Abstract The purpose of this study is to know the perspectives and practices of two Mathematics teachers who work in the last years of Elementary School, before and after a professional development process through the teachers’ narratives. We use a qualitative approach, with an interpretative paradigm. The data was collected during the training and in the two years afterwards, through interviews. Data analysis was supported by concepts related to the training and practice of teachers who teach Statistics. The results show that the teachers initially valued teaching focused on mathematical procedures, where the meaning of the statistical concepts was not evidenced. With the training, they reframed their practice, since they began to value the statistics exploratory approach, namely with carrying out statistical investigations. With the undertaking of these investigations, the teachers show practices that favor the development of their students’ statistical literacy.

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

Descriptive and inferential statistics are taught to students in many disciplines. More classroom time is often spent on the theory behind different statistical methods that investigate relationships between variables rather than on how to interpret the results obtained to answer the research question that started the process. While statistical software (such as R, Stata, and SPSS) has made it easier to undertake regression with any dataset, the output produced remains challenging to understand and explain to intended audiences. To address this issue, the author created a 90-minute workshop that teaches students how to read tables of descriptive statistics and linear regression results produced by statistical software. The workshop has been taught each semester at the author’s institution since its creation in the Fall 2022 term, attracting a predominantly graduate student audience. Feedback has been positive thus far, with student requests for additional workshops on reading the results of different statistical models, such as logistic and count regression. Through an explanation of the process and the resources used, this presentation will provide a practical overview of how librarians can teach others how to read descriptive statistics and regression results using a research question and their own experiences working with data to guide them. It will include steps to prepare for designing a statistical literacy workshop. The aim of this presentation is to provide ideas that will help librarians move towards teaching a statistical literacy workshop at their own institutions or help them expand their teaching activities in this area.

In this dataset we described all statistical tests mentioned in the article entitled "In-depth process parameter investigation into a protic ionic liquid pretreatment for 2G ethanol production".

Quantities of the Anderson-Darling statistics and p-values, for S1 (Chol dataset) and S2 (Hep datasets).

The Office of the Inspector General uses CLERS to assist investigators during an investigation. CLERS is a web-based case file management system used to store and process all case-related information and statistics in connection with OIG investigations. When fully operational, investigators will be afforded access to case data generated prior to the implementation of CLERS that may be relevant to ongoing investigations.

This dataset contains counts of offences recorded by the NT Police, categorised by offence type, time period (month), location and (for assault offences) alcohol and domestic violence involvement. Certain types of offences show strong seasonal impacts and numbers show considerable monthly variation, particularly at the regional level. Since implementation of the SerPro data system in November 2023, it has been identified that entry of the data used for crime statistics generally happens later in the investigation process when compared to the previous PROMIS system. This means that monthly data takes longer to settle and may take several months to reflect the actual numbers of offences recorded by police. For this reason, the monthly crime statistics should be reviewed with caution and will be marked as provisional until data collection is substantially complete There has been a break in the crime statistics time series following November 2023, due to the implementation of SerPro. This means that the statistics from December 2023 onwards should not be compared directly to earlier statistics.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.This study addressed the dearth of information about facilitators of transnational organized crime (TOC) by developing a method for identifying criminal facilitators of TOC within existing datasets and extend the available descriptive information about facilitators through analysis of pre-sentence investigation reports (PSRs). The study involved a two-step process: the first step involved the development of a methodology for identifying TOCFs; the second step involved screening PSRs to validate the methodology and systematically collect data on facilitators and their organizations. Our ultimate goal was to develop a predictive model which can be applied to identify TOC facilitators in the data efficiently.The collection contains 1 syntax text file (TOCF_Summary_Stats_NACJD.sas). No data is included in this collection.

In the survey companies were asked about quantitative and qualitative details of their order-to-cash process. The order-to-cash process starts when the order comes in and ends when payment is made by the customer. The shorter this process is, the sooner suppliers receive their money, which is good for their working capital position and usually also for transaction costs.The data could first of all be of use for researchers who want to investigate order-to-cash processes. Second, this data could be used in statistics classes for basic statistical analyses, mainly descriptive statistics due to the amount of answers (116), and to a limited extent inferential statistics. Date Submitted: 2023-04-18 Issued: 2023-04-12

Results of the backward reduction procedure by iteration: Statistics, p-values, and reduction of both studies and patients.

This statistic shows the likelihood of telematics data being used in claims investigations in next three to five years in the United States as of 2017. In 2017, over 70 percent of the senior auto insurance executives surveyed believed that telematics data would be used in claims investigations in the next three to five years.

Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming. We offer a 10-step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data...

This file contains Appendices for the article titled as "A Multiphase Acceptance Sampling Model by Attributes to Investigate the Production Interruptions in Batch Production within Tobacco Industry" published in International Journal of Quality & Reliability Management.

Data on synthetic fuel byproducts, such as Rock Springs data on oil shale retort byproducts and Hanna data on coal gasification byproducts, has been analyzed by various statistical procedures. It is found that standard statistical procedures, which assume Gaussian probability distributions for each concentration variable, are often inadequate. This investigation is the subject of part I of the enclosed report. A discriminated analysis is then proposed in detail to analyze the data where probability distribution functions (pdf’s) are obtained by using the principle of maximum entropy (see part II). Preliminary numerical results from the new procedure are presented and discussed. Recommendations for further studies are also given.

Terms of Use

Please read the terms of use below.

The National Institute of Justice (NIJ) adheres to codes of conduct that are generally accepted in higher education and scientific research for the exchange and proper use of knowledge and information.

These data are distributed under the following terms of use. By continuing past this point to the data retrieval process, you signify your agreement to comply with the requirements stated below:

Privacy and Confidentiality of Data

Any intentional identification of a individuals or unauthorized disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, users of data agree:

• To use these datasets solely for research or statistical purposes and not for investigation of specific individuals • To make no use of the identity of any individual discovered inadvertently, and to advise NIJ of any such discovery (NIJRecidivismChallenge@usdoj.gov)

Federal law and regulations require that research data collected by the U.S. Department of Justice or by its grantees and contractors may only be used for research or statistical purposes. The applicable laws and regulations may be found in the United States Code, 34 USC Section 10231(a), the Code of Federal Regulations, 28 CFR 22, and 62 F.R. 35044 (June 27, 1997) (The Federal Confidentiality Order). Accordingly, any intentional identification or disclosure of a person or establishment may violate federal law as well as the assurances of confidentiality given to the providers of the information. Therefore, users of this data must agree to abide by these regulations and understand that NIJ may report any potential violation to the appropriate entities within the U.S. Department of Justice.

Terms of Use

Please read the terms of use below.

The National Institute of Justice (NIJ) adheres to codes of conduct that are generally accepted in higher education and scientific research for the exchange and proper use of knowledge and information.

These data are distributed under the following terms of use. By continuing past this point to the data retrieval process, you signify your agreement to comply with the requirements stated below:

Privacy and Confidentiality of Data

Any intentional identification of a individuals or unauthorized disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, users of data agree:

• To use these datasets solely for research or statistical purposes and not for investigation of specific individuals • To make no use of the identity of any individual discovered inadvertently, and to advise NIJ of any such discovery (NIJRecidivismChallenge@usdoj.gov)

Federal law and regulations require that research data collected by the U.S. Department of Justice or by its grantees and contractors may only be used for research or statistical purposes. The applicable laws and regulations may be found in the United States Code, 34 USC Section 10231(a), the Code of Federal Regulations, 28 CFR 22, and 62 F.R. 35044 (June 27, 1997) (The Federal Confidentiality Order). Accordingly, any intentional identification or disclosure of a person or establishment may violate federal law as well as the assurances of confidentiality given to the providers of the information. Therefore, users of this data must agree to abide by these regulations and understand that NIJ may report any potential violation to the appropriate entities within the U.S. Department of Justice.

Terms of Use

Please read the terms of use below.

The National Institute of Justice (NIJ) adheres to codes of conduct that are generally accepted in higher education and scientific research for the exchange and proper use of knowledge and information.

These data are distributed under the following terms of use. By continuing past this point to the data retrieval process, you signify your agreement to comply with the requirements stated below:

Privacy and Confidentiality of Data

Any intentional identification of a individuals or unauthorized disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, users of data agree:

• To use these datasets solely for research or statistical purposes and not for investigation of specific individuals • To make no use of the identity of any individual discovered inadvertently, and to advise NIJ of any such discovery (NIJRecidivismChallenge@usdoj.gov)

Federal law and regulations require that research data collected by the U.S. Department of Justice or by its grantees and contractors may only be used for research or statistical purposes. The applicable laws and regulations may be found in the United States Code, 34 USC Section 10231(a), the Code of Federal Regulations, 28 CFR 22, and 62 F.R. 35044 (June 27, 1997) (The Federal Confidentiality Order). Accordingly, any intentional identification or disclosure of a person or establishment may violate federal law as well as the assurances of confidentiality given to the providers of the information. Therefore, users of this data must agree to abide by these regulations and understand that NIJ may report any potential violation to the appropriate entities within the U.S. Department of Justice.

This course is an introduction to the key ideas and principles of the collection, display, and analysis of data to guide you in making valid and appropriate conclusions about the world. We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate. This course will provide an intuitive introduction to applied statistical reasoning, introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields.

Estimates of various low-flow statistics were computed at 51 ungaged stream locations throughout New Jersey during the 2018 water year using methods in the published reports, Streamflow Characteristics and Trends in New Jersey, Water Years 1897-2003 (Watson and others, 2005) and Implementation of MOVE.1, Censored MOVE.1, and Piecewise MOVE.1 Low-Flow Regressions with Applications at Partial-Record Streamgages in New Jersey (Colarullo and others, 2018). The estimates are computed as needed for use in water resources permitting, assessment, and management by the New Jersey Department of Environmental Protection. The data release includes the stream name, location, method of estimation, drainage area, and intended use of the low-flow statistics computed during the 2018 water year. The data are provided as both a plain text file and ArcGIS shapefile format. References for publications cited: Watson, K.M., Reiser, R.G., Nieswand, S.P., and Schopp, R.D., 2005, Streamflow characteristics and trends in New Jersey, water years 1897-2003: U.S. Geological Survey Scientific Investigations Report 2005-5105, 131 p., https://pubs.usgs.gov/sir/2005/5105/pdf/NJsir2005-5105_report.pdf. Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018–1089, 20 p., https://doi.org/10.3133/ofr20181089.

Abstract

The 2015 National Agricultural Survey is a statistical investigation that has as its fundamental purpose to generate up to date information that can be used to construct indicators that facilitate the monitoring and evaluation of the various budgetary programs that fall within the framework of the Budget for Results that the Ministry of Economics and Finance has been implementing in the public sector, and in this way, contribute to the design and orientation of public policies that aim to improve the living standards of agricultural producers. The specific objectives of the 2015 National Agricultural Survey are: - To estimate land-use, the area planted and harvested, the production and yield of the main temporary and permanent crops, milk production, minor species and livestock inventory. - To obtain the necessary information to construct agricultural sector indicators that facilitate the monitoring of existing gaps between medium and small agricultural producers. - To provide information that enables the comparison of indicators of the budgetary programs for small and medium agricultural producers over time.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Universe

The survey covers all agricultural holdings within the country that are less than 50 ha and the agricultural units that are agricultural or farming enterprises.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the selection of the survey sample is made up of statistical information from the IV National Census of Agriculture 2012 (IV CENAGRO).

The total sample of the 2015 National Agricultural Survey is made up of 29,218 agricultural holdings of small and medium producers, 2,359 agricultural holdings of large agricultural producers; and 2,020 agricultural holdings of natural persons of special stratum.

Sampling deviation

From the sampling design, it was expected that a total of 30,577 agricultural holdings would be surveyed, out of which 29,218 are small and medium, and 1,359 are large agricultural holdings (enterprises, poultry farms, pig farms). In the sample, a total of 27,948 agricultural enterprises were surveyed, out of which 26,874 are small and medium and 1,074 belong to large (enterprises, poultry farms, pig farms) agricultural holdings.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

I. Development of the Data Entry System ENA 2015

An Integrated System is developed to provide support to the project. This system contains the following modules:

• Tablet Coverage Module
• Segmentation Module
• Monitoring Module
• Data Transfer Module (Export)
• Basic Consistency Module
• Indicator Calculation Module
• Report Module

II. Information Analysis

This task consisted of evaluating, identifying, and fixing the errors and missing values in variables in the dataset. This task is under the mandate of the National Supervisor and is supervised by a team in charge of data processing and methodology in the headquarters

III. Development and analysis of the quality-check indicators

This task entails the development of methodological rules and procedures through which the Monitoring and Data Entry System generates the specified quality-check indicators, after checking the consistency of the data.

IV. Exporting the Database in a Stata or SPSS format

The microdata is generated in either a Stata or SPSS format.

Response rate

1. The rate of non-response of the small and medium agricultural holdings was 1.6% The non-response rate of small and medium agricultural holdings in the coastal region was 2.0% The non-response rate of small and medium agricultural holdings in the mountainous region was 1.1% The non-response rate of small and medium agricultural holdings in the jungle region was 2.4%

2. The non-response rate of large agricultural units was 1.7%