Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

The focus of this report is to describe the statistical inference procedures used to produce design-based estimates as presented in the 2013 detailed tables, the 2013 mental health detailed tables, the 2013 national findings report, and the 2013 mental health findings report. Thestatistical procedures and information found in this report can also be generally applied to analyses based on the public use file as well as the restricted-use file available through the data portal. This report is organized as follows: Section 2 provides background informationconcerning the 2013 NSDUH; Section 3 discusses the prevalence rates and how they were calculated, including specifics on topics such as mental illness, major depressive episode, and serious psychological distress; Section 4 briefly discusses how missing item responses of variables that are not imputed may lead to biased estimates; Section 5 discusses sampling errors and how they were calculated; Section 6 describes the degrees of freedom that were used when comparing estimates; and Section 7 discusses how the statistical significance of differences between estimates was determined. Section 8 discusses confidence interval estimation, and Section 9 describes how past year incidence of drug use was computed. Finally, Section 10 discusses the conditions under which estimates with low precision were suppressed. Appendix A contains examples that demonstrate how to conduct various statistical procedures documented within this report using SAS® and SUDAAN® Software for Statistical Analysis of Correlated Data (RTI International, 2012) along with separate examples using Stata® software.

Example of a dataset for analyzing the ADR (adr) for the concomitant use of two drugs (d1 and d2) for the listds data.

Each attempt was replicated at least three times, and values of three observations for each point were considered for statistical analysis.*indicated the values are significant at p

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Discover the booming Statistical Analysis Software market! Our in-depth analysis reveals a $55.86B market (2025) projected to reach over $65B by 2033, driven by data analytics adoption and AI integration. Explore market trends, key players (like SAS, IBM, & MathWorks), and future growth projections.

This dataset is about book series. It has 1 row and is filtered where the books is Nonparametric methods in statistics with SAS applications. It features 2 columns including publication dates.

This statistic depicts the distribution of tools used to compile data and present analytics and/or reports to management, according to a marketing survey of C-level executives, conducted in ************* by Black Ink. As of *************, * percent of respondents used statistical modeling tools, such as IBM's SPSS or the SAS Institute's Statistical Analysis System package, to compile and present their reports.

SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

Raw data used for statistical analysis in SAS for: "Evaluating the effect of phenolic compounds as hydrogen acceptors when ruminal methanogenesis is inhibited in vitro: Part 2 - dairy goats" Romero et al. Animal 2023 http://doi.org/https://doi.org/10.1016/j.animal.2023.100789

Pregnancy is a condition of broad interest across many medical and health services research domains, but one not easily identified in healthcare claims data. Our objective was to establish an algorithm to identify pregnant women and their pregnancies in claims data. We identified pregnancy-related diagnosis, procedure, and diagnosis-related group codes, accounting for the transition to International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis and procedure codes, in health encounter reporting on 10/1/2015. We selected women in Merative MarketScan commercial databases aged 15–49 years with pregnancy-related claims, and their infants, during 2008–2019. Pregnancies, pregnancy outcomes, and gestational ages were assigned using the constellation of service dates, code types, pregnancy outcomes, and linkage to infant records. We describe pregnancy outcomes and gestational ages, as well as maternal age, census region, and health plan type. In a sensitivity analysis, we compared our algorithm-assigned date of last menstrual period (LMP) to fertility procedure-based LMP (date of procedure + 14 days) among women with embryo transfer or insemination procedures. Among 5,812,699 identified pregnancies, most (77.9%) were livebirths, followed by spontaneous abortions (16.2%); 3,274,353 (72.2%) livebirths could be linked to infants. Most pregnancies were among women 25–34 years (59.1%), living in the South (39.1%) and Midwest (22.4%), with large employer-sponsored insurance (52.0%). Outcome distributions were similar across ICD-9 and ICD-10 eras, with some variation in gestational age distribution observed. Sensitivity analyses supported our algorithm’s framework; algorithm- and fertility procedure-derived LMP estimates were within a week of each other (mean difference: -4 days [IQR: -13 to 6 days]; n = 107,870). We have developed an algorithm to identify pregnancies, their gestational age, and outcomes, across ICD-9 and ICD-10 eras using administrative data. This algorithm may be useful to reproductive health researchers investigating a broad range of pregnancy and infant outcomes.

Example of a dataset of three patients for the drugds data.

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Increased density and delayed emergence are two major biotic factors in nature that have profound and complex effects on plants. No studies have attempt to compare the responses of plants to the two factors via morphological plasticity, particularly in dynamic patterns. We subjected plants of Abutilon theophrasti to four emergence times and three planting densities and measured and analyzed a number of mass and morphological traits at different growth stages. Across both stages, plants emerged in late spring had the highest total mass, and spring and late-spring plants had higher stem mass allocation than later germinants, but plants with delayed emergence had higher leaf and reproductive mass allocation, more leaves and less lateral roots, but lower stem length, stem and root diameter than early-emerged plants. Plants at high density performed lower in total mass and most other traits, but performed better in stem allocation and length, with shorter petioles and lateral roots, than at lower densities. In competition for resources, plants will prefer stem elongation to leaf/root growth, and even at the cost of reproduction to ensure the survival of the present generations when competition is intense or lethal. By contrast, plants will prefer the investment into leaf and reproductive growth to stem/root growth, for offspring persistence, when shortened lifetime does not threaten the contemporary survival. The contrasting strategies revealed the intelligence of plants in balancing between survival, growth and reproduction, depending on environmental contexts. Methods Experimental design - Two field experiments were conducted in 2007, at the Pasture Ecological Research Station of Northeast Normal University, Changling, Jilin province, China (44°45’ N, 123°45’ E). We collected seeds of A. theophrasti from the small local wild populations near the research station in late August 2006. Seeds were dry stored at -4oC till using for experiments. We tested effects of emergence timing (ET) and population density (PD) on plants in two experiments respectively. The soil conditions of experimental field in the growth season of 2007 were the same as that in former studies: aeolian sandy soil (pH = 8.3) with nutrients availability of organic C 3.1 mg kg–1, available N 21.0 mg kg–1, and available P 1.1 mg kg–1. For effects of emergence timing, we divided the whole plot into twelve 2 × 3 m sub-plots, which were randomly assigned with four ET treatments and three replicates per treatment. Seeds were grown at an inter-planting distance of 10 cm on June 7, June 27, July 17 and August 7, as four ET treatments of spring (ET1), late spring (ET2), summer (ET3) and late summer (ET4). The treatments accorded with the range of emergence timing of A. theophrasti in its nature habitats in northeast of China (Zhou, Wang et al. 2005). Annually, spring occurs between April and June in northeast of China. Changchun, a city in northeast of China, locates in the middle of Northeast China, where spring starts in late April, summer starts in early July, autumn in mid-August and winter in mid-October. In spite of this, the weather between April and May is often chilling, and precipitation is unpredictable. We did not make plants emerge during this period, to avoid severe stress and mortality early in the season, and instead set up early June as the date of spring emergence, and late June as that of late-spring emergence. For effects of population density, the whole plot was divided into nine 2 × 3 m sub-plots, which were assigned with three PD treatments and three replicates per treatment randomly. We labeled the treatments as low, medium and high densities, which were created by growing plants with inter-planting distances of 30, 20 and 10 cm, to reach the target densities of 12.8, 27.5, and 108.5 plants·m-2 respectively. Seeds were grown at initial densities that were a little higher than the target ones on June 7, 2007, the same date of spring-emergence treatment. Most seeds emerged four to five days after planting. When almost all seedlings reached four-leaf stage, they were thinned to the target densities. Plots were hand weeded when necessary and regularly irrigated to prevent drought. Data collection - For each ET treatment, we arranged three to four times of sampling at the stages of early vegetative (EV), vegetative (VE), late vegetative (LV) or early reproductive growth (ER), reproductive (RE) and late reproductive (LR) growth of plants respectively, which were defined according to the patterns of plant biomass allocation. Since plants from different ET treatments had different lengths of life cycles and nonsynchronous developments, the intervals between samplings differed for different treatments as well (Table 1). For individuals that emerged in spring (ET1), we sampled four times since they had a prolonged life cycle; and for those emerged in late spring (ET2), the sampling at day 30 were not available due to small plant sizes. At each stage, five to six individual plants were randomly chosen from each plot, making a maximum total of 6 replicates × 3 blocks × 4 treatments × 4 stages = 288 samplings. For each PD treatment, plants were sampled at 30, 50 and 70 days of growth, which represented vegetative stage, early reproductive stage and middle reproductive stage respectively. For each stage, we also randomly sampled five to six individual plants per replicate per density, making a maximum total of 6 replicates × 3 blocks × 3 treatments × 3 stages = 162 samplings. For each individual plant, the following traits were measured (if applicable): main root length, diameter at the basal of the main root, length and number of lateral roots (above or equal to 1 mm in diameter along the main root), the length of stem, diameter at the base of stem, petiole length and angle, and leaf number (Table 2). Each plant individual was then separated into roots, stems, petioles, laminas, reproductive modules and branches (if there were any), oven-dried at 75oC for two days and weighed. Reproductive modules consisted of flowers and fruits produced along the main stem and branches, and branches included the stems and leaves on branches. The mass and morphological traits of branches were not included in statistical analyses, since plants of many samplings did not grow branches. Total mass and mass allocation traits were calculated. Statistical analysis - Statistical analyses were conducted using SAS statistical software (SAS Institute 9.0 Incorporation, 2002). Traits used for analysis included allocation traits of root, stem, petiole, lamina, reproductive organs and all morphological traits (Table 2). To minimize variance heterogeneity, all data was log-transformed, except for petiole angles and branch angles (square root-transformed), before statistical analysis. For plant total mass, we applied two-way ANOVA to analyze effects of emergence time or density, growth stage and their interactions, and one-way ANOVA to analyze the effects of emergence time or density or growth stage within each or across all of the other treatments. Plant size (e. g. total mass) can have substantial effects on other traits, which may bias the environmental effects. Consequently, for all the other traits, we applied two-way ANCOVA for overall effects of emergence time or density, growth stage and their interactions, and one-way ANCOVAs for effects of emergence time or density within each or across all of stages, with total mass as a covariate. For a given trait, the proportion explained by effects of total biomass (plant size) in variation in response to environments indicates apparent plasticity (McConnaughay and Coleman 1999), and the variation due to environmental (ET or PD) effects after removal of size effects in the trait indicates true plasticity (Weiner 2004). Multiple comparisons used the Least Significance Difference (LSD) method in the General Linear Model (GLM) program, which also produced adjusted mean values and standard errors in one-way ANCOVA. To evaluate the plastic responses of plants in a comprehensive perspective, we also performed standard principal component analyses (PCA) on all traits for each treatment and across all treatments at two stages of day 50 and 70 for both ET and PD experiments, to find out the most contributive traits.

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G protein-coupled receptors (GPCRs) are one of the largest protein families in mammals. They mediate signal transduction across cell membranes and are important targets for the pharmaceutical industry. The G Protein-Coupled Receptors—Sequence Analysis and Statistics (GPCR-SAS) web application provides a set of tools to perform comparative analysis of sequence positions between receptors, based on a curated structural-informed multiple sequence alignment. The analysis tools include: (i) percentage of occurrence of an amino acid or motif and entropy at a position or range of positions, (ii) covariance of two positions, (iii) correlation between two amino acids in two positions (or two sequence motifs in two ranges of positions), and (iv) snake-plot representation for a specific receptor or for the consensus sequence of a group of selected receptors. The analysis of conservation of residues and motifs across transmembrane (TM) segments may guide the design of more selective ligands or help to rationalize activation mechanisms, among others. As an example, here we analyze the amino acids of the “transmission switch”, that initiates receptor activation following ligand binding. The tool is freely accessible at http://lmc.uab.cat/gpcrsas/.

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