Absolute number of deaths registered during the reference year and crude death rate, meaning the ratio of the number of deaths during the year to the average population in that year, expressed per 1 000 persons.

Crude birth rate : The ratio of the number of live births during the year to the average population in that year. The value is expressed per 1 000 population. Total fertility rate : Mean number of children that would be born alive to a woman during her lifetime if she were to pass through and survive her childbearing years conforming to the fertility rates by age of a given year. Description copied from catalog.inspire.geoportail.lu.

Definition:The crude birth rate is the annual number of live births per 1,000 population.Method of measurementThe crude birth rate is generally computed as a ratio. The numerator is the number of live births observed in a population during a reference period and the denominator is the number of person-years lived by the population during the same period. It is expressed as births per 1,000 population. Method of estimation:Data are taken from the most recent UN Population Division's "World Population Prospects". Other possible data sources:Population censusHousehold surveysPreferred data sources:Civil registration with complete coverageExpected frequency of data dissemination:Biennial (Two years)Data collected March 5, 2021 from: https://www.who.int/data/maternal-newborn-child-adolescent-ageing/indicator-explorer-new/mca/crude-birth-rate-(births-per-1000-population)

IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.

Data on the proved reserves, production, and drilling of crude oil, lease condensates, natural gas, and natural gas wells. Monthly and annual data available. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The Detailed Mortality - Underlying Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

API Crude Oil Stock Change in the United States increased to 7.10 BBL/1Million in July 4 from 0.68 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Crude birth rates, age-specific fertility rates and total fertility rates (live births), 2000 to most recent year.

Emission factors from large scale fire whirls burning crude oil. Portions of this dataset are inaccessible because: Data created by external organizations. They can be accessed through the following means: Contact the creator of the data, Michael Gollner. Format: Movie data. Velocity data. This dataset is associated with the following publication: Cui, W., J. Dowling, M. Hajilou, M. Huffman, B. Pawar, J. Aurell, Q. Wang, E. Oran, K. Stone, and M. Gollner. Large-Scale Field Experiments on Enhancing In-Situ Burning with Fire Whirls. FUEL. Elsevier Science BV, Amsterdam, NETHERLANDS, 0, (2026).

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Refinery Crude Runs in the United States decreased to -99 Thousand Barrels in July 4 from 118 Thousand Barrels in the previous week. This dataset provides - United States Refinery Crude Runs- actual values, historical data, forecast, chart, statistics, economic calendar and news.

High Frequency Indicator: The dataset contains day-wise compiled data from the year 2003 to till date on the Organization of the Petroleum Exporting Countries (OPEC) international basket price of crude oil

The OPEC basket or OPEC reference basket refers to the weighted mean or average of oil prices that OPEC member countries throughout the world maintain. The basket refers generally to a standard or set reference point for countries that analyze the oil prices and the consistency of the international oil market

Despite their widespread use as predictors of the spot price of oil, oil futures prices tend to be less accurate in the mean-squared prediction error sense than no-change forecasts. This result is driven by the variability of the futures price about the spot price, as captured by the oil futures spread. This variability can be explained by the marginal convenience yield of oil inventories. Using a two-country, multi-period general equilibrium model of the spot and futures markets for crude oil we show that increased uncertainty about future oil supply shortfalls under plausible assumptions causes the spread to decline. Increased uncertainty also causes precautionary demand for oil to increase, resulting in an immediate increase in the real spot price. Thus the negative of the oil futures spread may be viewed as an indicator of fluctuations in the price of crude oil driven by precautionary demand. An empirical analysis of this indicator provides evidence of how shifts in the uncertainty about future oil supply shortfalls affect the real spot price of crude oil.

Browse Crude Oil Futures (CL) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Crude Oil Production in the United States increased to 13468 BBL/D/1K in April from 13450 BBL/D/1K in March of 2025. This dataset provides the latest reported value for - United States Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Deterministic and stochastic are two methods for modeling of crude oil and bottled water market. Forecasting the price of the market directly affected energy producer and water user.There are two software, Tableau and Python, which are utilized to model and visualize both markets for the aim of estimating possible price in the future.The role of those software is to provide an optimal alternative with different methods (deterministic versus stochastic). The base of predicted price in Tableau is deterministic—global optimization and time series. In contrast, Monte Carlo simulation as a stochastic method is modeled by Python software. The purpose of the project is, first, to predict the price of crude oil and bottled water with stochastic (Monte Carlo simulation) and deterministic (Tableau software),second, to compare the prices in a case study of Crude Oil Prices: West Texas Intermediate (WTI) and the U.S. bottled water. 1. Introduction Predicting stock and stock price index is challenging due to uncertainties involved. We can analyze with a different aspect; the investors perform before investing in a stock or the evaluation of stocks by means of studying statistics generated by market activity such as past prices and volumes. The data analysis attempt to identify stock patterns and trends that may predict the estimation price in the future. Initially, the classical regression (deterministic) methods were used to predict stock trends; furthermore, the uncertainty (stochastic) methods were used to forecast as same as deterministic. According to Deterministic versus stochastic volatility: implications for option pricing models (1997), Paul Brockman & Mustafa Chowdhury researched that the stock return volatility is deterministic or stochastic. They reported that “Results reported herein add support to the growing literature on preference-based stochastic volatility models and generally reject the notion of deterministic volatility” (Pag.499). For this argument, we need to research for modeling forecasting historical data with two software (Tableau and Python). In order to forecast analyze Tableau feature, the software automatically chooses the best of up to eight models which generates the highest quality forecast. According to the manual of Tableau , Tableau assesses forecast quality optimize the smoothing of each model. The optimization model is global. The main part of the model is a taxonomy of exponential smoothing that analyzes the best eight models with enough data. The real- world data generating process is a part of the forecast feature and to support deterministic method. Therefore, Tableau forecast feature is illustrated the best possible price in the future by deterministic (time – series and prices). Monte Carlo simulation (MCs) is modeled by Python, which is predicted the floating stock market index . Forecasting the stock market by Monte Carlo demonstrates in mathematics to solve various problems by generating suitable random numbers and observing that fraction of the numbers that obeys some property or properties. The method utilizes to obtain numerical solutions to problems too complicated to solve analytically. It randomly generates thousands of series representing potential outcomes for possible returns. Therefore, the variable price is the base of a random number between possible spot price between 2002-2016 that present a stochastic method.

Crude Oil Rigs in the United States decreased to 424 in July 11 from 425 in the previous week. This dataset provides - United States Crude Oil Rigs- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Stocks of crude oil in the United States increased by 7.07million barrels in the week ending July 4 of 2025. This dataset provides the latest reported value for - United States Crude Oil Stocks Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The 2025 annual OPEC oil price stood at ***** U.S. dollars per barrel, as of May. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

These indicators are designed to accompany the SHMI publication. The SHMI methodology includes an adjustment for admission method. This is because crude mortality rates for elective admissions tend to be lower than crude mortality rates for non-elective admissions. Contextual indicators on the crude percentage mortality rates for elective and non-elective admissions where a death occurred either in hospital or within 30 days (inclusive) of being discharged from hospital are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR) and The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. Due to a problem with the process which links Hospital Episode Statistics (HES) data to the Office for National Statistics (ONS) death registrations data, some in-hospital deaths have been counted as survivals in a small number of trusts. This affects 80 spells in the current time period for Mid and South Essex NHS Foundation Trust (trust code RAJ) meaning that the number of observed deaths has been underestimated and so the results for this trust should be interpreted with caution. For the other trusts, the number of affected spells is 5 or fewer and so the impact will be small. 6. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.