Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

Abstract

The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Universe

All Household members.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 9,000 households

The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.

The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.

In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.

In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.

Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

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

This statistic illustrates the percentage of organizations worldwide that have a project management officer (PMO) in 2020. During the survey, 82 percent of respondents said that they have one or more PMOs.

This statistic shows how organizations use standardized project management practices worldwide in 2018. During the survey, 37 percent of respondents said standardized practices are used by some departments.

The statistic shows the global market size of the IT project and portfolio management (IT PPM) market from 2014 to 2019 and a forecast for 2024. In 2019, The total market size of the global IT project and portfolio management (IT PPM) was at 3.88 billion U.S. dollars.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

The MIUR is the Italian Ministry of Education, University and Research and each year publishes a set of useful information about the University student data. The LOIUS project represents an attempt towards the adoption of semantic standards in publishing statistical data coming from MIUR Government.

NHSDP is a five-year, USAID- and DFID-funded project designed to increase quality of and access to an essential package of health services (ESP) in Bangladesh, especially among poor and under-served rural and urban populations. In order to achieve this, the project will directly engage with the Sujer Hashi network of 26 service delivery NGOs to strengthen the delivery and local ownership of health services through the provision of clinical and organizational technical support and capacity building.

Introduction

Project Management Statistics: Project management is the practice of planning, executing, and overseeing projects to achieve specific goals within a defined timeframe and budget. It involves coordinating resources, managing teams, and ensuring that the project objectives are met efficiently and effectively. In today's fast-paced and ever-evolving business environment, project management has become a critical function across various industries, from IT and construction to healthcare and finance.

It requires a blend of technical skills, leadership, and communication, making it a dynamic and challenging profession that plays a crucial role in the success of modern organisations.

In the PID database, the URL of the data object, such as PDF, video or data set, is stored. This is basically considered a service. A handle software is run to allow us to create these database entries to generate prefixes and suffixes. This process is carried out for many customers in ePIC (Persistent Identifier Consortium for eResearch), where they cooperate for handle servers. What is currently needed is a service on top of handle servers that provides a descriptive overview. An important issue here is that it isn’t sufficient to create PID prefixes and PIDs themselves. It should be easily understandable to the users how many prefixes are created and how many PIDs are in those prefixes. (This project work report was conducted in preparation for the Master thesis and was supervised by Dr. Sven Bingert and Triet Doan)

R code and txt files for climate relation evaluation. All data stems from http://data.giss.nasa.gov/gistemp/ Temperature datahttp://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt CO2 data:ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/flask/surface/co2_mlo_surface-flask_1_ccgg_month.txt Methane data: http://www.esrl.noaa. gov/gmd/obop/mlo/programs/esrl/methane/methane.html

The statistic displays the top reasons behind the failure of software projects, according to developers, worldwide, as of April 2015. When surveyed, 48 percent of developers pointed at changing or poorly documented requirements as one of the leading reasons for software project failure.

The Elementary-Secondary Education Statistics Project (ESESP) is a national survey that enables Statistics Canada to provide information on enrolments (including minority and second language programs), graduates, educators and finance of Canadian elementary-secondary public educational institutions.

To provide the public with a convenient channel for accessing information and expressing opinions, the National Development Council has established the "Public Policy Online Participation Platform - to supervise." It graphically presents government project information with the aim of expanding public participation, showcasing administrative performance, enhancing information value, promoting two-way interaction between the government and the public, and implementing open accountability. The case project data refers to the important annual work items promoted by each agency based on national development plans and annual administrative plans, as well as information on the basic data and implementation of case projects for the past five years provided by various agencies on the government's open data platform.

The Elementary-Secondary Education Statistics Project (ESESP) is a national pilot survey that enables Statistics Canada to provide information on enrolments, graduates, educators and finance of Canadian elementary-secondary public educational institutions. This information is used mainly to meet policy and planning needs in the field of elementary-secondary education. ESESP annually collects aggregate data from each jurisdiction. Specifically, the information on enrolments pertains to the following four programs: regular, minority and second languages, Aboriginal language and special needs education. The information on regular programs is collected by type of programs (regular, upgrading and professional), education sector (youth or adult), grade and sex. The one on minority and second language programs is collected by type program (immersion, as language of instruction, as a subject taught) and by grade. Information on Aboriginal language programs is requested by type of Aboriginal language (immersion, as language of instruction, as a subject taught) and by grade. Finally, data on special needs education are collected by type of disability (sensory, physical and intellectual disabilities -- low incidence disabilities, learning disabilities and behavioural disabilities -- high incidence disabilities, to compensate for the socio-economic status (SES) or other disadvantages), type of class (regular, special) and by sex. The survey also collects data on secondary school graduates by type of program (regular, upgrading and professional), sector (youth and adult), age and sex. Graduation counts rates can be produced from this data. Information pertaining to full-time and part-time educators by age group and sex is also collected. Finally, ESESP also gathers expenditures data pertaining to level of government (school board and other government) and type of expenditures. This data is collected to determine how much is spent in relative detail by school boards and by provincial/territorial total. It also collects expenditures on special needs education programs. The information on elementary-secondary education statistics is used by provincial and territorial departments or ministries of education, national and provincial teachers' and students' associations, school boards, journalists and researchers, as well as international bodies such as OECD and UNESCO. ESESP was first introduced by Statistics Canada in 2003. The goal of this pilot project is to replace the following surveys as the official collection tools for elementary-secondary enrolments, graduates, educators and finance data: Elementary-Secondary School Enrolment Survey (ESSE -- Survey #3128), Minority and Second Language Education -- Elementary and Secondary Levels Survey (Survey #3129), Secondary School Graduates Survey (SSGS -- Survey #5082), Elementary-Secondary Education Staff Survey (ESESS -- Survey #3127)

This is a collection of statistical projects where I used Microsoft Excel. The definition of each project was given by ProfessionAI, while the statistical analysis part was done by me. More specifically: - customer_complaints_assignment is an example of Introduction to Data Analytics where, given a dataset with complaints of customers of financial companies, tasks about filtering, counting and basic analytics were done; - trades_on_exchanges is a project for Advanced Data Analytics where statistical analysis about trading operations where done; - progetto_finale_inferenza is a project about Statistica Inference where, from a toy dataset about the population of a city, inference analysis was made.

Statistics of approved projects of Innovation and Technology Fund

As of January 2025, more than 651,000 projects had been launched worldwide on the American crowdfunding platform Kickstarter. Of these, 41.98 percent were successfully funded, meaning over four out of ten reached their funding goals. The total amount pledged across all projects surpassed 8.5 billion U.S. dollars, with approximately 7.86 billion U.S. dollars representing successfully funded contributions.

Financial overview and grant giving statistics of Project Home Again