The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

Inflation Rate in the United States increased to 2.70 percent in June from 2.40 percent in May of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

These rates are commonly referred to as Constant Maturity Treasury rates, or CMTs. Yields are interpolated by the Treasury from the daily yield curve. This curve, which relates the yield on a security to its time to maturity is based on the closing market bid yields on actively traded Treasury securities in the over-the-counter market. These market yields are calculated from composites of quotations obtained by the Federal Reserve Bank of New York. The yield values are read from the yield curve at fixed maturities, currently 1, 3 and 6 months and 1, 2, 3, 5, 7, 10, 20, and 30 years. This method provides a yield for a 10 year maturity, for example, even if no outstanding security has exactly 10 years remaining to maturity.

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.

About this dataset

Context

The dataset tabulates the Federal Heights 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 Federal Heights 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 Federal Heights was 13,943, a 0.41% decrease year-by-year from 2022. Previously, in 2022, Federal Heights population was 14,001, a decline of 1.50% compared to a population of 14,214 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Federal Heights increased by 1,870. In this period, the peak population was 14,394 in the year 2020. 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 Federal Heights is shown in this column.
• Year on Year Change: This column displays the change in Federal Heights 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 Federal Heights Population by Year. You can refer the same here

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 the United Kingdom was last recorded at 4.25 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is part of the following publication at the TransAI 2023 conference: R. Wallsberger, R. Knauer, S. Matzka; "Explainable Artificial Intelligence in Mechanical Engineering: A Synthetic Dataset for Comprehensive Failure Mode Analysis" DOI: http://dx.doi.org/10.1109/TransAI60598.2023.00032

This is the original XAI Drilling dataset optimized for XAI purposes and it can be used to evaluate explanations of such algortihms. The dataset comprises 20,000 data points, i.e., drilling operations, stored as rows, 10 features, one binary main failure label, and 4 binary subgroup failure modes, stored in columns. The main failure rate is about 5.0 % for the whole dataset. The features that constitute this dataset are as follows:

• ID: Every data point in the dataset is uniquely identifiable, thanks to the ID feature. This ensures traceability and easy referencing, especially when analyzing specific drilling scenarios or anomalies.
• Cutting speed vc (m/min): The cutting speed is a pivotal parameter in drilling, influencing the efficiency and quality of the drilling process. It represents the speed at which the drill bit's cutting edge moves through the material.
• Spindle speed n (1/min): This feature captures the rotational speed of the spindle or drill bit, respectively.
• Feed f (mm/rev): Feed denotes the depth the drill bit penetrates into the material with each revolution. There is a balance between speed and precision, with higher feeds leading to faster drilling but potentially compromising hole quality.
• Feed rate vf (mm/min): The feed rate is a measure of how quickly the material is fed to the drill bit. It is a determinant of the overall drilling time and influences the heat generated during the process.
• Power Pc (kW): The power consumption during drilling can be indicative of the efficiency of the process and the wear state of the drill bit.
• Cooling (%): Effective cooling is paramount in drilling, preventing overheating and reducing wear. This ordinal feature captures the cooling level applied, with four distinct states representing no cooling (0%), partial cooling (25% and 50%), and high to full cooling (75% and 100%).
• Material: The type of material being drilled can significantly influence the drilling parameters and outcomes. This dataset encompasses three primary materials: C45K hot-rolled heat-treatable steel (EN 1.0503), cast iron GJL (EN GJL-250), and aluminum-silicon (AlSi) alloy (EN AC-42000), each presenting its unique challenges and considerations. The three materials are represented as “P (Steel)” for C45K, “K (Cast Iron)” for cast iron GJL and “N (Non-ferrous metal)” for AlSi alloy.
• Drill Bit Type: Different materials often require specialized drill bits. This feature categorizes the type of drill bit used, ensuring compatibility with the material and optimizing the drilling process. It consists of three categories, which are based on the DIN 1836: “N” for C45K, “H” for cast iron and “W” for AlSi alloy [5].

• Process time t (s): This feature captures the full duration of each drilling operation, providing insights into efficiency and potential bottlenecks.

• Main failure: This binary feature indicates if any significant failure on the drill bit occurred during the drilling process. A value of 1 flags a drilling process that encountered issues, which in this case is true when any of the subgroup failure modes are 1, while 0 indicates a successful drilling operation without any major failures.

Subgroup failures: - Build-up edge failure (215x): Represented as a binary feature, a build-up edge failure indicates the occurrence of material accumulation on the cutting edge of the drill bit due to a combination of low cutting speeds and insufficient cooling. A value of 1 signifies the presence of this failure mode, while 0 denotes its absence. - Compression chips failure (344x): This binary feature captures the formation of compressed chips during drilling, resulting from the factors high feed rate, inadequate cooling and using an incompatible drill bit. A value of 1 indicates the occurrence of at least two of the three factors above, while 0 suggests a smooth drilling operation without compression chips. - Flank wear failure (278x): A binary feature representing the wear of the drill bit's flank due to a combination of high feed rates and low cutting speeds. A value of 1 indicates significant flank wear, affecting the drilling operation's accuracy and efficiency, while 0 denotes a wear-free operation. - Wrong drill bit failure (300x): As a binary feature, it indicates the use of an inappropriate drill bit for the material being drilled. A value of 1 signifies a mismatch, leading to potential drilling issues, while 0 indicates the correct drill bit usage.

The German Internet Panel (GIP) is an infrastructure project. The GIP serves to collect data about individual attitudes and preferences which are relevant for political and economic decision-making processes. Experimental variations were used in the instruments. The questionnaire contains numerous randomisations as well as a cross-questionnaire experiment. Topics: Party preference (Sunday question); assessment of the importance of selected policy fields for the federal government (labour market, foreign policy, education and research, citizen participation, energy supply, food and agriculture, European unification, family, health care system, gender equality, internal security, personal rights, pension system, national debt, tax system, environment and climate protection, consumer protection, transport, defence, currency, economy, immigration and integration); currently most important policy areas for the respondent; satisfaction with the performance of the federal government (scalometer); satisfaction with the performance of the parties CDU/CSU, SPD, Bündnis 90/Die Grünen, Die Linke (scalometer); probability of an external event: Effects of the Ukraine crisis on the availability and price of Russian gas in Germany; Federal government should draw consequences from the Ukraine crisis and find alternatives to the purchase of Russian gas; assessment of political decisions of the Federal government on the introduction of a rent brake and a car toll, on the expansion of the digital infrastructure as well as on the re-regulation of prostitution; respective responsibility for the fact that corresponding laws have not yet been passed; expected change in unemployment due to the introduction of the minimum wage respectively in Eastern Germany, Western Germany and in Germany as a whole; opinion on the introduction of a statutory minimum wage; assessment of an alternative proposal to the minimum wage (state pays the difference between the real hourly wage and a gross wage of 8.50 euros); opinion on lowering the minimum wage in regions with high unemployment instead of the same minimum wage throughout Germany; self-assessment of patience and willingness to take risks (scalometer); preferred date for the debt brake (from 2015, from 2020, from 2025, after 2030 or not at all); assessment of the debt brake; assessment of the probability that one´s own federal state will manage without new debt from 2020; one´s own federal state should comply with the debt brake if not all 16 federal states manage without new debt from 2020; net household income resp. personal income; own willingness to pay an additional tax amount so that the own federal state can get along without new debts from 2020 onwards and the amount of this contribution (answer scale depending on household income and personal income); debts of cities and municipalities: Willingness to pay additional fees so that the municipality of residence can manage without new debts and the amount of this contribution (classified); willingness to agree to the merger of one´s own federal state with a neighbouring federal state; opinion on self-determination of the tax level by the federal states; opinion on the financing of infrastructure costs in poor regions via a common EU budget; opinion on EU loans within the framework of the euro bailout fund for heavily indebted member states; opinion on the fiscal equalisation system between the federal states; whether one´s own federal state belongs to the donor states or the recipient states; opinion on a law on the formation of reserves by the federal states for the pensions of state civil servants; demand for state measures to reduce income disparities; acceptance of tax evasion; inflation in Germany: Assessment of the price development for food and clothing in general and measured against the expectations of the European Central Bank (ECB) (inflation expectations); expected annual inflation rate in five and in ten years (medium-term and long-term inflation); assessment of the European Central Bank with regard to price stability in the Eurozone; preferred combination of the amount of monthly expenditure and the amount of a loan repayment; reception frequency of news in general and of news on the topic of economy. Demography: sex; citizenship; year of birth (categorised); highest school-leaving qualification; highest professional qualification; marital status; household size; employment status; private internet use; federal state. Additionally coded were: Interview date; year of recruitment; questionnaire evaluation; overall interview assessment; unique ID identifier, household identifier and person identifier within household. Das German Internet Panel (GIP) ist ein Infrastrukturprojekt. Das GIP dient der Erhebung von Daten über individuelle Einstellungen und Präferenzen, die für politische und ökonomische Entscheidungsprozesse relevant sind. Es wurden experimentelle Variationen in den Instrumenten eingesetzt. Der Fragebogen enthält zahlreiche Randomisierungen sowie ein fragebogenübergreifendes Experiment. Themen: Parteipräferenz (Sonntagsfrage); Einschätzung der Wichtigkeit ausgewählter Politikfelder für die Bundesregierung (Arbeitsmarkt, Außenpolitik, Bildung und Forschung, Bürgerbeteiligung, Energieversorgung, Ernährung und Landwirtschaft, Europäische Einigung, Familie, Gesundheitssystem, Gleichstellung von Frauen und Männern, Innere Sicherheit, Persönlichkeitsrechte, Rentensystem, Staatsverschuldung, Steuersystem, Umwelt und Klimaschutz, Verbraucherschutz, Verkehr, Verteidigung, Währung, Wirtschaft, Zuwanderung und Integration); derzeit wichtigste Politikfelder für den Befragten; Zufriedenheit mit den Leistungen der Bundesregierung (Skalometer); Zufriedenheit mit den Leistungen der Parteien CDU/CSU, SPD, Bündnis 90/Die Grünen, Die Linke (Skalometer); Wahrscheinlichkeit eines von außen wirkenden Ereignisses: Auswirkungen der Ukraine-Krise auf die Verfügbarkeit und den Preis von russischem Gas in Deutschland; Bundesregierung sollte Konsequenzen aus der Ukraine-Krise ziehen und Alternativen zum Bezug von russischem Gas finden; Beurteilung von politischen Entscheidungen der Bundesregierung zur Einführung einer Mietpreisbremse und einer Pkw-Maut, zum Ausbau der digitalen Infrastruktur sowie zur Neuregulierung von Prostitution; jeweilige Verantwortlichkeit für die bisher nicht erfolgte Verabschiedung entsprechender Gesetze; erwartete Veränderung der Arbeitslosigkeit durch die Einführung des Mindestlohns jeweils in Ostdeutschland, Westdeutschland und in Deutschland insgesamt; Meinung zur Einführung eines gesetzlichen Mindestlohns; Bewertung eines Alternativvorschlags zum Mindestlohn (Staat zahlt Differenz zwischen dem realen Stundenlohn und einem Bruttolohn von 8,50 Euro); Meinung zur Senkung des Mindestlohns in Regionen mit hoher Arbeitslosigkeit statt gleicher Mindestlohn in ganz Deutschland; Selbsteinschätzung der Geduld und der Risikobereitschaft (Skalometer); präferierter Zeitpunkt für die Schuldenbremse (ab 2015, ab 2020, ab 2025, nach 2030 oder überhaupt nicht); Bewertung der Schuldenbremse; Einschätzung der Wahrscheinlichkeit, dass das eigene Bundesland ab 2020 ohne neue Schulden auskommt; eigenes Bundesland sollte Schuldenbremse einhalten, falls nicht alle 16 Bundesländer ab 2020 ohne neue Schulden auskommen; Haushaltsnettoeinkommen bzw. persönliches Einkommen; eigene Bereitschaft zur Zahlung eines zusätzlichen Steuerbetrages, damit das eigene Bundesland ab 2020 ohne neue Schulden auskommt und Höhe dieses Beitrags (Antwortskala abhängig vom Haushaltseinkommen und dem persönlichen Einkommen); Schulden von Städten und Gemeinden: Bereitschaft zur Zahlung zusätzlicher Gebühren, damit die Wohngemeinde ohne neue Schulden auskommt und Höhe diese Betrages (klassiert); Bereitschaft, dem Zusammenschluss des eigenen Bundeslandes mit einem benachbarten Bundesland zuzustimmen; Meinung zur Selbstbestimmung der Steuerhöhe durch die Bundesländer; Meinung zur Finanzierung der Infrastrukturkosten in armen Regionen über einen gemeinsamen EU-Haushalt; Meinung zu EU-Krediten im Rahmen des Euro-Rettungsschirms für stark verschuldete Mitgliedsstaaten; Meinung zum Länderfinanzausgleich; Zugehörigkeit des eigenen Bundeslandes zu den Geberländern oder Nehmerländern; Meinung zu einem Gesetz zur Bildung von Rücklagen durch die Bundesländer für die Pensionen von Landesbeamten; Forderung nach staatlichen Maßnahmen zur Verringerung von Einkommensunterschieden; Akzeptanz von Steuerhinterziehung; Inflation in Deutschland: Einschätzung der Preisentwicklung für Lebensmittel und Kleidung allgemein und gemessen an den Erwartungen der Europäischen Zentralbank (EZB) (Inflationserwartung); erwarte jährliche Inflationsrate in fünf und in zehn Jahren (mittelfristige und langfristige Inflation); Beurteilung der Europäischen Zentralbank im Hinblick auf die Preisstabilität in der Eurozone; präferierte Kombination der Höhe von monatlichen Ausgaben und der Höhe einer Kreditrückzahlung; Rezeptionshäufigkeit von Nachrichten allgemein und von Nachrichten zum Thema Wirtschaft. Demographie: Geschlecht; Staatsbürgerschaft; Geburtsjahr (kategorisiert); höchster Schulabschluss; höchste berufliche Qualifikation; Familienstand; Haushaltsgröße; Erwerbsstatus; private Internetnutzung; Bundesland. Zusätzlich verkodet wurde: Interviewdatum; Jahr der Rekrutierung; Fragebogenevaluation; Beurteilung der Befragung insgesamt; eindeutige ID-Kennung, Haushalts-Kennung und Personen-Kennung innerhalb des Haushalts.

The Federal Perkins Loan Cohort Default Rates is a data collection that is part of the Federal Perkins Loan program; the most recent Federal Perkins Loan Cohort Default Rates are available . Historical program data is available electronically since 2006 at . The data collection is conducted using a web-based entry system wherein postsecondary institutions must submit information electronically if they participate in the Federal Perkins Loan program. Key statistics produced from this data collection are the Federal Perkins Loan cohort default rates (previously known as the Orange Book).

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

The statistical data generated through the administration of the Federal milk order program is recognized widely as one of the benefits of this program. These data provide comprehensive and accurate information on milk supplies, utilization, and sales, as well as class prices established under the orders and prices paid to dairy farmers (producers). The sources of this data are monthly reports of receipts and utilization, producer payroll reports, and reports of nonpool handlers filed by milk processors (handlers) subject to the provisions of the various milk orders. The local market administrator (MA) uses these reports to determine pool obligations under the order and to verify proper payments to producers. Auditors employed by the MA review handler records to assure the accuracy of reported information. Reporting errors are corrected; if necessary, pool obligations are revised. After the pool obligations have been determined the local market administrator summarizes the individual handler reports and submits a series of order summary reports to the Market Information Branch (MIB) in Dairy Programs. The MIB summarizes the individual order data and disseminates this information via monthly, bimonthly, and annual releases or publications. Since milk marketing order statistics are based on reports filed by the population of possible reporting firms and not a sample, these statistics are comprehensive. Also, since these individual firm reports are subject to audit and verification, these statistics are accurate. The Federal milk order statistics database contains historical information, beginning in January 2000, generated by the administration of the Federal milk order program. Most of the information in the database has been published previously by the Market Information Branch in Dairy Programs either on its web site or in the Dairy Market News Report. New users are encouraged to use the "User Guide" to learn how to navigate the search screens. If you are interested in a description of the Federal milk order statistics program, or want current data, in ready made table form, use the "Current Information" link.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest 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 Australia was last recorded at 3.85 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Turkey was last recorded at 43 percent. This dataset provides the latest reported value for - Turkey Interest 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 China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest 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 Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Russia was last recorded at 18 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These rates are commonly referred to as "Real Constant Maturity Treasury" rates, or R-CMTs. Real yields on Treasury Inflation Protected Securities (TIPS) at "constant maturity" are interpolated by the U.S. Treasury from Treasury's daily real yield curve. These real market yields are calculated from composites of secondary market quotations obtained by the Federal Reserve Bank of New York. The real yield values are read from the real yield curve at fixed maturities, currently 5, 7, 10, 20, and 30 years. This method provides a real yield for a 10 year maturity, for example, even if no outstanding security has exactly 10 years remaining to maturity. Dataset updated daily every weekday.