diseased Dogs
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All data analyzed were categorical and thus summarized with percentages. Comparisons between diseased and SEC dogs were made through Fisher exact test, since at least 1 cell in all comparisons had an expected number <5. Dogs with missing data for particular variables were omitted from analysis involving those variables. Statistical tests were performed with Statistix 9 (Analytical Software, Tallahassee, Florida). P .05 was considered significant.
7
Analysis of Adult Emergence Patterns
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All statistical analyses were performed using the Statistix 9 package (Analytical Software 2008). The number of adults emerging in both experiments were analyzed by ANOVA for repeated measures, where cage effects were considered random and the response variable was calculated as the sum of terms for overall mean, treatment effect, time effect, and treatment*time interaction. Sphericity was tested with the response equal to the treatment*cage*time interaction. Mean adult emergence per plot was analyzed by 1-way ANOVA, separately for each experiment, and means were separated by Fisher's LSD test (α = 0.05) when more than 2 groups were compared.
8
Cadmium and Phosphorus Effects on Plants
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For each experiment, we designed a model speci c for a split-split plot with Cd concentrations (the main plot) and P levels (subplot). The six plants from three different plastic pots of FP36 were used as three independent biological replicates, and the results were presented as means ± standard deviation (SD).
The differences in the obtained data from the treatments were analyzed using analysis of variance (ANOVA), followed by least signi cant difference (LSD) tests (P < 0.05), which were performed in Statistix 9 (Analytical Software Tallahassee, FL).
The differences in the obtained data from the treatments were analyzed using analysis of variance (ANOVA), followed by least signi cant difference (LSD) tests (P < 0.05), which were performed in Statistix 9 (Analytical Software Tallahassee, FL).
9
Varietal Differences and Correlations Analysis
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One-way analysis of variance (ANOVA) was used to assess varietal differences in each parameter using Statistix 9 software (Analytical Software, Tallahassee, FL, USA). Linear regression analysis was performed to test the correlations between parameters using SigmaPlot 10 (Systat Software Inc., CA, USA).
10
Multivariate Analysis of PGPR-Influenced Rice
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Two-way ANOVA together with LSD values at a 1% probability level [73 ] was used for analyzing collected data using Statistix 9 (Analytical Software, Inc., Tallahassee, FL, USA). Principal component analysis (PCA) is a statistical technique that allows easier analysis of a large dataset with visualization by reducing the complexity and noise of the data, and highlighting the most important features and relationships between observed parameters. In this study, the relationships between the growth parameters (shoot and root length and fresh and dry biomass), chlorophyll content, nutrient uptake (N, P, K, Ca, Mg, and Na), antioxidant activity, proline, and 2AP level accumulation of KDML105 rice seedlings as affected by ST-PGPR inoculation were evaluated using PCA. The measured parameters were introduced as variables in the PCA using R 1.2.1335 [74 ].
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