Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

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It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

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.

The existence of interactive effects of a dichotomous treatment variable on the relationship between the continuous predictor and response variables is an essential issue in biological and medical sciences. Also, considerable attention has been devoted to raising awareness of the often-untenable assumption of homogeneous error variance among treatment groups. Although the procedures for detecting interactions between treatment and predictor variables are well documented in the literature, the corresponding problem of power and sample size calculations has received relatively little attention. In order to facilitate interaction design planning, this article describes power and sample size procedures for the extended Welch test of difference between two regression slopes under heterogeneity of variance. Two different formulations are presented to explicate the implications of appropriate reliance on the predictor variables. The simplified method only utilizes the partial information of predictor variances and has the advantages of statistical and computational simplifications. However, extensive numerical investigations showed that it is relatively less accurate than the more profound procedure that accommodates the full distributional features of the predictors. According to the analytic justification and empirical performance, the proposed approach gives reliable solutions to power assessment and sample size determination in the detection of interaction effects. A numerical example involving kidney weigh and body weigh of crossbred diabetic and normal mice is used to illustrate the suggested procedures with flexible allocation schemes. Moreover, the organ and body weights data is incorporated in the accompany SAS and R software programs to illustrate the ease and convenience of the proposed techniques for design planning in interactive research.

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.

SAS code to reproduce the simulation study and the analysis of the urine osmolarity example. (ZIP)

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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.

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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

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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.

This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It enables researchers to both reproduce the initial results and to undertake further analyses. The publication includes introductory chapters explaining the statistical theories and concepts required to analyse the PISA data, including full chapters on how to apply replicate weights and undertake analyses using plausible values; worked examples providing full syntax in SAS®; and a comprehensive description of the OECD PISA 2003 international database. The PISA 2003 database includes micro-level data on student educational performance for 41 countries collected in 2003, together with students’ responses to the PISA 2003 questionnaires and the test questions. A similar manual is available for SPSS users.

Results from PROC MIXED (SAS) analysis of effects of inoculum origin on plant biomass production of mid-successional plant species relative to the sterilized control treatment.

List of 56 characters used for cluster analysis and their significance levels from univariate test statistics using CANDISC procedure (SAS software).

FunCoup network information for gene SAS in Homo sapiens. SIAS_HUMAN Sialic acid synthase

Abstract (en): The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 94 district and 12 appellate court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. All federal court cases, 1970-2000. 2012-05-22 All parts are being moved to restricted access and will be available only using the restricted access procedures.2005-04-29 The codebook files in Parts 57, 94, and 95 have undergone minor edits and been incorporated with their respective datasets. The SAS files in Parts 90, 91, 227, and 229-231 have undergone minor edits and been incorporated with their respective datasets. The SPSS files in Parts 92, 93, 226, and 228 have undergone minor edits and been incorporated with their respective datasets. Parts 15-28, 34-56, 61-66, 70-75, 82-89, 96-105, 107, 108, and 115-121 have had identifying information removed from the public use file and restricted data files that still include that information have been created. These parts have had their SPSS, SAS, and PDF codebook files updated to reflect the change. The data, SPSS, and SAS files for Parts 34-37 have been updated from OSIRIS to LRECL format. The codebook files for Parts 109-113 have been updated. The case counts for Parts 61-66 and 71-75 have been corrected in the study description. The LRECL for Parts 82, 100-102, and 105 have been corrected in the study description.2003-04-03 A codebook was created for Part 105, Civil Pending, 1997. Parts 232-233, SAS and SPSS setup files for Civil Data, 1996-1997, were removed from the collection since the civil data files for those years have corresponding SAS and SPSS setup files.2002-04-25 Criminal data files for Parts 109-113 have all been replaced with updated files. The updated files contain Criminal Terminations and Criminal Pending data in one file for the years 1996-2000. Part 114, originally Criminal Pending 2000, has been removed from the study and the 2000 pending data are now included in Part 113.2001-08-13 The following data files were revised to include plaintiff and defendant information: Appellate Terminations, 2000 (Part 107), Appellate Pending, 2000 (Part 108), Civil Terminations, 1996-2000 (Parts 103, 104, 115-117), and Civil Pending, 2000 (Part 118). The corresponding SAS and SPSS setup files and PDF codebooks have also been edited.2001-04-12 Criminal Terminations (Parts 109-113) data for 1996-2000 and Criminal Pending (Part 114) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.2001-03-26 Appellate Terminations (Part 107) and Appellate Pending (Part 108) data for 2000 have been added to the data collection, along with corresponding SAS and SPSS setup files and PDF codebooks.1997-07-16 The data for 18 of the Criminal Data files were matched to the wrong part numbers and names, and now have been corrected. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. (1) Several, but not all, of these record counts include a final blank record. Researchers may want to detect this occurrence and eliminate this record before analysis. (2) In July 1984, a major change in the recording and disposition of an appeal occurred, and several data fields dealing with disposition were restructured or replaced. The new structure more clearly delineates mutually exclusive dispositions. Researchers must exercise care in using these fields for comparisons. (3) In 1992, the Administrative Office of the United States Courts changed the reporting period for statistical data. Up to 1992, the reporting period...

Example of the code used to account for statistical significances for phenotype and other variables.

SAS HBA Market Outlook

According to our latest research, the global SAS HBA (Serial Attached SCSI Host Bus Adapter) market size reached USD 1.47 billion in 2024, and it is poised to grow at a CAGR of 7.1% during the forecast period, reaching an estimated USD 2.74 billion by 2033. This robust growth is driven by increasing demand for high-speed and reliable data transfer solutions across data centers, enterprise storage, and server environments. The proliferation of big data analytics, cloud computing, and the expansion of enterprise IT infrastructure are among the primary factors fueling market expansion, as organizations worldwide seek efficient and scalable storage connectivity solutions.

One of the most significant growth factors for the SAS HBA market is the exponential rise in data generation and storage requirements across various industries. With digital transformation initiatives accelerating globally, organizations are investing heavily in advanced storage systems to manage and process vast volumes of data efficiently. SAS HBAs play a crucial role in enabling high-speed, low-latency connections between servers and storage devices, ensuring seamless data flow and robust performance. The growing adoption of cloud-based services, virtualization, and high-performance computing (HPC) further amplifies the need for scalable and reliable storage connectivity, driving the demand for SAS HBA solutions in both enterprise and hyperscale data center environments.

Another critical driver propelling the SAS HBA market is the ongoing evolution of storage technologies and the increasing complexity of enterprise IT infrastructure. As businesses transition from traditional storage architectures to more sophisticated, hybrid, and software-defined storage environments, the need for versatile and high-capacity connectivity solutions has become paramount. SAS HBAs offer backward compatibility, enhanced error correction, and superior scalability compared to legacy solutions, making them an ideal choice for organizations seeking to future-proof their storage investments. The integration of advanced features such as multi-path I/O, improved power management, and support for higher data transfer rates positions SAS HBAs as essential components in modern IT ecosystems.

Furthermore, the surge in demand for mission-critical applications and real-time data processing across sectors such as BFSI, healthcare, manufacturing, and government is accelerating the adoption of SAS HBA solutions. These applications require uninterrupted access to large datasets and depend on the high reliability and performance provided by SAS HBA technology. The increasing prevalence of AI, machine learning, and IoT-driven workloads is also contributing to the marketÂ’s momentum, as these technologies necessitate robust storage connectivity to handle intensive data processing requirements. As a result, vendors are continuously innovating and expanding their product portfolios to cater to the evolving needs of diverse end-users.

In addition to SAS HBAs, Fibre Channel HBA technology is gaining traction as an alternative storage connectivity solution, particularly in environments where high-speed data transfer and low latency are critical. Fibre Channel HBAs are known for their ability to provide dedicated bandwidth and enhanced reliability, making them a preferred choice for mission-critical applications in sectors such as finance, healthcare, and telecommunications. As organizations continue to seek robust and scalable storage solutions, the integration of Fibre Channel HBAs into existing IT infrastructures offers a pathway to achieving optimal performance and efficiency. The growing adoption of this technology underscores the importance of versatile connectivity options in modern data center environments.

From a regional perspective, North America continues to dominate the global SAS HBA market, accounting for the largest revenue share in 2024, followed by Europe and the Asia Pacific. The strong presence of leading technology companies, early adoption of advanced storage solutions, and significant investments in data center infrastructure are key factors supporting North AmericaÂ’s leadership position. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, expanding enterprise IT infrastructure, and increasing investment