The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Australia was last recorded at 3.60 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Formaat: PDFOmvang: 60 KbOnline beschikbaar: [01-12-2014]This article was published on the Guardian website at 20.25 BST on Thursday 11 June 2009. A version appeared on p1 of the Main section section of the Guardian on Friday 12 June 2009. It was last modified at 12.21 BST on Monday 19 May 2014.© 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

About this dataset

Context

The dataset tabulates the Price population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Price was 8,261, a 0.12% increase year-by-year from 2022. Previously, in 2022, Price population was 8,251, an increase of 0.87% compared to a population of 8,180 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Price decreased by 244. In this period, the peak population was 8,716 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Price is shown in this column.
• Year on Year Change: This column displays the change in Price population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price Population by Year. You can refer the same here

This dataset contains published estimates of hatching failure (due to infertility or embryo death) in bird eggs, with red list category, estimates of global population size, number of clutches from which the hatching failure rates are based upon, study year and latitude of the study site(s). The dataset was used to analyze time trends in avian hatching failure rates, and to investigate any potential effects of conservation status on temporal relationships.

About this dataset

Context

The dataset tabulates the Price town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Price town was 226, a 0.89% increase year-by-year from 2021. Previously, in 2021, Price town population was 224, an increase of 0.45% compared to a population of 223 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price town decreased by 17. In this period, the peak population was 250 in the year 2007. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price town is shown in this column.
• Year on Year Change: This column displays the change in Price town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price town Population by Year. You can refer the same here

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.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

The benchmark interest rate in Mexico was last recorded at 7.75 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Point Reyes National Seashore in California. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range and mean significant wave height. The rankings for each input variable were combined and an index value calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI and the data contained within this dataset provide an objective technique for evaluation and long-term planning by scientists and park managers.

About this dataset

Context

The dataset tabulates the Colorado population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Colorado across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of Colorado was 5.96 million, a 0.95% increase year-by-year from 2023. Previously, in 2023, Colorado population was 5.9 million, an increase of 0.86% compared to a population of 5.85 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Colorado increased by 1.63 million. In this period, the peak population was 5.96 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Colorado is shown in this column.
• Year on Year Change: This column displays the change in Colorado population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Colorado Population by Year. You can refer the same here

This paper addresses the reduction of market failure under imperfect competition. It proposes a tax-scheme that provides firms with an incentive to forgo their market power: Firms optimize after‐tax profits. Now simply consider a firm’s gross profit margin the unique tax‐rate it is charged on absolute profits. In theory the firm’s tax‐rate would be the mark‐up over marginal costs, the firm’s Lerner index. As a result every firm determines its own tax‐rate by setting its price and incurring costs. This creates a new trade off for firms between a low tax‐burden and the exercising of market power. Welfare for society increases since firms with market power choose a lower price and produce a quantity closer or equal to social optimum; at the original monopolistic price‐level they can increase their profits by lowering their tax‐burden. Essentially the tax‐condition does not seem to distort profit incentives or markets; under perfect competition the tax‐rate would be zero. Thus, it is clear that the tax only takes effect when markets work inefficiently and its countervailing nature subsequently helps to remedy inefficiencies of imperfectly competitive markets.

About this dataset

Context

The dataset tabulates the Arkansas population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Arkansas across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of Arkansas was 3.09 million, a 0.62% increase year-by-year from 2023. Previously, in 2023, Arkansas population was 3.07 million, an increase of 0.71% compared to a population of 3.05 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Arkansas increased by 410,239. In this period, the peak population was 3.09 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Arkansas is shown in this column.
• Year on Year Change: This column displays the change in Arkansas population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Arkansas Population by Year. You can refer the same here