SIA23 - Nominal Median and Nominal Mean Income Measures. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Nominal Median and Nominal Mean Income Measures...

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). Integrated Global Radiosonde Archive is a digital data set archived at the former National Climatic Data Center (NCDC), now National Centers for Environmental Information (NCEI). This dataset contains monthly means of geopotential height, temperature, zonal wind, and meridional wind derived from the Integrated Global Radiosonde Archive (IGRA). IGRA consists of radiosonde and pilot balloon observations at over 1500 globally distributed stations, and monthly means are available for the surface and mandatory levels at many of these stations. The period of record varies from station to station, with many extending from 1970 to 2016. Monthly means are computed separately for the nominal times of 0000 and 1200 UTC, considering data within two hours of each nominal time. A mean is provided, along with the number of values used to calculate it, whenever there are at least 10 values for a particular station, month, nominal time, and level.

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.

TAH28 - Mean and Median equivalised nominal disposable income. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Mean and Median equivalised nominal disposable income...

United States FRBOP Forecast: Nominal GDP: saar: Mean: Plus 4 Qtrs data was reported at 21,994.169 USD bn in Mar 2019. This records an increase from the previous number of 21,891.236 USD bn for Dec 2018. United States FRBOP Forecast: Nominal GDP: saar: Mean: Plus 4 Qtrs data is updated quarterly, averaging 7,163.010 USD bn from Dec 1968 (Median) to Mar 2019, with 197 observations. The data reached an all-time high of 21,994.169 USD bn in Mar 2019 and a record low of 939.793 USD bn in Dec 1968. United States FRBOP Forecast: Nominal GDP: saar: Mean: Plus 4 Qtrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.A003: NIPA 2018: GDP by Expenditure: Current Price: saar: Forecast: Federal Reserve Bank of Philadelphia.

The nominal unit labour cost (NULC) index is defined as the ratio of labour cost to labour productivity, where labour cost is the ratio of compensation of employees (current prices) to hours worked by employees, and labour productivity is the ratio of gross domestic product (at market prices in millions, chain-linked volumes reference year 2015) to total hours worked. Data on employment are presented according to the domestic concept used in national accounts. Input data are obtained from the official national accounts' country data, through ESA 2010 transmission programme.

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)

Dataset from Ministry of Manpower. For more information, visit https://data.gov.sg/datasets/d_16dfef0280cd2c09f95dcb52c6a7a006/view

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

FRBOP Forecast: Nominal GDP: saar: Mean: Plus 2 Qtrs data was reported at 21,546.124 USD bn in Mar 2019. This records an increase from the previous number of 21,415.169 USD bn for Dec 2018. FRBOP Forecast: Nominal GDP: saar: Mean: Plus 2 Qtrs data is updated quarterly, averaging 6,732.814 USD bn from Dec 1968 (Median) to Mar 2019, with 202 observations. The data reached an all-time high of 21,546.124 USD bn in Mar 2019 and a record low of 908.770 USD bn in Dec 1968. FRBOP Forecast: Nominal GDP: saar: Mean: Plus 2 Qtrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.A003: NIPA 2018: GDP by Expenditure: Current Price: saar: Forecast: Federal Reserve Bank of Philadelphia.

These data represent daily values (daily mean, instantaneous daily output) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The 1PCTTO2X simulation(included year 2080) was initiated from nominal year 1970 of preindustriel run,when equilibrium was reached (corresponds to nominal year 1860 of CO2-doubling experiment). Forcing agents included: CO2, CH4, N2O, O3, CFC11 (including other CFCs and HFCs), CFC12; sulfate(Boucher), BC, sea salt, desert dust aerosols.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRA1B: SRES A1B) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRA1B_1_MM_hur850

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

These data represent daily values (daily mean, instantaneous daily output, diurnal cycle) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The 20th century simulation (included year 2000) was initiated from year 111 of the control preindustrial simulation (nominal year 1970), when equilibrium was reached (corresponds to nominal year 1860 of 20C3M). Forcing agents included: CO2,CH4,N2O,O3,CFC11(including other CFCs and HFCs),CFC12; sulfate(Boucher),BC,sea salt,desert dust aerosols. This is followed by a commitment experiment for the 21th century (year 2001-2100) with all concentrations fixed at their levels of year 2000.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRA2: SRES A2) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRA2_1_MM_hur850

The time coverage of the experiment is 1/1/1860 - 31/12/2000 , but for relative_humidity (hur) and all variables on level 925hPa the storage begins only at 1/1/1900 (Only for run1, run2 is complete).

For this experiment 2 ensemble runs were carried out.

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

These data represent daily values (daily mean, instantaneous daily output, diurnal cycle) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The SRES-B1 simulation(included year 2100) was initiated from nominal year 2000 of 20C3M run1. It corresponds to nominal year 2000 of SRES-B1 experiment. Forcing agents included: CO2,CH4,N2O,O3,CFC11(including other CFCs and HFCs),CFC12; sulfate(Boucher),BC,sea salt,desert dust aerosols. This 550 ppm stabilization experiment continued until 2300 with all concentrations fixed at their levels of year 2100.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRB1: SRES B1) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRB1_1_MM_hur850

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

This dataset contains ocean currents data from Shipboard Acoustic Doppler Current Profilers (SADCP) collected during the R/V Point Sur cruise PS18_28 in the northern Gulf of Mexico from 2018-06-21 to 2018-06-28. The experimental site is on the continental slope of the northern Gulf of Mexico, next to the Deepwater Horizon Spill site. The raw ADCP data were collected and processed using the University of Hawaii data acquisition system (UHDAS) during the cruise. The post-cruise data processing were conducted by the University of Hawaii using the Common Oceanographic Data Analysis System (CODAS) (Common Ocean Data Access System) processing. This dataset contains both raw and processed data. There were two vessel-mounted ADCPs on Point Sur operating at 75 kHz and 300 kHz respectively. Both were manufactured by RD Instruments. CODAS_variables cdm_data_type=TrajectoryProfile cdm_profile_variables=time cdm_trajectory_variables=trajectory, latitude, longitude comment=software: pycurrents comment1=CODAS_variables comment2=Variables in this CODAS long-form netcdf file are taken directly from the original CODAS database used in processing. For additional information see the CODAS_processing_note global attribute and the attributes of each of the variables.

The term "bin" refers to the depth cell index, starting from 1 nearest the transducer. Bin depths correspond to the centers of the depth cells.

short_name description

time : Time at the end of the ensemble, days from start of year. lon, lat : Longitude, Latitude at the end of the ensemble. u,v : Zonal and meridional velocity component profiles relative to the moving ship, not to the earth. w : Vertical velocity -- Caution: usually dominated by ship motion and other artifacts. error_vel : Error velocity -- diagnostic, scaled difference between 2 estimates of vertical velocity (w). amp_sound_scat : Received signal strength (ADCP units; not corrected for spreading or attenuation). profile_flags : Editing flags for averaged data. percent_good : Percentage of pings used for averaging u, v after editing. spectral_width : Spectral width for NB instruments; correlation for WH, BB, OS instruments.

CONFIG1_tr_depth : Transducer depth, meters. CONFIG1_top_ref_bin : Reference layer averaging: top bin. CONFIG1_bot_ref_bin : Reference layer averaging: bottom bin. CONFIG1_pls_length : Pulse length projected on vertical (meters). CONFIG1_blank_length : Blank length (vertical; meters). CONFIG1_bin_length : Bin length (vertical; meters). CONFIG1_num_bins : Number of bins. CONFIG1_ping_interval : Approximate mean time between pings or ping groups. CONFIG1_hd_offset : Transducer azimuth approximation prior to data processing, clockwise rotation of beam 3 from forward. CONFIG1_freq_transmit : Nominal (round number) instrument frequency. CONFIG1_ev_threshold : Error velocity editing threshold (if known). CONFIG1_bot_track : Flag: does any bottom track data exist? CONFIG1_avg_interval : Ensemble-averaging interval (seconds).

BT_u : Eastward ship velocity from bottom tracking. BT_v : Northward ship velocity from bottom tracking. BT_depth : Depth from bottom tracking.

ANCIL2_watrk_scale_factor : Scale factor; multiplier applied to measured velocity. ANCIL2_watrk_hd_misalign : Azimuth correction used to rotate measured velocity. ANCIL2_botrk_scale_factor : Scale factor for bottom tracking. ANCIL2_botrk_hd_misalign : Azimuth correction for bottom tracking. ANCIL2_mn_roll : Ensemble-mean roll. ANCIL2_mn_pitch : Ensemble-mean pitch. ANCIL1_mn_heading : Ensemble-mean heading. ANCIL1_tr_temp : Ensemble-mean transducer temperature. ANCIL2_std_roll : Standard deviation of roll. ANCIL2_std_pitch : Standard deviation of pitch. ANCIL2_std_heading : Standard deviation of heading. ANCIL2_std_temp : Standard deviation of transducer temperature. ANCIL2_last_roll : Last measurement of roll in the ensemble. ANCIL2_last_pitch : Last measurement of pitch. ANCIL2_last_heading : Last measurement of heading. ANCIL2_last_temp : Last measurement of transducer temperature. ANCIL2_last_good_bin : Deepest bin with good velocities. ANCIL2_max_amp_bin : Bin with maximum amplitude based on bottom- detection, if the bottom is within range. ANCIL1_snd_spd_used : Sound speed used for velocity calculations. ANCIL1_pgs_sample : Number of pings averaged in the ensemble.

ACCESS_last_good_bin : Last bin with good data. (-1 if the entire profile is bad.) ACCESS_first_good_bin : First bin with good data. ACCESS_U_ship_absolute : Ship's mean eastward velocity component. ACCESS_V_ship_absolute : Ship's mean northward velocity component.

The following historical variables are not currently used.

NAV_speed : NAV_longitude : NAV_latitude : NAV_direction :

CONFIG1_rol_offset : CONFIG1_pit_offset : CONFIG1_compensation : CONFIG1_pgs_ensemble : Number of pings averaged in the instrument; always 1 for SADCP. CONFIG1_heading_bias : Only relevant for narrowband ADCP data collected with DAS2.48 or earlier (MS-DOS). CONFIG1_ens_threshold :

ANCIL2_rol_misalign : ANCIL2_pit_misalign : ANCIL2_ocean_depth : ANCIL1_best_snd_spd :

percent_3_beam : This may have different meanings depending on the data acquisition system, processing method, and software versions; it is not useful without this context.

............................................................................. comment3=CODAS_processing_note

comment4=CODAS processing note:

Overview

The CODAS database is a specialized storage format designed for shipboard ADCP data. "CODAS processing" uses this format to hold averaged shipboard ADCP velocities and other variables, during the stages of data processing. The CODAS database stores velocity profiles relative to the ship as east and north components along with position, ship speed, heading, and other variables. The netCDF short form contains ocean velocities relative to earth, time, position, transducer temperature, and ship heading; these are designed to be "ready for immediate use". The netCDF long form is just a dump of the entire CODAS database. Some variables are no longer used, and all have names derived from their original CODAS names, dating back to the late 1980's.

Post-processing

CODAS post-processing, i.e. that which occurs after the single-ping profiles have been vector-averaged and loaded into the CODAS database, includes editing (using automated algorithms and manual tools), rotation and scaling of the measured velocities, and application of a time-varying heading correction. Additional algorithms developed more recently include translation of the GPS positions to the transducer location, and averaging of ship's speed over the times of valid pings when Percent Good is reduced. Such post-processing is needed prior to submission of "processed ADCP data" to JASADCP or other archives.

Full CODAS processing

Whenever single-ping data have been recorded, full CODAS processing provides the best end product.

Full CODAS processing starts with the single-ping velocities in beam coordinates. Based on the transducer orientation relative to the hull, the beam velocities are transformed to horizontal, vertical, and "error velocity" components. Using a reliable heading (typically from the ship's gyro compass), the velocities in ship coordinates are rotated into earth coordinates.

Pings are grouped into an "ensemble" (usually 2-5 minutes duration) and undergo a suite of automated editing algorithms (removal of acoustic interference; identification of the bottom; editing based on thresholds; and specialized editing that targets CTD wire interference and "weak, biased profiles". The ensemble of single-ping velocities is then averaged using an iterative reference layer averaging scheme. Each ensemble is approximated as a single function of depth, with a zero-average over a reference layer plus a reference layer velocity for each ping. Adding the average of the single-ping reference layer velocities to the function of depth yields the ensemble-average velocity profile. These averaged profiles, along with ancillary measurements, are written to disk, and subsequently loaded into the CODAS database. Everything after this stage is "post-processing".

note (time):

Time is stored in the database using UTC Year, Month, Day, Hour, Minute, Seconds. Floating point time "Decimal Day" is the floating point interval in days since the start of the year, usually the year of the first day of the cruise.

note (heading):

CODAS processing uses heading from a reliable device, and (if available) uses a time-dependent correction by an accurate heading device. The reliable heading device is typically a gyro compass (for example, the Bridge gyro). Accurate heading devices

United States FRBOP Forecast: Nominal GDP: saar: Mean: QoQ data was reported at 5.159 % in Jun 2018. This records an increase from the previous number of 4.882 % for Mar 2018. United States FRBOP Forecast: Nominal GDP: saar: Mean: QoQ data is updated quarterly, averaging 5.431 % from Dec 1968 (Median) to Jun 2018, with 199 observations. The data reached an all-time high of 13.773 % in Jun 1978 and a record low of -3.802 % in Mar 2009. United States FRBOP Forecast: Nominal GDP: saar: Mean: QoQ data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A002: NIPA 2013: GDP by Expenditure: saar: Current Price: Forecast: Federal Reserve Bank of Philadelphia.

FRBOP Forecast: Nominal GDP: saar: Mean: QoQ: Plus 1 Qtr data was reported at 5.135 % in Jun 2018. This records an increase from the previous number of 4.842 % for Mar 2018. FRBOP Forecast: Nominal GDP: saar: Mean: QoQ: Plus 1 Qtr data is updated quarterly, averaging 5.540 % from Dec 1968 (Median) to Jun 2018, with 199 observations. The data reached an all-time high of 12.357 % in Sep 1975 and a record low of -0.844 % in Mar 2009. FRBOP Forecast: Nominal GDP: saar: Mean: QoQ: Plus 1 Qtr data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A002: NIPA 2013: GDP by Expenditure: saar: Current Price: Forecast: Federal Reserve Bank of Philadelphia.