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Sas 8.2 statistical package

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SAS 8.2 is a comprehensive statistical software package designed for data analysis, modeling, and reporting. It provides a wide range of statistical procedures and tools to help users explore, analyze, and interpret data. The core function of SAS 8.2 is to enable users to perform advanced statistical analyses, including regression, ANOVA, time series analysis, and multivariate techniques, among others. The software offers a user-friendly interface and powerful programming capabilities to handle complex data structures and generate customized reports.

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Lab products found in correlation

Spss 19.0 for window, by IBM (1 mentions) Prism 5, by GraphPad (1 mentions)

4 protocols using sas 8.2 statistical package

1

Seed Germination Analysis of A. teniussimum

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A two-way analysis of variance (ANOVA) was carried out using SPSS 19.0 for Windows (SPSS Inc., Chicago, IL, USA) to evaluate the influence of T, Ψ, and their interactions on seed germination variables of A. teniussimum. The results showed with mean and SE value of four replicates. All probit analyses of HT and HTT models were fitted in SAS 8.2 statistical package (SAS Institute, Cary, NC, USA) using the PROC PROBIT routine, which employs a maximum-likelihood weighted regression method (Bradford, 1990 (link); Dahal & Bradford, 1994 (link)).
Xiao H., Yang H., Monaco T., Song Q, & Rong Y. (2020). Modeling the influence of temperature and water potential on seed germination of Allium tenuissimum L.. PeerJ, 8, e8866.
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2

Statistical Analysis of Experimental Data

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The results were expressed as mean +/- SEM; the SEM values for these groups were analyzed on the basis of three independent experiments. Parametric variables of normal distribution were analyzed by either a two-tailed Student’s t-test or ANOVA with Bonferroni test as post-hoc test. Results were considered significant at p<0.05. Statistical analysis was performed using the SAS 8.2 statistical package (SAS Institute Inc., USA) and GraphPad Prism 5.01 (GraphPad software Inc., USA).
Leong W., Xu W., Wang B., Gao S., Zhai X., Wang C., Gilson E., Ye J, & Lu Y. (2020). PP2A subunit PPP2R2C is downregulated in the brains of Alzheimer’s transgenic mice. Aging (Albany NY), 12(8), 6880-6890.
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3

Muscle Function and Gait Analysis

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Categorical variables were expressed in numbers and percentages, and continuous variables were reported according to gender as means (and standard deviations, SD) for normally distributed parameters or as median and interquartile range (IQR) for those non-normally distributed.
Since reference values for some muscle function and gait parameters in non-disabled older individuals are different between males and females [23 (link)], a gender-specific analysis was carried out. Scatterplots of data were built to examine the correlation between 4-MM and 4-MA. Then, unadjusted Pearson correlations were calculated, as appropriate. Correlations of 4-MM and 4-MA with hand grip strength and 6MWT were also separately assessed in men and women using the same tests. 4-MA and 4-MM were categorized by using the cut-off point for dismobility syndrome of 0.6 m/sec [7 (link),8 (link)]. The concordance between 4-MM and 4-MA in diagnosing dismobility syndrome was then assessed.
Statistical significance was defined as p≤0.05. SAS 8.2 statistical package (SAS Institute, Inc., Cary, NC, USA) was used for all analyses.
Maggio M., Ceda G.P., Ticinesi A., De Vita F., Gelmini G., Costantino C., Meschi T., Kressig R.W., Cesari M., Fabi M, & Lauretani F. (2016). Instrumental and Non-Instrumental Evaluation of 4-Meter Walking Speed in Older Individuals. PLoS ONE, 11(4), e0153583.
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4

Anemia, IGF-1, and Hemoglobin Associations

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Normally distributed variables are reported as means ± SDs. To approximate normal distributions, log-transformed values for erythropoietin and IL-6 were used in the analysis and back-transformed for data presentation.
Differences in IGF-1 levels and other parameters among female and male subjects with anemia and without anemia as defined according to WHO criteria were tested by an age-adjusted linear regression model.
Hb levels were also categorized into quartiles based on distributions in the female and male study populations. Differences in IGF-1 levels across Hb quartiles were determined by using analysis of variance and tests for trend after adjustment for age.
Backward multiple regression analyses including age, BMI, IGFBP-3, insulin, testosterone, iron, sTfr, ferritin, folic acid, vitamin B12, erythropoietin, IL-6, caloric intake, and creatinine clearance were used to address the associations between IGF-1 and Hb and between IGF-1 and anemia in the 2 sexes.
Differences were considered statistically significant at P<.05. SAS 8.2 statistical package was used for all analyses (SAS Institute, Inc, Cary, NC).
De Vita F., Maggio M., Lauretani F., Crucitti L., Bandinelli S., Mammarella F., Landi F., Ferrucci L, & Ceda G.P. (2015). INSULIN-LIKE GROWTH FACTOR-1 AND ANEMIA IN OLDER SUBJECTS: THE InCHIANTI STUDY. Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 21(11), 1211-1218.
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