Nobel prize winners basic statistics for the χ > h class, when ck > k (left) and ck < k (right).

1The unit of body weight (BW) is gram.2The unit of average daily weight gain (ADG) is gram per day.

To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, and migration from the March Annual Demographic Supplement and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.

United States US: People Using Basic Drinking Water Services: % of Population data was reported at 99.200 % in 2015. This records an increase from the previous number of 99.195 % for 2014. United States US: People Using Basic Drinking Water Services: % of Population data is updated yearly, averaging 99.174 % from Dec 2005 (Median) to 2015, with 11 observations. The data reached an all-time high of 99.200 % in 2015 and a record low of 99.148 % in 2005. United States US: People Using Basic Drinking Water Services: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

As of 2021, almost three quarters of all production sites of essential active pharmaceutical ingredients for the U.S. market were located outside the United States.

In 2023, sales from premium wineries in the United States shrank by 4.8 percent compared to the previous year. Wine industryWine is classified as alcoholic beverage which goes well with a large variety of occasions: some often choose to serve this classy drink as an aperitif, others see wine as a perfect accompaniment to a multi-course meal, and others again prefer drinking wine while spending time with friends or family.Wine is made from fermented grapes or other fruits. Due to the conditions needed by the fruit that make it, wine requires special climate conditions: wine cultivating areas have to meet requirements and provide a moderate climate, the right amount of sunshine hours and fertile soils. Traditionally, the cultivation of Champagne, an exclusive sparkling wine, is limited to the Champagne region of France. However, some countries use the term also for general sparkling wines not originating from that region.The latest wine production figures listed Italy, France and Spain as the leading wine producing countries worldwide. The United States was ranked fourth, where wine production is primarily concentrated in California.

The United States Environmental Protection Agency (EPA), in cooperation with the States, biennially collects information regarding the generation, management, and final disposition of hazardous wastes regulated under the Resource Conservation and Recovery Act of 1976 (RCRA), as amended. Collection, validation and verification of the Biennial Report (BR) data is the responsibility of RCRA authorized states and EPA regions. EPA does not modify the data reported by the states or regions. Any questions regarding the information reported for a RCRA handler should be directed to the state agency or region responsible for the BR data collection. BR data are collected every other year (odd-numbered years) and submitted in the following year. The BR data are used to support regulatory activities and provide basic statistics and trend of hazardous waste generation and management.

BR data is available to the public through 3 mechanisms. 1. The RCRAInfo website includes data collected from 2001 to present-day (https://rcrainfo.epa.gov/rcrainfoweb/action/main-menu/view). Users of the RCRAInfo website can run queries and output reports for different data collection years at this site. All BR data collected from 2001 to present-day is stored in RCRAInfo, and is accessible through this website. 2. BR data files collected from 1999 - present day may be downloaded directory in zip file format from (https://rcrapublic.epa.gov/rcra-public-export/?outputType=Fixed or https://rcrapublic.epa.gov/rcra-public-export/?outputType=CSV). 3. Historical data collected prior to 1999 may be ordered on CD. Please see contact information in this metadata file to order historical BR data. BR data are typically published in December of the year following their collection. Data must be received by authorized states and EPA regions if a state is not authorized to implement the BR program by March 1st of the year following collection, and are usually published in December of the year following collection. For example, data collected in 2001 would be received by states and EPA regions by March 1, 2002 and states and EPA regions compile the BR data submitted by facilities and load the state data set into RCRAInfo, the system which EPA Headquarters (HQ) manage. Then EPA HQ published the data files around December 2002. Additional information regarding the biennial report data is available here: https://rcrapublic.epa.gov/rcra-public-export/rcrainfo_flat_file_documentation_v5.pdf and here: https://www.epa.gov/hwgenerators/biennial-hazardous-waste-report.

Please note that the update frequency field for this data set indicates annual, but that the true update period is biennial (every other year). There is no selection option for biennial for the update frequency field.

According to the Polish Statistical Office, there were over 37.5 million inhabitants in Poland in 2024, a decrease of 1.79 percent compared to 2004.

Dominican Republic DO: People Using Basic Sanitation Services: Rural: % of Rural Population data was reported at 73.613 % in 2015. This records an increase from the previous number of 73.379 % for 2014. Dominican Republic DO: People Using Basic Sanitation Services: Rural: % of Rural Population data is updated yearly, averaging 71.860 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 73.613 % in 2015 and a record low of 70.106 % in 2000. Dominican Republic DO: People Using Basic Sanitation Services: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Health Statistics. The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, and migration from the March Annual Demographic Supplement and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.

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This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

Financial overview and grant giving statistics of Veterans of Foreign Wars of the US Basic Post 3848

This table contains data for gross domestic product (GDP), in current dollars, for all census metropolitan area and non-census metropolitan areas.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Colombia Industrial Sales Index: Real: Mfg: Chemical Products: Basic data was reported at 191.310 2001=100 in Dec 2014. This records a decrease from the previous number of 205.210 2001=100 for Nov 2014. Colombia Industrial Sales Index: Real: Mfg: Chemical Products: Basic data is updated monthly, averaging 173.705 2001=100 from Jan 2001 (Median) to Dec 2014, with 168 observations. The data reached an all-time high of 243.170 2001=100 in Mar 2011 and a record low of 88.680 2001=100 in Jan 2002. Colombia Industrial Sales Index: Real: Mfg: Chemical Products: Basic data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.C005: Industrial Sales Index: Manufacturing: Real: 2001=100.

Brazil Working Age Population: Labour Force: Northeast: Unemployed: by Number of Year Studies: Basic Education: Completed data was reported at 311.000 Person th in Mar 2019. This stayed constant from the previous number of 311.000 Person th for Dec 2018. Brazil Working Age Population: Labour Force: Northeast: Unemployed: by Number of Year Studies: Basic Education: Completed data is updated quarterly, averaging 251.000 Person th from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 344.000 Person th in Mar 2017 and a record low of 202.000 Person th in Dec 2013. Brazil Working Age Population: Labour Force: Northeast: Unemployed: by Number of Year Studies: Basic Education: Completed data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA014: Continuous National Household Sample Survey: Working Age Population: Labour Force: Unemployed: by Number of Year Studies. [External Remarks] People without work (which generates income for the household), has taken action to seek employment during the reference period of 30 day and is available to work during the week of reference. Also people without work and who did not take action in order to find a job in 30 days reference period because had achieved work that would start after the reference week.

Financial overview and grant giving statistics of Horizon Elementary School Pto

This statistic shows the net auction sales of Sotheby's worldwide from 2009 to 2019. Fine art auction house Sotheby's net auction sales, or total hammer price, amounted to approximately 3.92 billion U.S. dollars in 2019, down from the previous year's total of 4.4 billion U.S. dollars.

This database automatically captures metadata sourced from the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Manufacture and sale of industrial products and services by the Nomenclature of Industrial Products (NIP 2018), Slovenia, 2018”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Context

This datasets contains include basic and advanced player statistics of nba players in this current season so far. There are 3 different csv files that you can explore. Feel free to do whatever you want.

Content

Per Game Stats: Basically dividing every individual stats by the played game . Per 36 Minute Stats: In order to calculate per-36 minute stats, you divide 36 by the number of minutes the player actually played, then take that number and multiply all of the player's stats by it. Advanced Stats: These are more focused on the players direct effect on winning the games or scoring a point. For example a key tenet for many modern basketball analysts is that basketball is best evaluated at the level of possessions. During a single game, both teams have approximately the same number of possessions, because they alternate possession. (A team can have slightly more if it begins and ends a quarter or half with possession.) However, over the course of the season, teams play at very different paces, which can dramatically color their points scored and points allowed per game. Therefore, these analysts favor use of points scored per 100 possessions (offensive rating) and points allowed per 100 possessions (defensive rating).

Acknowledgements

The player data retrieved from http://www.basketball-reference.com/.