Boxplots are useful displays that convey rough information about the distribution of a variable. Boxplots were designed to be drawn by hand and work best for small datasets, where detailed estimates of tail behavior beyond the quartiles may not be trustworthy. Larger datasets afford more precise estimates of tail behavior, but boxplots do not take advantage of this precision, instead presenting large numbers of extreme, though not unexpected, observations. Letter-value plots address this problem by including more detailed information about the tails using “letter values,” an order statistic defined by Tukey. Boxplots display the first two letter values (the median and quartiles); letter-value plots display further letter values so far as they are reliable estimates of their corresponding quantiles. We illustrate letter-value plots with real data that demonstrate their usefulness for large datasets. All graphics are created using the R package lvplot, and code and data are available in the supplementary materials.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

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.

According to the source's data, the market value of big data analytics in Italy increased steadily over the period considered, growing from 790 million euros in 2015 to approximately 1.8 billion euros in 2020.

Daily data provide one data value to represent water conditions for the day. Throughout much of the history of the USGS, the primary water data available was daily data collected manually at the monitoring location once each day. With improved availability of computer storage and automated transmission of data, the daily data published today are generally a statistical summary or metric of the continuous data collected each day, such as the daily mean, minimum, or maximum value. Daily data are automatically calculated from the continuous data of the same parameter code and are described by parameter code and a statistic code. These data have also been referred to as “daily values” or “DV”.

This statistic shows the global data visualization market revenue in 2017 and 2023. In 2017, the total value of this market was estimated to be 4.51 billion US dollars. The market is expected to increase to 7.76 billion U.S. dollars by 2023, with a CAGR of 9.47 percent over the forecast period.

The Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December of the census year, forms are sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies. This dataset presents company, establishments, value of shipments, value of product shipments, percentage of product shipments of the total value of shipments, and percentage of distribution of value of product shipments.

Errata: On Dec 2nd, 2018, several yearly statistics files were replaced with new versions to correct an inconsistency related to the computation of the "dma8epax" statistics. As written in Schultz et al. (2017) [https://doi.org/10.1525/elementa.244], Supplement 1, Table 6: "When the aggregation period is “seasonal”, “summer”, or “annual”, the 4th highest daily 8-hour maximum of the aggregation period will be computed.". The data values for these aggregation periods are correct, however, the header information in the original files stated that the respective data column would contain "average daily maximum 8-hour ozone mixing ratio (nmol mol-1)". Therefore, the header of the seasonal, summer, and annual files has been corrected. Furthermore, the "dma8epax" column in the monthly files erroneously contained 4th highest daily maximum 8-hour average values, while it should have listed monthly average values instead. The data of this metric in the monthly files have therefore been replaced. The new column header reads "avgdma8epax". The updated files contain a version label "1.1" and a brief description of the error. If you have made use of previous TOAR data files with the "dma8epax" metric, please exchange your data files.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

This data set contains annual quantities and value for all seafood products that are landed and sold by established seafood dealers and brokers in the Southeast Region (North Carolina through Texas). These types of data, referred to as the general canvass landings statistics, have been collected by the NOAA Fisheries Service, National Marine Fisheries Service and its predecessor agency, the Bureau of Commercial Fisheries. The data are available on computer since the early 1960's. The quantities and values that are reported in this data set include the annual landings that were initiated in 1962. Beginning in 1976, the data were collected monthly. See the sections on Links for the reference to the monthly general canvass landings. The annual general canvass landings include quantities and value for all living marine species and are identified by species (usually the local or common name). These data were collected by field agents employed by the National Marine Fisheries Service or the Bureau of Commercial Fisheries and assigned to local fishing ports. The agents contacted the majority of the seafood dealers or brokers in their assigned areas and recorded the quantities and value for each species or species category from the sales receipts maintained by the seafood dealers. In addition, information on the gear and area of capture is available for most of the landings statistics in the data set. Based on their knowledge of the fishing activity in the area, the agents would estimate the type of fishing gear and area where the fishing was likely to have occurred. More detailed information on the caveats associated with these data is provided in the Characteristics, Caveats and Issues section. However, because these data are summaries, they do not contain information on the quantities of fishing effort or identifications of the fishermen or vessels that caught the fish or shellfish.

The dataset collection in focus comprises an assortment of tables, each carrying a distinct set of data. These tables are meticulously sourced from the website of Lantmäteriet (The Swedish Mapping, Cadastral and Land Registration Authority) in Sweden. The dataset provides a wide range of valuable data, including but not limited to, information about companies, geospatial data, meteorological data, statistical data, and earth observation & environmental data. The tables present the data in an organized manner, with the information arranged systematically in columns and rows. This makes it convenient to analyze and draw insights from the dataset. Overall, it's a comprehensive dataset collection that offers a diverse and substantial range of information.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5300314b2a47151f85b1786e449e860c/view

Euro Area Growth Signals from Industrial Production: Warnings from a Comparison of Gross Value Added and Production

This study compares industrial production and gross value added in volume terms in the euro area and euro area countries, because real GDP growth
signals from industrial production growth might be misleading and earlier released industrial production growth is not one-to-one translated into industrial value added growth. This is an important issue for analysts and policy makers, because industrial production is a standard element in tools for nowcasting real GDP in real time. It also raises the question about the factors explaining these differences. Differences in terms of (changes in) quarterly growth between production and gross value added include sign reversals and can last for consecutive quarters. Persistent level differences might also exist. The explanatory factors for these differences are the treatment of prices, seasonality and coverage. Data limitations prevent a detailed analysis of the price factor, but the other two factors are more closely evaluated. It turns out that the relative importance of these factors varies over time and thus is difficult to assess ex ante for a specific quarter. A remedy is that statisticians further harmonize national accounts and short-term statistics as well as national practices for seasonal adjustment.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Equivalence of the F test and t test in Example 1.

In this article we reply to Silva and Guarnieri (2014) comments on Figueiredo Filho et al (2013) paper published by the Brazilian Political Science Review. Originally, we developed four recommendations: (1) scholars must always graphically analyze their data before interpreting the p-value; (2) it is pointless to estimate the p-value for non-random samples; (3) the p-value is highly affected by the sample size and (4) it is pointless to estimate the p-value when dealing with data on population. Here we defend our view about the proper use the p-value statistic and we use both observational and simulation data to make our case. Substantively, we hope to advance the debate about statistical significance in Political Science.

Graph and download economic data for Business Sector: Value-Added Output for All Workers (PRS84006051) from Q1 1948 to Q1 2025 about current dollars, value added, output, sector, business, rate, and USA.