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Proc univariate

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PROC UNIVARIATE is a statistical procedure in the SAS software suite that provides a comprehensive analysis of the distribution of a single variable. It calculates descriptive statistics, generates graphical displays, and performs tests for normality. The core function of PROC UNIVARIATE is to summarize the characteristics of a dataset in a concise and informative manner.

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Lab products found in correlation

Proc mixed, by SAS Institute (5 mentions) Sas 9, by SAS Institute (5 mentions) Proc glm, by SAS Institute (3 mentions) Mixed model procedure, by SAS Institute (1 mentions) Proc glm of sas version 9, by SAS Institute (1 mentions) Proc glimmix, by SAS Institute (1 mentions) Proc freq, by SAS Institute (1 mentions) Statistical analysis system, by SAS Institute (1 mentions) Sigmaplot 12, by Merck Group (1 mentions) Sas statistical software, by SAS Institute (1 mentions) Mixed model, by SAS Institute (1 mentions)

21 protocols using proc univariate

1

Evaluating Physiological Responses in Cows

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The data were evaluated for normality of residual distribution before analysis (PROC UNIVARIATE; SAS Institute, 2003 ) and all blood variables that were not normally distributed were logarithmically transformed. Data on production variables, plasma metabolites, BCS, and rectal temperature were analyzed using the MIXED MODEL procedure (SAS Institute, 2003 ) for repeated measures according to the following model:
in which, Yijklm is the dependent variable, µ is the average experimental value, Cowi is the random effect of cow, Treatmentj is the fixed effect of treatment j (j = CL or HS), Timek is the fixed effect of time k (k = number of day or week), (Treatment × Time)jk represents the effect of the interaction between treatment and time, Eijkl is the sampling error and eijklm is the error term.
Time (day or week) was modeled as a repeated measurement by using a first-order autoregressive covariance structure which was determined by the lowest Bayesian information criterion. When the interaction between treatment and time was significant (P ≤ 0.05), pair-wise comparisons of the individual means were performed using the Tukey–Kramer test. Differences between treatments were declared significant at P ≤ 0.05 and differences from P > 0.05 to P ≤ 0.10 were considered as trends.
Safa S., Kargar S., Moghaddam G.A., Ciliberti M.G, & Caroprese M. (2018). Heat stress abatement during the postpartum period: effects on whole lactation milk yield, indicators of metabolic status, inflammatory cytokines, and biomarkers of the oxidative stress. Journal of Animal Science, 97(1), 122-132.
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2

Statistical Analysis of Experimental Data

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Data was checked for normality using the Shapiro–Wilk test [58 (link)] (Proc UNIVARIATE, SAS). Percentage and incidence data were arcsine transformed (arcsin (sqrt(x)). Data was then analyzed using Proc GLM of SAS, version 9.4 (SAS Institute, Cary, NC, USA). Significant differences were identified; means were separated by Tukey’s Honestly Significant Difference (HSD) post hoc test (differences were considered significant at α = 0.05). Non-transformed means were used in the results section.
Manickam R., Chen J.R., Sotelo-Cardona P., Kenyon L, & Srinivasan R. (2021). Evaluation of Different Bacterial Wilt Resistant Eggplant Rootstocks for Grafting Tomato. Plants, 10(1), 75.
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3

Genetic Selection in Aquaculture Environments

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Statistical analyses were performed on 19,916 data records (15,003 fish in ponds combined with 4,913 fish in cages) collected over three generations of selection (2010–2012), tracing back to the base population in 2009. Basic statistics given in Table 2 show that the fish were harvested at a similar weight and size in both pond and cage environments. All traits were evaluated for normality before undertaking further analyses and raw data were transformed when appropriate. Exploratory analyses using a general linear model (GLM) and PROC UNIVARIATE (SAS Institute Inc., 2007 ) were undertaken. All body traits followed approximate normal distribution, but square root transformation of body weight was also used to improve normality of the residuals. A series of statistical analyses was conducted separately for each environment as follows:
Nguyen N.H., Hamzah A, & Thoa N.P. (2017). Effects of Genotype by Environment Interaction on Genetic Gain and Genetic Parameter Estimates in Red Tilapia (Oreochromis spp.). Frontiers in Genetics, 8, 82.
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4

Pasture and Genotype Effects on Livestock

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Data were analyzed by the MIXED procedure of Statistical Analyses System (SAS) (49 ) after verifying for outliers and the residue normality by using the Shapiro–Wilk test (PROC UNIVARIATE, SAS Institute). For the analysis, among the 15 different covariance structures tested, the matrix that best fit to the data was chosen based on the lower corrected Akaike information criteria value (AICC) (50 (link)). The model included the effects of two types of pasture and two animals' genotypes and the interaction between pasture and genotypes (2 x 2). The Tukey test was used as the test to separate the means. The effect of periods and area replication was included in the model as random effect. The effects were considered significant at p ≤ 0.05.
Oliveira P.P., Berndt A., Pedroso A.D., Alves T.C., Lemes A.P., Oliveira B.A., Pezzopane J.R, & Rodrigues P.H. (2022). Greenhouse gas balance and mitigation of pasture-based dairy production systems in the Brazilian Atlantic Forest Biome. Frontiers in Veterinary Science, 9, 958751.
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5

Feather Corticosterone Levels in Brooders

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The assumption of normality was assessed using the Kolmogorov-Smirnov test (PROC UNIVARIATE, SAS Inst. Inc., Cary, NC). For the IHC results, it was not possible to achieve a normal distribution to both conditions using transformations, thus a Friedman's two-way non-parametric analysis was performed on the ranked values with age and treatment (brooder vs. control) as main effects, and age × treatment interaction.
Visual inspection of the data indicated a possible outlier in the corticosterone data from the control group; this was statistically confirmed with a Grubbs' test, also called the ESD method (extreme studentized deviate), thus data from one control animal were removed from this analysis. The CORT levels expressed in pg/mg feather were normally distributed following exclusion of this outlier, thus ANOVA analysis was performed. A repeated measures ANOVA was conducted with feather (2 and 8) as repeated measure, and with the main effects age (16 weeks vs. 28 weeks) and treatment (brooder vs. control). The experimental unit was individual animal.
Nordquist R.E., Zeinstra E.C., Dougherty A, & Riber A.B. (2020). Effects of Dark Brooder Rearing and Age on Hypothalamic Vasotocin and Feather Corticosterone Levels in Laying Hens. Frontiers in Veterinary Science, 7, 19.
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6

Pullet Behavior and Corticosterone in Poultry

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The experiment was a randomized complete block design consisting of 2 trials (blocks), with treatments arranged as a 3 (F) x 2 (S) factorial, and room was nested within F. Block significance was tested and removed from the analysis when not significant. Data were checked for normality (Proc Univariate; SAS 9.4, Cary, NC) and normalized using log+1 transformation where needed. Proc Mixed (SAS 9.4, Cary, NC) was used to analyze data with room as the replicate unit for F (5 replications for 30 Hz and 250 Hz; 6 replications for 90 Hz), and pen as the replicate unit for S (3 replicates per S per room per trial). Pullet behavior data were analyzed as repeated measures with age (wk) as the repeating factor. Corticosterone data were analyzed by trial due to the different lab methodologies used for each trial. A Tukey's range test was used to separate the means, and significance was declared when P ≤ 0.05.
McPhee S., Shynkaruk T., Buchynski K., Crowe T, & Schwean-Lardner K. (2024). How does visible light flicker impact laying hen pullet behavior, fear, and stress levels?. Poultry Science, 103(6), 103713.
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7

Light-Flicker Effects on Experimental Units

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Two trials (blocks) were conducted in a randomized complete block design with a 1-way factorial arrangement for light-flicker treatment. Individual rooms were the experimental unit, allowing for 6 replications per flicker treatment. Data were checked for normality (Proc Univariate; SAS 9.4, Cary, NC) and log transformed (log+1) when necessary. Data were tested for block significance, with block removed as a random factor when P ≥ 0.05. An analysis of variance (Proc Mixed; SAS 9.4, Cary, NC) was used to test for differences between group means and a Tukey's range test was completed for mean separation. Differences were considered significant when P ≤ 0.05.
Hammond A., Buchynski K., Shynkaruk T., Brown J., Crowe T, & Schwean-Lardner K. (2024). Do flickering lights impact turkey hen behavior, stress, and fear?. Poultry Science, 103(6), 103699.
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8

Evaluation of Welfare Quality® Exclusions

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Statistical analyses were carried out with SAS® 9.3 (procedure PROC MIXED, SAS Institute Inc., Cary, NC, USA). All parameters were evaluated at herd level by a mixed model with repeated measures. The fixed factors were group (Groups 0, 1 and 2), season (winter, summer) and their interaction (Group x season). A p-value of 0.05 was assumed as the significance limit. To verify the assumption of the models, residuals and homogeneity of variance were checked by the procedure PROC UNIVARIATE (SAS Institute Inc., Cary, NC, USA) and visually.
Three criteria of the WQ® were excluded from the evaluation due to confounding effects (“expression of other behavior” because of circular reasoning, “thermal comfort” because no indicators has been developed, and “ease of movement” because of infinite likelihood, as the same data set for the two seasons was used). Consequentially, the aggregation into WQ® principles or into a WQ® overall score was not performed in our study because of the three missing WQ® criteria.
Wagner K., Brinkmann J., March S., Hinterstoißer P., Warnecke S., Schüler M, & Paulsen H.M. (2017). Impact of Daily Grazing Time on Dairy Cow Welfare—Results of the Welfare Quality® Protocol. Animals : an Open Access Journal from MDPI, 8(1), 1.
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9

Pen Experiment Statistical Analysis

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Each pen was considered as an experimental unit. Data were checked for normality using PROC UNIVARIATE (version 9.4; SAS Institute Inc., Cary, NC) and were analyzed by one-way ANOVA using the PROC GLM (version 9.4; SAS Institute Inc., Cary, NC). Duncan's multiple range test was used to determine means and differences among treatments. The significance level was preset at P < 0.05, and tendency was declared at P < 0.10.
Kim Y.B., Kim D.H., Jeong S.B., Lee J.W., Kim T.H., Lee H.G, & Lee K.W. (2020). Black soldier fly larvae oil as an alternative fat source in broiler nutrition. Poultry Science, 99(6), 3133-3143.
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10

Pen performance evaluation by ANOVA

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Each pen was considered as an experimental unit. Data were initially checked for normality using PROC UNIVARIATE (version 9.4; SAS Institute Inc., Cary, NC, USA) and analyzed by one-way ANOVA using the PROC GLM (version 9.4; SAS Institute Inc., Cary, NC, USA). The Tukey test was used to determine the differences among treatments as a post hoc test after ANOVA. The significance level was preset at p < 0.05.
Jeon Y.S., Kim Y.B., Lee H.G., Park J., Heo Y.J., Chu G.M, & Lee K.W. (2022). Effect of Dietary Organic and Inorganic Sulfur on the Performance of Coccidiosis Vaccine Challenged Broiler Chickens. Animals : an Open Access Journal from MDPI, 12(9), 1200.
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