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Statistica 5

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Statistica 5.0 is a comprehensive data analysis and visualization software package developed by StatSoft. It provides a wide range of statistical and analytical tools for researchers and professionals in various fields. The core function of Statistica 5.0 is to enable users to efficiently collect, analyze, and interpret data through its robust set of statistical methods and graphical capabilities.

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Matlab 7, by MathWorks (3 mentions) Labview, by National Instruments (3 mentions) Axioplan 2, by Zeiss (2 mentions) Sigmaplot 12, by Grafiti LLC (1 mentions) Jmp pro 13, by SAS Institute (1 mentions) Microsoft office excel 2007, by IBM (1 mentions) Nis elements software, by Nikon (1 mentions) Nis elements ar analysis software, by Nikon (1 mentions) Microcal origin version 6, by Malvern Panalytical (1 mentions) Prism 5, by GraphPad (1 mentions) Prism 7.0v, by GraphPad (1 mentions) Prism 6, by GraphPad (1 mentions) Prism 7, by GraphPad (1 mentions)

Close spelling variants of this product

Statistica (1482 citations) Statistica 12.5 PL (23 citations) Statistica version 5.0 (19 citations) Statistica v. 5.1 (15 citations) STATISTICA software (version 12.5) (9 citations) Statistica software version 5.0 (7 citations) Statistica 13.5 PL (5 citations) STATISTICA 5.0 PL (4 citations) STATISTICA 5.1 PL (3 citations) STATISTICA for Windows Release 5.0 B (3 citations)

73 protocols using statistica 5

1

Evaluating and Optimizing Predictive Models

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The adequacy of the models was evaluated by calculating the multiple determination coefficient (R 2 ), the adjusted determination coefficient (R 2 -adj), the lack of fit and the F-and p-values obtained from the analysis of variance (ANOVA) by using Statistica 5.0 software (Statsoft, USA). The validation of the obtained models was performed by direct comparison of predicted and experimental values obtained for the different responses in the optimal conditions. The optimal conditions were determined by applying the Derringer's desirability function, that identifies a combination of the variable levels that maximize a set of dependent variables, using Statistica 5.0 software (Statsoft, USA).
Paucar‐Menacho L.M., Martínez‐Villaluenga C., Dueñas M., Frias J, & Peñas E. (2018). Response surface optimisation of germination conditions to improve the accumulation of bioactive compounds and the antioxidant activity in quinoa. International Journal of Food Science & Technology., 53(2).
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2

Comprehensive Statistical Analysis Methods

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All results are expressed as means ± standard deviation (SD), indicated by error bars. Results shown are supported by at least 3 independent experiments to ensure reproducibility. Statistica 5.11 (StatSoft, Inc.), MATLAB 7.0.1 (MathWorks), and LabVIEW (National Instruments) were used for statistical tests. All p values shown reflect 2-tailed test results, although 1-tailed tests are sufficient when direction of change is known. Significance of survival differences was assessed by Cox-Mantel log-rank tests of full plots.
Nedosekin D.A., Chen T., Ayyadevara S., Zharov V.P, & Shmookler Reis R.J. (2021). Label-free photothermal disruption of cytotoxic aggregates rescues pathology in a C. elegans model of Huntington’s disease. Scientific Reports, 11, 19732.
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3

Statistical Rigor in Experimental Data

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Results are expressed as means±s.d. and the confidence interval of at least three independent experiments (P > 0.95). Statistica 5.11 (StatSoft, Inc.), MATLAB 7.0.1 (MathWorks) and LabVIEW (National Instruments) were used for the statistical calculations. Data were summarized as the mean, s.d., relative s.d., median and full range. The error bars in figures represent s.d. In the selected figures, we only indicated the average s.d. in the caption to provide more clear data presentation.
Galanzha E.I., Weingold R., Nedosekin D.A., Sarimollaoglu M., Nolan J., Harrington W., Kuchyanov A.S., Parkhomenko R.G., Watanabe F., Nima Z., Biris A.S., Plekhanov A.I., Stockman M.I, & Zharov V.P. (2017). Spaser as a biological probe. Nature Communications, 8, 15528.
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4

Standardized Statistical Analysis

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Results are expressed as the mean ± standard deviation (SD), and the the confidence interval was determined with at least three independent experiments (P0.25 0.95). Statistica 5.11 (StatSoft, Inc.), MATLAB 7.0.1 (MathWorks), and LabVIEW (National Instruments) were used for the statistical calculations. In the figures, we indicate only the average SD in the caption to simplify data presentation.
Harrington W.N., Novoselova M.V., Bratashov D.N., Khlebtsov B.N., Gorin D.A., Galanzha E.I, & Zharov V.P. (2019). Photoswitchable Spasers with a Plasmonic Core and Photoswitchable Fluorescent Proteins. Scientific Reports, 9, 12439.
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5

Nudibranch Lipid Composition Analysis

Cited 1 time
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The analysis approach was recently described22 (link). Briefly, significance of differences in mean contents of FAs, lipid classes, and lipid molecular species between nudibranch species was tested by one-way analysis of variance (ANOVA). Raw data were used following evaluation of the homogeneity of variances (Levene’s test) and the normality of data distribution (Shapiro–Wilk test). To represent differences between nudibranch species, the variables (square roots of TL class, or PL class, or FA contents) were included in principal components analyses (PCA). All statistical analyses were performed using STATISTICA 5.1 (StatSoft, Inc., USA). A statistical probability of p < 0.01 was considered significant. Values are represented as mean ± standard deviation.
Imbs A.B, & Grigorchuk V.P. (2019). Lipidomic study of the influence of dietary fatty acids on structural lipids of cold-water nudibranch molluscs. Scientific Reports, 9, 20013.
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6

Statistical Analysis of Experimental Treatments

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Results were expressed as the mean ± S.E.M. In all experiments, statistical differences between several treatments and their respective control were determined by one-way analysis of variance (ANOVA), and when the F value was significant, post hoc differences were determined by the Dunnett’s multiple comparison test. The level of significance was set at p < 0.05. All statistical analyses were performed using the software Statistica 5.1 (StatSoft, Inc.).
Herrera Cano N., Andujar S.A., Theoduloz C., Wunderlin D.A., Santiago A.N., Schmeda-Hirschmann G., Enriz R.D, & Feresin G.E. (2022). Arylated analogues of cypronazole: fungicidal effect and activity on human fibroblasts. Docking analysis and molecular dynamics simulations. Natural Products and Bioprospecting, 12(1), 9.
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7

Effects of Age and Obstacle Height on Gait Parameters

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To examine differences in the mean parameter values among different age groups and obstacle heights for each parameter at the initial contact instant, two-factor analysis of variance (ANOVA) with repeated measures on one factor was used. To compare means among age levels and obstacle heights for each parameter in which significant correlations with the height were observed, we performed an analysis of covariance (ANCOVA) in which height was used as a covariate. Multiple post hoc comparisons were performed using Tukey’s honestly significant differences test if a significant main effect or interaction was identified. A probability level of P < 0.05 was considered statistically significant. STATISTICA 5.1 (StatSoft Inc., Tulsa, USA) was used for all statistical analyses.
Shin S., Demura S., Watanabe T., Yabumoto T., Lee J.H., Sakakibara N, & Matsuoka T. (2015). Age-related and obstacle height-related differences in movements while stepping over obstacles. Journal of Physiological Anthropology, 34(1), 15.
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8

Lipid Profile Comparison and Analysis

Cited 1 time
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The analysis approach was recently described28 (link). In brief, significance of differences between mean contents of DAGE and alkylglycerols was tested by one-way analysis of variance (ANOVA). Raw data were used following evaluation of the homogeneity of variances (Levene’s test) and the normality of data distribution (Shapiro–Wilk test). For the cluster analysis, the unweighted pair-group method with arithmetic mean and the Euclidean distance as dissimilarity metric were applied to the percentage content of molecular species of DAGE and four GPL classes according to the structure of their non-polar parts. All statistical analyses were performed using STATISTICA 5.1 (StatSoft, Inc., USA). A statistical probability of p < 0.01 was considered significant. Values are represented as mean ± standard deviation.
Imbs A.B, & Velansky P.V. (2021). Lipidomic profiling reveals biosynthetic relationships between phospholipids and diacylglycerol ethers in the deep-sea soft coral Paragorgia arborea. Scientific Reports, 11, 21285.
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9

Measuring Algal Photosynthetic Stress

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To monitor the stress intensity caused by Cu treatments in algal tissues, maximum quantum yield of PSII (dark adapted) was measured in one-hour intervals as Fv/Fm using a Walz Phyto-PAM (Waltz, Germany) as previously described [21 (link)]. Since photosynthesis is stress-sensitive, the quantum yield decreased under sub-optimal (stressful) conditions. Statistically significant differences in Fv/Fm values between treatments were determined by a z-adjusted Mann–Whitney U test performed using Statistica 5.1 (Statsoft, USA; p < 0.05).
Ritter A., Dittami S.M., Goulitquer S., Correa J.A., Boyen C., Potin P, & Tonon T. (2014). Transcriptomic and metabolomic analysis of copper stress acclimation in Ectocarpus siliculosus highlights signaling and tolerance mechanisms in brown algae. BMC Plant Biology, 14, 116.
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10

Transcranial Direct Current Stimulation Effects on Motoneurons

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Effects of tDCS were estimated from changes in postsynaptic potentials evoked in individual motoneurons or in ventral root responses evoked by submaximal stimuli applied within the PTs and MLF. Repeated series of 10 or 20 stimuli were delivered at 2–3Hz, alternately to PTs and MLF, or by using overlapping short trains of PT and MLF stimuli. Responses to these stimuli were recorded during and after tDCs, and compared to those evoked before tDCS. The comparison included effects of a brief train of stimuli and it was noted after which stimuli the responses appeared and how their areas and latencies were affected by tDCS. Software for sampling and analysis developed by E. Eide, T. Holmström and N. Pihlgren (University of Gothenburg) was used for measurements of latencies and areas and to calculate the differences between the areas of the tested potentials from their averaged records.
Differences between data sets were assessed for statistical significance by using Student’s t-test, Mann-Whitney Rank Sum Test, z-test or ANOVA with the adequate (Holm-Sidak or Dunn’s) post-hoc tests for determining differences to control (using Statistica 5.1, StatSoft or SigmaPlot 12.5 Systat software INC). For all tests the overall significance level was set at p<0.05.
Bączyk M., Pettersson L.G, & Jankowska E. (2014). Facilitation of ipsilateral actions of corticospinal tract neurons on feline motoneurons by transcranial DC stimulation (tDCS). The European journal of neuroscience, 40(4), 2628-2640.
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