Graph and download economic data for Nominal Statistical Discrepancy for United States (NSDGDPSAXDCUSQ) from Q1 1950 to Q1 2021 about residual and USA.

Austria Services Turnover Index: Nominal data was reported at 124.000 2010=100 in Dec 2017. This records an increase from the previous number of 117.100 2010=100 for Sep 2017. Austria Services Turnover Index: Nominal data is updated quarterly, averaging 108.900 2010=100 from Mar 2011 (Median) to Dec 2017, with 28 observations. The data reached an all-time high of 124.000 2010=100 in Dec 2017 and a record low of 99.400 2010=100 in Jun 2011. Austria Services Turnover Index: Nominal data remains active status in CEIC and is reported by Statistics Austria. The data is categorized under Global Database’s Austria – Table AT.H013: Nominal Services Turnover Index: 2010=100. Rebased from 2010=100 to 2015=100 Replacement series ID: 403929797

Graph and download economic data for Nominal Statistical Discrepancy for Italy (NSDGDPNSAXDCITQ) from Q1 1995 to Q1 2023 about residual and Italy.

The food dollar series measures annual expenditures by U.S. consumers on domestically produced food. This data series is composed of three primary series—the marketing bill series, the industry group series, and the primary factor series—that shed light on different aspects of the food supply chain. The three series show three different ways to split up the same food dollar. Nominal DataThe FoodDollarDataNominal.xls file and the NominalData.csv file include statistics reported in current year dollars. In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2015UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)Real Data The FoodDollarDataReal.xls file and the FoodDollarDataReal.csv file include statistics reported in constant year 2009 dollars. Since the March 30, 2016 update, 2006 data in cents per domestic real food dollar units have been added to the real food dollar series.In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2014UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)

Key information about Spain Nominal GDP

• Spain Nominal GDP reached 377.3 USD bn in Mar 2023, compared with 352.5 USD bn in the previous quarter.
• Nominal GDP in Spain is updated quarterly, available from Mar 1970 to Mar 2023, with an average number of 150.1 USD bn.
• The data reached an all-time high of 435.8 USD bn in Jun 2008 and a record low of 9.2 USD bn in Mar 1970.

• In the latest reports, Spain GDP expanded 3.8 % YoY in Mar 2023.
• Its GDP deflator (implicit price deflator) increased 6.2 % in Mar 2023.
• Spain GDP Per Capita reached 27,002.6 USD in Dec 2020.
• Its Gross Savings Rate was measured at 24.2 % in Dec 2022.
• For Nominal GDP contributions, Investment accounted for 19.1 % in Mar 2023.
• Public Consumption accounted for 19.2 % in Mar 2023.
• Private Consumption accounted for 58.1 % in Mar 2023.

Korea HS: OU: Income: Nominal data was reported at 3,669,884.000 KRW in Mar 2018. This records a decrease from the previous number of 3,762,108.000 KRW for Dec 2017. Korea HS: OU: Income: Nominal data is updated quarterly, averaging 2,449,037.000 KRW from Mar 1990 (Median) to Mar 2018, with 113 observations. The data reached an all-time high of 3,762,108.000 KRW in Dec 2017 and a record low of 860,860.000 KRW in Mar 1990. Korea HS: OU: Income: Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H056: Household Income and Expenditure Survey (HS): Other Urban Household: Nominal.

Nominal Median and Nominal Mean Income Measures by National Income Definition, Year and Statistic

View data using web pages

Download .px file (Software required)

Francisco Bernis elaboró en 1914 con la colaboración de sus estudiantes de economía política en la Universidad de Salamanca los "Estudios estadísticos" donde creaba un índice ponderado de precios, otro de salarios, y una encuesta de presupuestos. En el curso 2024-25 hemos recuperado estos datos para su utilización en la docencia actual dentro del proyecto de innovación docente USAL 2024/122. In 1914, Francisco Bernis, in collaboration with his students of political economy at the University of Salamanca, developed the "Statistical Studies," which included a weighted price index, a wage index, and a budget survey. In the 2024-25 academic year, we recovered these data for use in current teaching within the Innovative Teaching Project USAL 2024/122.

Wage and Payroll Statistics - Table 220-19003 : Nominal Wage Indices for employees up to supervisory level by industry section by broad occupational group (September 1992 = 100)

Graph and download economic data for Nominal Statistical Discrepancy for Germany (NSDGDPNSAXDCDEQ) from Q1 1991 to Q1 2022 about residual and Germany.

Wage and Payroll Statistics - Table 220-19023 : Nominal Indices of Payroll per Person Engaged by industry division (Q1 1999 = 100)

Korea HS: OH: Income: Nominal data was reported at 3,599,960.000 KRW in Mar 2018. This records a decrease from the previous number of 3,728,516.000 KRW for Dec 2017. Korea HS: OH: Income: Nominal data is updated quarterly, averaging 3,115,133.000 KRW from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 3,728,516.000 KRW in Dec 2017 and a record low of 2,221,416.000 KRW in Mar 2003. Korea HS: OH: Income: Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H052: Household Income and Expenditure Survey (HS): Other Household: Nominal.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5d2a513a20f58239f8c449ea6c9b6ecd/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_64f98475cef1e94300362cb400a50012/view

Salaries and Employee Benefits Statistics - Managerial and Professional Employees (Excluding Top Management) - Table 220-25001 : Nominal Salary Indices (A) for middle-level managerial and professional employees by industry section (June 1995 = 100)

Here are some interesting statistics about the world from Wikipedia

• This dataset provides a comprehensive snapshot of global country statistics for the year 2023. It was scraped from various Wikipedia pages using BeautifulSoup, consolidating key indicators and metrics for 142 countries. The dataset covers diverse aspects such as land area, water area, Human Development Index (HDI), GDP forecasts, internet usage, and population changes.

Key Columns and Metrics:

• Country: The name of the country.
• Total in km2: Total area of the country.
• Land in km2: Land area excluding water bodies.
• Water in km2: Area covered by water bodies.
• Water %: Percentage of the total area covered by water.
• HDI: Human Development Index, a measure of a country's overall achievement in its social and economic dimensions.
• %HDI Growth: Percentage growth in HDI.
• IMF Forecast GDP(Nominal): International Monetary Fund's forecast for Gross Domestic Product in nominal terms.
• World Bank Forecast GDP(Nominal): World Bank's forecast for Gross Domestic Product in nominal terms.
• UN Forecast GDP(Nominal): United Nations' forecast for Gross Domestic Product in nominal terms.
• IMF Forecast GDP(PPP): IMF's forecast for Gross Domestic Product in purchasing power parity terms.
• World Bank Forecast GDP(PPP): World Bank's forecast for Gross Domestic Product in purchasing power parity terms.
• CIA Forecast GDP(PPP): Central Intelligence Agency's forecast for Gross Domestic Product in purchasing power parity terms.
• Internet Users: Number of internet users in the country.
• UN Continental Region: Continental region classification by the United Nations.
• UN Statistical Subregion: Statistical subregion classification by the United Nations.
• Population 2022: Population of the country in the year 2022.
• Population 2023: Population of the country in the year 2023.
• Population %Change: Percentage change in population from 2022 to 2023.

ObjectiveTo provide a practical guidance for the analysis of N-of-1 trials by comparing four commonly used models.MethodsThe four models, paired t-test, mixed effects model of difference, mixed effects model and meta-analysis of summary data were compared using a simulation study. The assumed 3-cycles and 4-cycles N-of-1 trials were set with sample sizes of 1, 3, 5, 10, 20 and 30 respectively under normally distributed assumption. The data were generated based on variance-covariance matrix under the assumption of (i) compound symmetry structure or first-order autoregressive structure, and (ii) no carryover effect or 20% carryover effect. Type I error, power, bias (mean error), and mean square error (MSE) of effect differences between two groups were used to evaluate the performance of the four models.ResultsThe results from the 3-cycles and 4-cycles N-of-1 trials were comparable with respect to type I error, power, bias and MSE. Paired t-test yielded type I error near to the nominal level, higher power, comparable bias and small MSE, whether there was carryover effect or not. Compared with paired t-test, mixed effects model produced similar size of type I error, smaller bias, but lower power and bigger MSE. Mixed effects model of difference and meta-analysis of summary data yielded type I error far from the nominal level, low power, and large bias and MSE irrespective of the presence or absence of carryover effect.ConclusionWe recommended paired t-test to be used for normally distributed data of N-of-1 trials because of its optimal statistical performance. In the presence of carryover effects, mixed effects model could be used as an alternative.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

Graph and download economic data for Nominal Statistical Discrepancy for Estonia (NSDGDPSAXDCESQ) from Q1 1995 to Q2 2023 about residual and Estonia.