This table contains 24844 series, with data for years 2009 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-11-10. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Inputs-outputs (2 items: Inputs; Outputs) Industry (236 items: Total industries; Crop production (except greenhouse, nursery and floriculture production); Greenhouse, nursery and floriculture production; Animal production; ...) Commodity (471 items: Total commodities; Canola; Oilseeds (except canola); Wheat; ...).

This dataset is a simple table that contains a list of Maryland cities and associated counties. It may not be a complete list. This dataset is often used as a lookup table when pulled into a third party business intelligence tool. Users may also want to use the Zip Code Lookup Table and the Maryland Counties Match Tool for Data Quality dataset with this City Lookup Table in order to easily create one to many relationships (joins) in a third party business intelligence tool.

Abstract This article deals with the problem of translating statistical information given in other registers into the tabular register, from the following two objectives: 1) to study the performance of prospective teachers in translating information given in the other registers into the tabular register; and 2) to compare the performance of future teachers in the different translations. The study included 30 students, future teachers of the first school years, who were attending the 1st or 2nd year of the Degree in Basic Education, at a Higher Education School in the north of Portugal. The data of the present study were obtained through the answers given by the students to four questions, which required the translation of statistical information given in the graphic, numeric-verbal and simple data list register into the tabular register. In terms of results, it is noteworthy that students were more successful in building the simple frequency tables than in building the two two-way tables and the data table grouped into class intervals, the latter being the one that proved to be the most difficult. These results, related to the translation of different registers into the tabular register, are the main contribution of the study and imply that the prospective teachers must deepen their skills of tabular representation.

Information on case developments compared to the previous year (total number, change, awareness rate)

This table contains 5572 series, with data for years 2009 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-11-10. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Final demand categories (279 items: Total, final demand: final expenditure on gross domestic product (GDP); Household consumption expenditure, food; Household consumption expenditure, non-alcoholic beverages; Household consumption expenditure, alcoholic beverages; ...) Commodity (471 items: Total commodities; Canola; Oilseeds (except canola); Wheat; ...).

Objectives: In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table ("Table 1") of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers.

Materials and Methods: The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged.

Results: The tableone software package automatically compiles summary statistics into publishable formats such as CSV, HTML, and LaTeX. An executable Jupyter Notebook demonstrates application of the package to a subset of data from the MIMIC-III database. Tests such as Tukey's rule for outlier detection and Hartigan's Dip Test for modality are computed to highlight potential issues in summarizing the data.

Discussion and Conclusion: We present open source software for researchers to facilitate carrying out reproducible studies in Python, an increasingly popular language in scientific research. The toolkit is intended to mature over time with community feedback and input. Development of a common tool for summarizing data may help to promote good practice when used as a supplement to existing guidelines and recommendations. We encourage use of tableone alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. We also suggest seeking guidance from a statistician when using tableone for a research study, especially prior to submitting the study for publication.

A simple table time series for school probability and statistics. We have to learn how to investigate data: value via time. What we try to do: - mean: average is the sum of all values divided by the number of values. It is also sometimes referred to as mean. - median is the middle number, when in order. Mode is the most common number. Range is the largest number minus the smallest number. - standard deviation s a measure of how dispersed the data is in relation to the mean.

Provide customs online simple application (no certificate) statistical table

Time series Information on cases (total number, attempted — breakdown by offence — crime location distribution — proportions of male, female, non-German suspects)

The tables report selected forms of arts participation for U.S. states and the District of Columbia. State-level figures are reported for those estimates with coefficients of variation under 30 percent, at 90 percent confidence. The period refers to the 12 months ending February 2020. The data were derive from the 2020 Arts Basic Survey (ABS), a supplement to the Current Population Survey, and sponsored by the National Endowment for the Arts. The following state-level tables are included: Table 1A. Percent of U.S. adults who work with pottery, ceramics, or jewelry, or who create visual art such as paintings, sculpture, or graphic designs, by state Table 1B. Percent of U.S. adults do leatherwork, metalwork, or woodwork, or who weave, crochet, quilt, do needlepoint, knit, or sew, by state Table 1C. Percent of U.S. adults who play a musical instrument, by state Table 1D. Percent of U.S. adults who perform or practice any singing, by state Table 1E. Percent of U.S. adults who create any films or videos, or who take any photographs, as artistic activities, by state Table 1F. Percent of U.S. adults who attend live music, theater, or dance events, by state Table 1G. Percent of U.S. adults who go to art exhibits, by state Table 1H. Percent of U.S. adults who go out to the movies or go to see films, by state Table 1I. Percent of U.S. adults who visit buildings, neighborhoods, parks, or monuments for their historical, architectural, or design value, by state Table 1J. Percent of U.S. adults who read literature (novels or short stories, poetry, or plays), by state Table 1K. Percent of U.S. adults who use a device to watch, listen to, or download any music, theater, dance, or creative writing, or information about these art forms, by state For information about the 2020 ABS, please visit the 2020 ABS study homepage.

Information on completed cases (total number, breakdown by offence, crime location distribution, proportions of male, female, non-German crime suspects)

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_c7652191d32fef35e63680c278fe2972/view

Use Table for Imports at Basic Prices (NACE Rev 2) (Euro Million) by Industries, Year and Products

View data using web pages

Download .px file (Software required)

This table contains 8262 series, with data for years 2007 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-11-10. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...) Final demand categories (26 items: Total, final demand: final expenditure on gross domestic product (GDP); Household consumption expenditure; Expenditure by Canadians abroad; Expenditure by Canadians in other provinces or territories; ...) Commodity (75 items: Total commodities; Grains and other crop products; Live animals; Other farm products; ...).

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_021bbc050129abb8351d5dcbf0b7f8b9/view

Education statistics basic tables of 2019. 41 p.

HCUP Fast Stats provides easy access to the latest HCUP-based statistics for health information topics. HCUP Fast Stats uses visual statistical displays in stand-alone graphs, trend figures, or simple tables to convey complex information at a glance. Information on the effect of Medicaid expansion on hospital use will be updated regularly (quarterly or annually, as newer data become available).

Provide simple service counter statistics for business tax handling in various units of this agency.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...