Frequency tables for various meteorological quantities for various KNMI observation stations, for the period 1991-2020. The frequency tables describe, among other things, wind direction, wind speed, sunshine duration, temperature and humidity. The tables consist of hourly data, grouped into hourly blocks (columns) and classes (rows). All tables are available in four different formats. Two with distributive data, in which the associated frequency is given for each hourly block per class, and two with cumulative data, in which the sum of the frequencies of this and all the above classes is given for each hourly block per class. Of both the distributive and cumulative variants, there is one in which the frequencies are given as exact numbers of observations, and one in which the frequencies are given as a percentage of the total number of observations per hourly block.

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.

Frequency tables for various meteorological quantities for various KNMI observation stations, for the period 1991-2020. The frequency tables describe, among other things, wind direction, wind speed, sunshine duration, temperature and humidity.

The tables consist of hourly data, grouped into hourly blocks (columns) and classes (rows). All tables are available in four different formats. Two with distributive data, in which the associated frequency is given for each hourly block per class, and two with cumulative data, in which the sum of the frequencies of this and all the above classes is given for each hourly block per class. Of both the distributive and cumulative variants, there is one in which the frequencies are given as exact numbers of observations, and one in which the frequencies are given as a percentage of the total number of observations per hourly block.

Each table contains the following data.

Table S-1: Frequency distribution table of growth measures (Sales, Number of patents, Number of domestic patents, Number of patent attorneys and Number of employees)

Table S-2: Correlation coefficients between absolute and relative growth

Table S-3: Correlation of absolute growth measures (Sales, Number of patents, Number of domestic patents, Number of patent attorneys and Number of employees)

Table S-4: Correlation of relative growth measures (Sales, Number of patents, Number of domestic patents, Number of patent attorneys and Number of employees)

Table S-5: Correlation of absolute and relative growth measures in different time spans

The Excel spreadsheet contains the quantitative questions (Questions 1, 3 and 4). Each question is analysed in the form of a frequency distribution table and a pie chart.

This table contains 1260 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (30 items: Austria; Belgium (Flemish speaking); Belgium; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age group (3 items: 11 years;13 years;15 years ...), Frequency of exercise (7 items: Everyday; Once a week;2 to 3 times a week;4 to 6 times a week ...).

Current Effective Date: 0901Z 12 Jun 2025 to 0901Z 07 Aug 2025Frequencies provide radio frequency information. This frequency data is provided as a non-spatial database table and can be used to build a relationship table with the Service feature data. Frequency data information is shown on Enroute charts and published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.

Quality assessment of factorial designs, particularly mixed-level factorial designs, is a nontrivial task. Existing methods for orthogonal arrays include generalized minimum aberration, a modification thereof that was proposed by Wu and Zhang for mixed two- and four-level arrays, and minimum projection aberration. For supersaturated designs, E(s2) or χ2-based criteria are widely used. Based on recent insights by Grömping and Xu regarding the interpretation of the projected aR values used in minimum projection aberration, this article proposes three new types of frequency tables for assessing the quality of level-balanced factorial designs. These are coding invariant, which is particularly important for designs with qualitative factors. The proposed tables are used in the same way as those used in minimum projection aberration and behave more favorably when used for mixed-level arrays. Furthermore, they are much more manageable than the above-mentioned approach by Wu and Zhang. The article justifies the proposed tables based on their statistical information content, makes recommendations for their use, and compares them with each other and with existing criteria. As a byproduct, it is shown that generalized minimum aberration refines the established expected χ2 criterion for level-balanced supersaturated designs.

The frequency list of words by source was prepared in the following manner: words (i.e. lemmas with their lexical features) were extracted from 15 most frequent sources in the Trendi Monitor Corpus of Slovene (http://hdl.handle.net/11356/1590) covering the period between 1 January 2019 and 31 July 2022. The extracted sources are the following:

• STA (sta.si)
• RTV (rtvslo.si)
• Delo (delo.si)
• Siol (siol.net)
• Vestnik (vestnik.si)
• Večer (vecer.com)
• Svet24 – Novice (novice.svet24.si)
• 24ur (24ur.com)
• Dnevnik (dnevnik.si)
• Žurnal24 (zurnal24.si)
• Demokracija (demokracija.si)
• Nova24TV (nova24tv.si)
• Slovenske novice (slovenskenovice.si)
• Gorenjski glas (gorenjskiglas.si)
• Svet 24 – Ekipa (ekipa.svet24.si)

The frequency lists obtained from Trendi were then compared to the frequency list of words from Gigafida 2.0 (http://hdl.handle.net/11356/1320; covering the period between 1991–2018). The final frequency list contains lemmas, their lexical features, and – for each source (including Gigafida 2.0) – their absolute and relative frequencies from the first (1991–2018) and second periods (from 2019 to 2022-07), as well as the simple maths value indicating if the word is more frequent in 2019-2022-07 (simple maths > 1.00) or in 1991–2018 (simple maths < 1.00).

Because the entire frequency list is quite large, a shorter version with the first 150,000 entries is also provided for easier use in data processing software (such as MS Excel). The lists are sorted by their total absolute frequencies. Note that words with a total frequency of 1 (when adding absolute frequencies from both compared corpora; hapax legomena) were removed.

The U.S. Geological Survey (USGS) has a long history of working cooperatively with the South Carolina Department of Transportation to develop methods for estimating the magnitude and frequency of floods for rural and urban basins that have minimal to no regulation or tidal influence. As part of those previous investigations, flood-frequency estimates have been generated at selected regulated streamgages. This is the data release for the report which assesses the effects of impoundments on flood-frequency characteristics by comparing annual exceedance probability (AEP) streamflows from pre- and post-regulated (before and after impoundment) periods at 18 USGS long-term streamgages, which is defined as a streamgage with 30 or more years of record, in Georgia, South Carolina, and North Carolina. For an assessment of how differences in such statistics can be influenced by period of record and hydrologic conditions captured in those records, which could be considered as natural variability, AEP streamflows at an additional 18 long-term USGS streamgages that represent unregulated conditions in those three states were computed and compared for the first and last half of those records. This data release contains the tables and software input and output files from the report Effects of impoundments on selected flood-frequency and daily mean streamflow characteristics in Georgia, South Carolina, and North Carolina (Feaster and Musser, 2023). These tables contain information about the streamgages used in the analysis. The tables are contained in the zip file Impoundments_tables.zip, which includes 8 tab-delimited txt files, and a formatted Excel file of all the tables used in the publication. Two additional files, PeakFQ_files.zip and WREG-Archive.zip include input and output files from the analysis.

Small example of a frequency table with patterns and diaries.

This statistic displays the result of a survey on the usage frequency of food or shopping list apps in Norway in 2016. During the survey period, ** percent of respondents in Norway stated never to be using food or shopping list apps when grocery shopping, while ** percent answered that they use food or shopping list apps from time to time.

Frequency Utilization: Medium Frequency: North Sumatera data was reported at 5.000 Unit in 2017. This stayed constant from the previous number of 5.000 Unit for 2016. Frequency Utilization: Medium Frequency: North Sumatera data is updated yearly, averaging 29.000 Unit from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 39.000 Unit in 2009 and a record low of 5.000 Unit in 2017. Frequency Utilization: Medium Frequency: North Sumatera data remains active status in CEIC and is reported by Directorate General of Resources and Equipment of Post and Information Technology. The data is categorized under Indonesia Premium Database’s Transport and Telecommunication Sector – Table ID.TE005: Telecommunication Statistics: Frequency Utilization.

Frequency Utilization: Medium Frequency: Jambi data was reported at 2.000 Unit in 2017. This stayed constant from the previous number of 2.000 Unit for 2016. Frequency Utilization: Medium Frequency: Jambi data is updated yearly, averaging 3.000 Unit from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 4.000 Unit in 2013 and a record low of 1.000 Unit in 2015. Frequency Utilization: Medium Frequency: Jambi data remains active status in CEIC and is reported by Directorate General of Resources and Equipment of Post and Information Technology. The data is categorized under Indonesia Premium Database’s Transport and Telecommunication Sector – Table ID.TE005: Telecommunication Statistics: Frequency Utilization.

Supplementary material is provided in support of wavelength and intensity of the bands that are studied in this article.

Indonesia Frequency Utilization: Very High Frequency: Central Kalimantan data was reported at 2,075.000 Unit in 2017. This records an increase from the previous number of 1,598.000 Unit for 2016. Indonesia Frequency Utilization: Very High Frequency: Central Kalimantan data is updated yearly, averaging 810.000 Unit from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 2,075.000 Unit in 2017 and a record low of 593.000 Unit in 2010. Indonesia Frequency Utilization: Very High Frequency: Central Kalimantan data remains active status in CEIC and is reported by Directorate General of Resources and Equipment of Post and Information Technology. The data is categorized under Indonesia Premium Database’s Transport and Telecommunication Sector – Table ID.TE005: Telecommunication Statistics: Frequency Utilization.

Frequency Utilization: Super High Frequency: West Nusa Tenggara data was reported at 7,587.000 Unit in 2017. This records an increase from the previous number of 6,739.000 Unit for 2016. Frequency Utilization: Super High Frequency: West Nusa Tenggara data is updated yearly, averaging 4,615.000 Unit from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 7,587.000 Unit in 2017 and a record low of 2,328.000 Unit in 2009. Frequency Utilization: Super High Frequency: West Nusa Tenggara data remains active status in CEIC and is reported by Directorate General of Resources and Equipment of Post and Information Technology. The data is categorized under Indonesia Premium Database’s Transport and Telecommunication Sector – Table ID.TE005: Telecommunication Statistics: Frequency Utilization.

Frequency of in-person contact with friends, population aged 15 years and older, by sex, number and percentage, 2013.

Canadian Internet use survey, Internet use at home, by age group and frequency of use, for Canada from 2005 to 2009. (Terminated)

The data set records the frequency statistics of typical geological disasters in Qinghai Province from 2011 to 2016. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains six data tables, which are: the frequency of sudden geological disasters in 2011, 2012, 2013, 2014 and 2015 Statistical table, 2016 Qinghai Province sudden geological disasters frequency statistical table, data table structure is the same. There are two fields in each data table, such as the occurrence frequency of sudden geological disasters in 2011: Field 1: Location Field 2: frequency ratio