In 2020, the banking sector led in terms of data-driven decision making within organizations, with ** percent of respondents indicating as such. Other noteworthy sectors for data-driven decision making within organizations are insurance and telecom.

In 2020, the United States led in terms of data-driven decision making within organizations, with ** percent of respondents indicating as such. Other noteworthy countries for data-driven decision making within organizations are Germany and the United Kingdom.

Further information is available at GitHub (https://github.com/julian-matthews/information-availability-risk)Here we provide R code and complete trial-by-trial data supporting our study of non-instrumental information availability and risky decision-making. Code descriptionexp1_stats.R: statistics for Experiment 1exp2_stats.R: statistics for Experiment 2exp3_stats.R: statistics for Experiment 3group_stats.R: pooled statistics including data from all experimentsinformation-model-comparison.R: model comparison and boostrap simulationsData descriptionBehavioural dataexp_version: the experiment in our study; EXP1, EXP2, or EXP3subject_num: the unique ID for each of 71 participant across out experimentstrial_num: the trial number from 1 to 180information: non-instrumental information availability, reflected in the number of slots with veridical information from 0 to 5stake: the amount of money that can be won or lost on the trial. Fixed at 50 cents for EXP1. Varies from 10 to 50 cents (in 10 cent increments) for EXP2 and EXP3.decision: whether the gamble was accepted (1) or rejected (0). Coded (NA) if no response was made within the 5 second response window.reaction_time: the amount of time it took to make a decisionoutcome: the predetermined outcome of the gamble (if the participants chooses to accept)early_late: in EXP3 only, whether information was available at the earliest or latest opportunity (analysed for information levels 1:4)Modeling dataexp_version: as abovesubject_num: as abovetrial_num: as aboveinformation: as abovedecision: as abovemod2_linear: information value in model 2mod3_entropy: information value in model 3mod4_res_uncertainty: information value in model 4

Provide statistics on the number and amount of tenders awarded by government agencies in the last quarter with the most favorable terms.

This statistic shows the summarized percentage of companies, by industry, which reported that the gathering, analysis, and utilization of data had a high level of significance on decision-making, today and in five years, according to a 2016 survey conducted by PwC. As of 2016, ** percent of industrial manufacturing companies surveyed said that data played a highly significant role in decision-making.

This information is the "Population Age Distribution" statistical monthly indicator data of the "Chiayi City Statistical Database" query system of the Directorate General of Budget, Accounting and Statistics, starting from 2009.

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the numb...

Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation, and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem.

Workflow showing the different steps from data collection, processing, validation to upload to the data portal. Links to indicator development, reporting, communication products and informed decision making are made. You are free to use this workflow to help explain and support data management and sharing.

Dataset

This dataset was created by Sudipta Biswas

Contents

When data and analytics leaders throughout Europe and the United States were asked whether their companies have a corporate initiative to become a more data-driven organization, approximately two-thirds answered that they currently do have an initiative in place, as of 2021. Only 3 percent of those surveyed answered that they do not have an initiative, nor will they implement one in the future.

The Government of PNG through the National Executive Council (NEC) Decision No. 135/2010 deliberated on the lack of core statistics for informed decision-making and evidence-based planning and as a result directed relevant Government departments responsible for producing and using statistics to develop a National Strategy for the Development of Statistics (NSDS) for the country.

The PNGSDS will strengthen the Papua New Guinea Statistical System and support PNG’s development agenda through evidence-based process. It is a cycle that will have two midterm reviews before a major review in 2027.

IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or omitted. One of the attributes is delegated the 'target' and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection.

Financial overview and grant giving statistics of Gradys Decision

Banks, James, Carvalho, Leandro S., and Perez-Arce, Francisco, (2019) "Education, Decision Making, and Economic Rationality." Review of Economics and Statistics 101:3, 428-441.

The data in these files were retrieved from the Nursing Home Compare (NHC) data repository, https://data.medicare.gov/data/nursing-home-compare, on April 26, 2019. The data were compiled from the NHC files ProviderInfo_Download.csv, QualityMsrMDS_Download.csv and QualityMsrClaims_Download.csv.

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center Abstract The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. Introduction The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro

Survey of innovation and business strategy, employee involvement in decision making on task allocation, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2009 to today.

Investigator(s): Harold J. Spaeth, James L. Gibson, Michigan State University This data collection encompasses all aspects of United States Supreme Court decision-making from the beginning of the Warren Court in 1953 up to the completion of the 1995 term of the Rehnquist Court on July 1, 1996, including any decisions made afterward but before the start of the 1996 term on October 7, 1996. In this collection, distinct aspects of the court's decisions are covered by six types of variables: (1) identification variables including case citation, docket number, unit of analysis, and number of records per unit of analysis, (2) background variables offering information on origin of case, source of case, reason for granting cert, parties to the case, direction of the lower court's decision, and manner in which the Court takes jurisdiction, (3) chronological variables covering date of term of court, chief justice, and natural court, (4) substantive variables including multiple legal provisions, authority for decision, issue, issue areas, and direction of decision, (5) outcome variables supplying information on form of decision, disposition of case, winning party, declaration of unconstitutionality, and multiple memorandum decisions, and (6) voting and opinion variables pertaining to the vote in the case and to the direction of the individual justices' votes.Years Produced: Annually