Sigmastat 3

Manufactured by IBM

Sourced in United States, Germany

SigmaStat 3.0 is a statistical analysis software package developed by IBM. It provides a comprehensive set of tools for data analysis and statistical modeling. The software is designed to assist researchers, scientists, and professionals in various fields to analyze their data and draw insights from it.

Automatically generated - may contain errors

Lab products found in correlation

Sigmaplot 8, by IBM (3 mentions) Nanodrop 2000, by Thermo Fisher Scientific (2 mentions) Prism 8, by GraphPad (2 mentions) Sigma plot 11, by IBM (1 mentions) Gel doc xr, by Bio-Rad (1 mentions) Cfx real time pcr detection system, by Bio-Rad (1 mentions) Minibest universal rna extraction kit, by Takara Bio (1 mentions) Primescript rt master mix, by Takara Bio (1 mentions) Sybr premix ex taq 2 tli rnaseh plus, by Takara Bio (1 mentions) Sigmastat, by Merck Group (1 mentions) Prism 7, by GraphPad (1 mentions) Spss version 19, by IBM (1 mentions) Spss statistics for window, by IBM (1 mentions) Spss statistics 21, by IBM (1 mentions) Beadarray reader, by Illumina (1 mentions) Trizol reagent, by Thermo Fisher Scientific (1 mentions) Rneasy minelute cleanup kit, by Qiagen (1 mentions) Ab213430, by Abcam (1 mentions) Anti β actin primary antibody, by Santa Cruz Biotechnology (1 mentions)

Close spelling variants of this product

SigmaStat (163 citations) SigmaStat Version 3.1 (20 citations) SigmaStat for Windows Version 3.5 (8 citations) Sigmastat 3.1 SPSS Inc. software (5 citations) SigmaStat for Windows 3.5 (4 citations) SigmaStat Statistical Software, Version 3.1 (3 citations) SIGMASTAT program version 3.0 (2 citations)

74 protocols using sigmastat 3

Mitochondrial CoQ6 Quantification Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

CoQ₆ quantification was performed using mitochondrial samples
according to previously published methods 28 (link). Densitometry analysis was carried out with a Gel Doc XR+
(Bio-Rad) with Image Lab 4.0 as software analysis. Statistical (t-Student)
analyses were carried out using the Sigmastat 3.0 (SPSS) statistical package.
Mitochondrial DNA integrity was checked in all strains using two ρ⁰strains, JM6 and JM8 strains. All results are expressed as the average ± SD.
Statistical analyses were carried out using the Sigmastat 3.0 (SPSS) statistical
package.

González-Mariscal I., Martín-Montalvo A., Ojeda-González C., Rodríguez-Eguren A., Gutiérrez-Ríos P., Navas P, & Santos-Ocaña C. (2017). Balanced CoQ6 biosynthesis is required for lifespan and mitophagy in yeast. Microbial Cell, 4(2), 38-51.

+ Open protocol

+ Expand

Statistical Comparison of Experimental Means

Check if the same lab product or an alternative is used in the 5 most similar protocols

Means were compared with the Student’s t-test or one-way ANOVA with a pairwise multiple comparison procedure (Holm-Sidak method) using SigmaStat 3.0 (SPSS, Chicago, IL).

Laragione T., Cheng K.F., Tanner M.R., He M., Beeton C., Al-Abed Y, & Gulko P.S. (2015). THE CATION CHANNEL TRPV2 IS A NEW SUPPRESSOR OF ARTHRITIS SEVERITY, JOINT DAMAGE AND SYNOVIAL FIBROBLAST INVASION. Clinical immunology (Orlando, Fla.), 158(2), 183-192.

+ Open protocol

+ Expand

Seroepidemiological Data Analysis Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

The differences in seroepidemiological data among the item numbers of the groups were analysed with a χ² test for independence. Calculating the χ², a Yate's continuity correction was applied. Fisher's exact test was also performed. The calculations were done using GraphPadInStat V2 (GraphPad Software, V2.05a, USA). We applied the commonly used significance level, P < 0.05.
In the second part of our mathematical investigation, a factual amount of serum anti-measles IgG level was analysed. Kolmogorov–Smirnov and Shapiro–Wilk tests for normality were performed using STATISTICA Release 6.0 (StatSoft Inc., USA). In each age group, the probability distribution proved to be skew, not corresponding to the normal distribution. Because of failing normality criterion, a Kruskal–Wallis analysis of variance (ANOVA) on ranks with Dunn's post hoc multiple comparisons were used and performed with SigmaStat 3.0 (SPSS Inc., USA). The significance level of P < 0.05 has been applied, calculating with two-sided probabilities.

Lengyel G., Marossy A., Ánosi N., Farkas S.L., Kele B., Nemes-Nikodém É., Szentgyörgyi V., Kopcsó I, & Mátyus M. (2018). Screening of more than 2000 Hungarian healthcare workers’ anti-measles antibody level: results and possible population-level consequences. Epidemiology and Infection, 147, e7.

+ Open protocol

+ Expand

Seasonal Variation in Male Testosterone

Check if the same lab product or an alternative is used in the 5 most similar protocols

Mean testosterone metabolites were calculated on a monthly basis and compared across months within each male to determine the influence of seasonality on hormone production using Kruskal–Wallis one-way analysis of variance (ANOVA) followed by Dunn's method (SigmaStat 3.0; SPSS Inc., Chicago, IL, USA). Significance was set at 95%.

Khonmee J., Rojanasthien S., Thitaram C., Sumretprasong J., Aunsusin A., Chaisongkram C, & Songsasen N. (2017). Non-invasive endocrine monitoring indicates seasonal variations in gonadal hormone metabolites in dholes (Cuon alpinus). Conservation Physiology, 5(1), cox001.

+ Open protocol

+ Expand

Statistical Analysis of Patient Data

Check if the same lab product or an alternative is used in the 5 most similar protocols

Data are given in mean ± standard error of the mean (SEM). For comparing patient values with control values or the patient value at the time of admission, the data was analyzed using ANOVA (analysis of variance on ranks) for nonparametric data according to Kruskal-Wallis. For analysis of significant differences over the course of time ANOVA was performed using the Student-Newman-Keuls test. For determination of specific differences Dunn's test was performed. The level of significance was set to ≤0.05. For all statistical analysis the Sigma Stat 3.0 software package (SPSS Inc., Chicago, USA) was used.

Postl L.K., Bogner V., van Griensven M., Beirer M., Kanz K.G., Egginger C., Schmitt-Sody M., Biberthaler P, & Kirchhoff C. (2015). Early Dynamics of Cerebrospinal CD14+ Monocytes and CD15+ Granulocytes in Patients after Severe Traumatic Brain Injury: A Cohort Study. Mediators of Inflammation, 2015, 197150.

+ Open protocol

+ Expand

Statistical Analysis of Genetic Variations

Check if the same lab product or an alternative is used in the 5 most similar protocols

In those cases where data did not meet the specific assumptions of normal distribution and equal variance required for parametric analysis, the Wilcoxon- Mann-Whitney Rank-Sum test was employed to compare experimental and age-matched control mice. Likewise, the one-way analysis of variance (ANOVA) and the non-parametric Kruskal-Wallis analysis of variance on ranks were used to compare data for each mouse genotype. Statistical analyses were carried out using Sigmastat 3.0 (SPSS, Inc.) and Kaleidagraph‥ Level of significance for all tests was set at p <0.05 and power was set at 0.8.

Carlson E.C., Chhoun J.M., Grove B.D., Laturnus D.I., Zheng S., Epstein P.N, & Tan Y. (2017). Renoprotection From Diabetic Complications in OVE Transgenic Mice by Endothelial Cell Specific Overexpression of Metallothionein: A TEM Stereological Analysis. Anatomical Record (Hoboken, N.j. : 2007), 300(3), 560-576.

+ Open protocol

+ Expand

Comprehensive Statistical Analysis of Data

Check if the same lab product or an alternative is used in the 5 most similar protocols

All data are presented as mean±SE. Statistical analysis was performed using one–way, two-way analysis of variance (ANOVA), or Student’s t-test with SigmaStat 3.0 software (SPSS, Inc., Chicago, IL), as dictated by experimental design. All the analyses were followed by Student-Newman-Keuls post hoc tests (for all pairwise comparisons) or Dunnett’s post-hoc tests (for multiple comparisons versus a control), as appropriate. Statistical significance was accepted at the 95% confidence level.

Zhao N., Zhuo X., Lu Y., Dong Y., Ahmed M.E., Tucker D., Scott E.L, & Zhang Q. (2016). Intranasal Delivery of a Caspase-1 Inhibitor in the Treatment of Global Cerebral Ischemia. Molecular neurobiology, 54(7), 4936-4952.

+ Open protocol

+ Expand

Adrenal Gland mRNA Expression Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

For qPCR results, the data obtained were analyzed and compared with control values (mean of normal adrenal samples) using the Mann–Whitney Rank Sum test with the SigmaStat 3.0 software package (SPSS, Chicago, IL, USA). Results of other in vitro experiments were analyzed by ANOVA followed by post hoc Tukey test. The results were considered significantly different when the P value was ≤ 0.05.

Nakamura Y., Hattangady N.G., Ye P., Satoh F., Morimoto R., Ito-Saito T., Sugawara A., Ohba K., Takahashi K., Rainey W.E, & Sasano H. (2014). Aberrant gonadotropin-releasing hormone receptor (GnRHR) expression and its regulation of CYP11B2 expression and aldosterone production in adrenal aldosterone-producing adenoma (APA). Molecular and cellular endocrinology, 384(0), 102-108.

+ Open protocol

+ Expand

ICD-10 Diagnosis and Statistical Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

Data processing was based on the documented diagnosis key (according to ICD-10 coding) in the MCS ISYNET (version 6.56.1404 NFAArzt) for outpatients, or on the ICPM search list with the ORBIS Software (QRP version 1.01.02.002) and the digital patient file Pegasos for inpatients.
Statistical analysis was performed with SigmaPlot 8.0 and SigmaStat 3.0 (SPSS Inc, Chicago, USA). Firstly, a test for normal distribution was performed for each record. Depending on whether it was a normally distributed or not normally distributed sample, the specific test was performed, including analysis of variance (ANOVA). The significance level was set at P≤0.05.

Aurich M., Hofmann G.O., Rolauffs B, & Gras F. (2014). Differences in Injury Pattern and Prevalence of Cartilage Lesions in Knee and Ankle Joints: A Retrospective Cohort Study. Orthopedic Reviews, 6(4), 5611.

+ Open protocol

+ Expand

Spectroscopic Analysis of Metabolite Ratios

Check if the same lab product or an alternative is used in the 5 most similar protocols

Spectroscopy data were analyzed using the LCModel method (version 6.2-0X).22 (link) Water scaling and eddy current correction was done. We investigated the metabolites N-acetylaspartate, glutamate, and myo-inositol in ratios to creatine concentration, which all had sufficient quality, with a Cramér-Rao <20%. The Cramér-Rao is a reliability measurement and is the estimated standard deviation in percent of the estimated concentration.23 (link) Metabolite concentrations were measured as mM and ratios were calculated from metabolite concentrations. Figure 2 shows an example of the metabolite spectrum. Metabolites were fitted in the chemical shift range of 0.1–4.0 ppm.
One-way repeated measurement analysis of variance was done in SigmaStat 3.0 (SPSS Inc., Chicago, IL, USA) to test for differences in metabolite concentrations and pain intensity ratings. Pain intensity during treatment was also calculated as change compared with ratings before treatment (during treatment/before treatment ×100%). Data are presented as the mean ± standard deviation. The significance level was set at P=0.05.

Hansen T.M., Olesen A.E., Simonsen C.W., Drewes A.M, & Frøkjær J.B. (2014). Cingulate metabolites during pain and morphine treatment as assessed by magnetic resonance spectroscopy. Journal of Pain Research, 7, 269-276.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!