BackgroundIndividual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and FindingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

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.

A survey conducted in April and May 2023 found that 60 percent of the companies that do business in the United States find it challenging to track the status of the data privacy legislation and the differences between state laws when preparing for changes in the data privacy laws. The challenge for around 50 percent of the respondents were increasing their budget because of the changes.

The data informs from the ballistic strength training feasibility study. Data includes descriptive statistics and pre-test post-test difference reports. The data was analysed with statistical support using R software. A data collection spreadsheet is also included for the scoping review completed as part of the dissertation.

We analyse and compare NBA and Euroleague basketball through box-score statistics in the period from 2000 to 2017. Overall, the quantitative differences between the NBA and Euroleague have decreased and are still decreasing. Differences are even smaller after we adjust for game length and when playoff NBA basketball is considered instead of regular season basketball. The differences in factors that contribute to success are also very small—(Oliver’s) four factors derived from box-score statistics explain most of the variability in team success even if the coefficients are determined for both competitions simultaneously instead of each competition separately. The largest difference is game pace—in the NBA there are more possessions per game. The number of blocks, the defensive rebounding rate and the number of free throws per foul committed are also higher in the NBA, while the number of fouls committed is lower. Most of the differences that persist can be reasonably explained by the contrasts between the better athleticism of NBA players and more emphasis on tactical aspects of basketball in the Euroleague.

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

NBA Rookies Performance Statistics and Minutes Played: 1980-2016

Tracking Basketball Prodigies' Growth and Achievements

By Gabe Salzer [source]

About this dataset

This dataset contains essential performance statistics for NBA rookies from 1980-2016. Here you can find minute per game stats, points scored, field goals made and attempted, three-pointers made and attempted, free throws made and attempted (with the respective percentages for each), offensive rebounds, defensive rebounds, assists, steals blocks turnovers efficiency rating and Hall of Fame induction year. It is organized in descending order by minutes played per game as well as draft year. This Kaggle dataset is an excellent resource for basketball analysts to gain a better understanding of how rookies have evolved over the years—from their stats to how they were inducted into the Hall of Fame. With its great detail on individual players' performance data this dataset allows you to compare their performances against different eras in NBA history along with overall trends in rookie statistics. Compare rookies drafted far apart or those that played together- whatever your goal may be!

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How to use the dataset

This dataset is perfect for providing insight into the performance of NBA rookies over an extended period of time. The data covers rookie stats from 1980 to 2016 and includes statistics such as points scored, field goals made, free throw percentage, offensive rebounds, defensive rebounds and assists. It also provides the name of each rookie along with the year they were drafted and their Hall of Fame class.

This data set is useful for researching how rookies’ stats have changed over time in order to compare different eras or identify trends in player performance. It can also be used to evaluate players by comparing their stats against those of other players or previous years’ stats.

In order to use this dataset effectively, a few tips are helpful:

• Consider using Field Goal Percentage (FG%), Three Point Percentage (3P%) and Free Throw Percentage (FT%) to measure a player’s efficiency beyond just points scored or field goals made/attempted (FGM/FGA).

• Lookout for anomalies such as low efficiency ratings despite high minutes played as this could indicate that either a player has not had enough playing time in order for their statistics to reach what would be per game average when playing more minutes or that they simply did not play well over that short period with limited opportunities.

• Try different visualizations with the data such as histograms, line graphs and scatter plots because each may offer different insights into varied aspects of the data set like comparison between individual years vs aggregate trends over multiple years etc.

  Lastly it is important keep in mind whether you're dealing with cumulative totals over multiple seasons versus looking at individual season averages or per game numbers when attempting analysis on these sets!

Research Ideas

• Evaluating the performance of historical NBA rookies over time and how this can help inform future draft picks in the NBA.
• Analysing the relative importance of certain performance stats, such as three-point percentage, to overall success and Hall of Fame induction from 1980-2016.
• Comparing rookie seasons across different years to identify common trends in terms of statistical contributions and development over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: NBA Rookies by Year_Hall of Fame Class.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | Name | The name of...

Identifying signatures of recent or ongoing selection is of high relevance in livestock population genomics. From a statistical perspective, determining a proper testing procedure and combining various test statistics is challenging. On the basis of extensive simulations in this study, we discuss the statistical properties of eight different established selection signature statistics. In the considered scenario, we show that a reasonable power to detect selection signatures is achieved with high marker density (>1 SNP/kb) as obtained from sequencing, while rather small sample sizes (~15 diploid individuals) appear to be sufficient. Most selection signature statistics such as composite likelihood ratio and cross population extended haplotype homozogysity have the highest power when fixation of the selected allele is reached, while integrated haplotype score has the highest power when selection is ongoing. We suggest a novel strategy, called de-correlated composite of multiple signals (DCMS) to combine different statistics for detecting selection signatures while accounting for the correlation between the different selection signature statistics. When examined with simulated data, DCMS consistently has a higher power than most of the single statistics and shows a reliable positional resolution. We illustrate the new statistic to the established selective sweep around the lactase gene in human HapMap data providing further evidence of the reliability of this new statistic. Then, we apply it to scan selection signatures in two chicken samples with diverse skin color. Our analysis suggests that a set of well-known genes such as BCO2, MC1R, ASIP and TYR were involved in the divergent selection for this trait.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

The purpose of the present study was to verify the pattern of use of statistics in scientific articles published in national journals of the Physical Education area. Thus, all articles published in the 2009-2011triennium of the Physical Education journals stratified at B2 or higher in the current QUALIS CAPES (Field 21) were analyzed. The bibliographic search found 872 articles in the five journals selected, which were classified into no statistics, descriptive statistics and analytical statistics. For the analysis, descriptive statistics was performed and the 95% confidence interval to compare the difference between the proportions and, when necessary, the chi-square test and Logistic Regression. There was a lower proportion of articles with descriptive analysis (7.1%, 95%CI=5.4%-8.8%) compared with those with no statistics (46.3%, 95%CI=42.9%-49.6%) and analytical statistics (46.6%, 95%CI=43.2%-49.9%). The pattern of use of statistical procedures ranged among the five scientific journals and across fields of concentration (Health, Sport, Leisure, Education and Others). The proportion of articles which did not meet the basic assumptions for the use of parametric test was 43.3%. This proportion was not significantly different over the three years analyzed, first and last authors' region of affiliation of or for the first and last authors' degree. The present study points out a worrying scenario regarding the use of statistics in the area of Physical Education, because besides the high amount of work which do not follow the basic assumptions for statistical use , this situation seems to be common regardless of authors' degree, region of affiliation or journal of publication.

Abstract

Amongst the many objectives of the survey were the following: · Measure the extent to which the supply and quality of official statistics satisfy the needs of users. · Examine the strengths and weaknesses of official statistics and identify the areas that need improvement. · Determine how relevant statistical products from the NSS are for informed decision-making by government and business communities, by the education sector; and for informed discussions and debates by the media. · Help GSS formulate actions for the NSS and increase quality of statistical products; help improve packaging of statistical products to be user friendly, and enhance use of statistical information in the country. · Make known the perception of users of statistics on the supply and quality of statistics in terms of reliability, credibility, timeliness and packaging. · Monitor the use of statistics and for examining the perceptions of users of statistics. · Identify misconceptions and help to determine the corrective actions that need to be taken to improve the NSS.

Geographic coverage

National coverage

Analysis unit

Institution, individual

Universe

Several types of institutions or organizations constituted the broad sector, as explained below: · Metropolitan, Municipal and District Assemblies (MMDAs)/Ministries, Department and Agencies (MDAs): includes government ministries; the legislative assembly of the country (parliamentarians) and associated entities, such as public agencies; the central bank (Bank of Ghana) and other government bodies; and district assemblies. · Business community: includes business organizations such as the chamber of commerce, industries and other business entities, association of employers, labour unions, banks and other financial corporations. · Education sector: includes universities and other tertiary institutions, educational institutions at the intermediate levels, such as teacher training colleges, nursing training schools etc. · Media: includes the main media houses in the country such as newspaper, radio and television stations and other media publishing houses writing on economic, societal and political affairs. · International agencies: includes development partners and other international bodies operating within Ghana and dealing with economic and social development issues, providing technical assistance, and donating or administering funds for development. · Civil society: includes key non-governmental organizations, professional associations, religious institutions and political parties. · Individual researchers: These are individuals who collect data from the Ghana Statistical Service for research and other activities.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame of the GUSS was prepared by compiling the names of organizations and individuals who had ever used official statistics or statistical product from all the producers of official statistics from January 2007 to December 2011 across the 10 regions of Ghana. This resulted in master sampling frame of 934 local users of statistics. This included: 1. Ministries, Department and Agencies (MDAs) and Metropolitan, Municipal and District Assemblies (MMDAs) 2. Financial Institutions 3. International Organizations 4. Media Houses 5. Educational/Research Organizations 6. Other Private Enterprises 7. Civil Society and 8. Individual researchers.

By assuming a Z-value of 1.96, an absolute precision of 10 percent and an expected rate of satisfaction of 50 percent, each sector required at least 87 institutions/individuals. This represents about 10 percent of the sample size required nationally to be able to have enough data for detailed analysis for each sector. In determining the total sample size of the survey, it was ensured that each sector had enough representation of statistical users to allow detail analysis per sector.

A one-stage stratified sample design with proportional allocation to size was adopted in selecting the number of users for each sector. The selection procedure of each sector involved the following steps: · arranging Institutions/individuals in each sector in alphabetical order, and · systematically selecting Users in each sector using the systematic sampling method.

Sampling deviation

No deviation from the original sample design was made.

Mode of data collection

Face-to-face [f2f]

Research instrument

A GUSS questionnaire was developed based on a standard template used by other statistics authorities elsewhere. The standard templates were customized to ensure that it was appropriate for Ghana. All the questionnaires were in English and whenever necessary, the interview was conducted in a language of the respondent's choice. The questionnaire was in four sections:

• Section A asked about the respondents' use of official statistics - which statistics they use regularly, the main sources from which they obtain those statistics, what they normally use them for, how long and how often they have been using official statistics.
• Section B looked at the respondents' views on the quality of official statistics in terms of relevance and accuracy, reliability, timeliness of release, frequency of release and accessibility; their overall assessment of the quality of, and level of satisfaction with, official statistics in the country.
• Section C asked the respondents about the quality of service delivery by the GSS including the frequency with which they seek their products and services, methods that they use when seeking those products and services, their views on the official websites.
• Section D gathered information on the respondents, including the organizations for which they worked age, sex, education and contact information.

Cleaning operations

Two office editors checked and prepared the questionnaires for data entry as they arrive from the field.

Response rate

A total of 610 institutions/individuals were selected in the sample, of which 566 completed the interview yielding a response rate of 92.8 percent. The difference between selected and completed interview occurred mainly because some of the selected institutions refused and some could not be traced. In some cases the officer to answer the questions had travelled out of the country and some also stopped the interview midway. Business community recorded the least response rate (71.2%) whiles MDAs and the Media recorded 96.6 percent and 95.9 percent respectively.

For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large number of variables and pathway sizes in genomics data, some of these statistics cannot be computed. However, in metabolomics data, the number of variables and pathway sizes are typically much smaller, making such computations feasible. Of particular interest is being able to detect changes in pathways that may not be detected for the individual variables. We compare the performance of both the p-value methods and multivariate statistics for self-contained tests with an extensive simulation study and a human metabolomics study. Permutation tests, rather than asymptotic results are used to assess the statistical significance of the pathways. Furthermore, both one and two-sided alternatives hypotheses are examined. From the human metabolomic study, many pathways were statistically significant, although the majority of the individual variables in the pathway were not. Overall, the p-value methods perform at least as well as the multivariate statistics for these scenarios.

The data contain records of arrests and bookings for federal offenses in the United States during fiscal year 2022. The data were constructed from the United States Marshals Service (USMS) Capture database. Records include arrests and bookings made by federal law enforcement agencies (including the USMS) and state and local agencies. Justice involved individuals arrested or booked for federal offenses are transferred to the custody of the USMS for processing, transportation, and detention. The Capture system contains data on all justice involved individuals within the custody of the USMS. Variables containing identifying information were either removed, coarsened, or blanked in order to protect the identities of individuals. A primary difference between the 2020 data and previous years is that the 2020 data were subset based upon an admission to custody in the fiscal year. In previous years, arrest date was used to subset the records. Some individuals in the 2022 data will have a missing arrest date. These data are part of a series designed by Abt Associates and the Bureau of Justice Statistics. Data and documentation were prepared by Abt Associates.

This webinar series introduce some research data with a focus on China and discuss the difference from the US data. Each webinar will cover the following topics: (1) data sources, data collection, data category, definition, description, and interpretation; (2) alternative data and derivable data from other data sources, especially some big data sources; (3) comparison of data difference between the US and China; (4) available tools for efficient data analysis; (5) discussions on pros and cons; and (6) data applications in research and teaching.

Note that the eight elephant samples (1 to 8) used in Illumina RAD-sequencing are different from the two.samples (A and B) used in 454 shotgun sequencing.*weighted median statistic such that 50% of the entire assembly is contained in the number of contigs equal.to or greater than this value.#>q20: 0.01% chance that a base was wrongly called.$identified in elephant sample 4.

Goalkeeper and Midfielder Statistics

Leveraging Statistical Data Of Goalkeepers and Midfielders

By [source]

About this dataset

Welcome to Kaggle's dataset, where we provide rich and detailed insights into professional football players. Analyze player performance and team data with over 125 different metrics covering everything from goal involvement to tackles won, errors made and clean sheets kept. With the high levels of granularity included in our analysis, you can identify which players are underperforming or stand out from their peers for areas such as defense, shot stopping and key passes. Discover current trends in the game or uncover players' hidden value with this comprehensive dataset - a must-have resource for any aspiring football analyst!

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How to use the dataset

• Define Performance: The first step of using this dataset is defining what type of performance you are measuring. Are you looking at total goals scored? Assists made? Shots on target? This will allow you to choose which metrics from the dataset best fit your criteria.

• Descriptive Analysis: Once you have chosen your metric(s), it's time for descriptive analysis. This means analyzing the patterns within the data that contribute towards that metric(s). Does one team have more potential assist makers than another? What about shot accuracy or tackles won %? With descriptive analysis, we'll look for general trends across teams or specific players that influence performance in a meaningful way.

• Predictive Analysis: Finally, we can move onto predictive analysis. This type of analysis seeks to answer two questions: what are factors that predict player performance? And which factors are most important when predicting performance? Utilizing various predictive models—ex – Logistic regression or Random forest -we can determine which variables in our dataset best explain a certain metric’s outcome—for example –expected goals per match -and build models that accurately predict future outcomes based on given input values associated with those factors.

By following these steps outlined here, you'll be able to get started in finding relationships between different metrics from this dataset and leveraging these insights into predictions about player performance!

Research Ideas

• Creating an advanced predictive analytics model: By using the data in this dataset, it would be possible to create an advanced predictive analytics model that can analyze player performance and provide more accurate insights on which players are likely to have the most impact during a given season.
• Using Machine Learning algorithms to identify potential transfer targets: By using a variety of metrics included in this dataset, such as shots, shots on target and goals scored, it would be possible to use Machine Learning algorithms to identify potential transfer targets for a team.
• Analyzing positional differences between players: This dataset contains information about each player's position as well as their performance metrics across various aspects of the game (e.g., crosses attempted, defensive clearances). Thus it could be used for analyzing how certain positional groupings perform differently from one another in certain aspects of their play over different stretches of time or within one season or matchday in particular.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: DEF PerApp 2GWs.csv | Column name | Description | |:----------------------------|:------------------------------------------------------------| | Name | Name of the player. (String) | | App. | Number of appearances. (Integer) | | Minutes | Number of minutes played. (Integer) | | Shots | Number of shots taken. (Integer) | | Shots on Target | Number of shots on target. (Integer) ...

Indonesia Consumption: Statistical Differences: Natural Gas data was reported at 1,952.000 TJ in 2017. This records a decrease from the previous number of 8,766.000 TJ for 2016. Indonesia Consumption: Statistical Differences: Natural Gas data is updated yearly, averaging -4,244.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 108,822.000 TJ in 2007 and a record low of -1,789,529.000 TJ in 2008. Indonesia Consumption: Statistical Differences: Natural Gas data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.