United States CSI: Expected Interest Rates: Next Yr: Go Down data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 6.000 % for Apr 2018. United States CSI: Expected Interest Rates: Next Yr: Go Down data is updated monthly, averaging 11.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 54.000 % in Jun 1980 and a record low of 3.000 % in May 2014. United States CSI: Expected Interest Rates: Next Yr: Go Down data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.

This data set provides energetic (MeV) ion count rates and events measured by the Heavy Ion Counter (HIC) instrument on the Galileo spacecraft. These data are derived from high time resolution raw data that were recorded to tape and then played back later in the orbit. There are two basic types of data files associated with the full-rate reduced data: Detector Count Rates and Events (Pulse Heights).

This dataset contains the predicted prices of the asset Go Game Token over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains counts and rates (per 10,000 residents) of asthma emergency department (ED) visits among Californians. The table “Asthma Emergency Department Visit Rates by County” contains statewide and county-level data stratified by age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native). The table “Asthma Emergency Department Visit Rates by ZIP Code” contains zip-code level data stratified by age group (all ages, 0-17, 18+). The data are derived from the Department of Health Care Access and Information emergency department database. These data include emergency department visits from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes. On October 1, 2015, diagnostic coding for asthma transitioned from ICD9-CM (493) to ICD10-CM (J45). Because of this change, CDPH and CDC do not recommend comparing data from 2015 (or earlier) to 2016 (or later). NOTE: Rates are calculated from the total number of asthma emergency department visits (not the unique number of individuals).

Asthma ED Visit Rates by ZIP

Counts and Rates by Age Group in California

By Health [source]

About this dataset

This dataset presents a comprehensive look into the prevalence of asthma among Californian residents in terms of emergency department visits. Using age-adjusted rates and county FIPS codes, it offers an accurate snapshot of the prevalence rates per 10,000 people and provides key insights into how this condition affects certain age groups by ZIP Code. With its easy to use associated map view, this dataset allows users to quickly gain deeper knowledge about this important health issue and craft meaningful solutions to address it

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How to use the dataset

This dataset contains counts and rates of asthma related emergency department visits by ZIP Code and age group in California. This data can be useful when doing research on asthma related trends or attempting to find correlations between environmental factors, prevalence of disease and geography.

• Select a year for analysis - the latest year for which data is available is the default selection, but other years are also listed in the dropdown menu.
• Select an Age Group to analyze - use the provided dropdown menus to select one or more age groups (all ages, 0-17, 18+) if you wish to analyze two different age groups in your analysis.
• Define a geographical area by selecting a ZIP code or County Fips code from which you wish to obtain your dataset from based on its availability or importance in your research question .
• View and download relevant data - after selecting all of the desired criteria (year,Age group(s), ZIP code/County FIPS Code) click “View Data” then “Download” at the bottom right corner of window that opens up
  5 Analyze information found - use software such as Microsoft Excel or open source programs like Openoffice Calc to gain insight into your downloaded dataset through statistics calculations, graphs etc.. In particular look out for anomalies that could signify further investigation

Research Ideas

• Identifying the geographic clusters of asthma sufferers by analyzing the rate of emergency department visits with geographic mapping.
• Developing outreach initiatives to areas with a high rate of ED visits for asthma to provide education, interventions and resources designed towards increasing preventive care and reducing preventable complications due to lack of access or knowledge about available services in these communities.
• Assessing disparities in ED visit rates for asthma between age groups as well as between urban and rural areas or different socio-economic groups within counties or ZIP codes in order to identify areas where there is a need for increased interventions, services and other resources related to asthma care in order to reduce the burden or severity of this chronic condition among particularly vulnerable population groups

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Asthma_Emergency_Department_Visit_Rates_by_ZIP_Code.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | ZIP code | The ZIP code of the area the data was collected from. (String...

The benchmark interest rate in the United States was last recorded at 4 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.

This dataset contains the predicted prices of the asset GO-RESTFUL github.com/emicklei/GO-RESTFUL over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

The data and code in this replication package reproduce all tables, figures, and all numbers that appear in the text in our manuscript "When Interest Rates Go Low, Should Public Debt Go High?". The package consists of two parts. The first part numerically solves the OLG models presented in Section 3 through 5 and generates all of the 10 figures, 6 tables as well as calibrated structural parameters in the paper using Matlab. The second part employs three data sources to compute the calibration targets in Section 5 in the paper using R. The replicator should run each part separately, with the first taking approximately 4-5 hours on an 8-core CPU and the second less than 1 minute.

This dataset contains the predicted prices of the asset lets fucking go over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

United States CSI: Expected Inflation: Next Yr: Up by 1-2% data was reported at 29.000 % in May 2018. This stayed constant from the previous number of 29.000 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: Up by 1-2% data is updated monthly, averaging 18.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 34.000 % in Oct 2016 and a record low of 1.000 % in May 1980. United States CSI: Expected Inflation: Next Yr: Up by 1-2% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

Nearly all surveyed online shoppers from different territories were expecting increases in delivery fees in 2023. In a survey conducted in late 2022, around ** percent of e-shoppers in Italy were expecting the cost of e-commerce delivery to go up in 2023. In France, this figure stood at ** percent of online shoppers.

The Annual Travel Insurance Cost Report by Squaremouth analyzes thousands of travel insurance policies to provide insights into the cost and value of annual versus single-trip coverage for U.S. travelers. The dataset includes comparisons of average costs, daily rates, and coverage types, as well as trends by traveler age and policy type. The data shows generational pricing differences and further distinguishes between the cost of medical-only and comprehensive annual plans, illustrating how coverage inclusions affect premiums.

CSI: Expected Inflation: Next Yr: Up by 6-9% data was reported at 3.000 % in May 2018. This stayed constant from the previous number of 3.000 % for Apr 2018. CSI: Expected Inflation: Next Yr: Up by 6-9% data is updated monthly, averaging 5.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 26.000 % in Apr 1978 and a record low of 1.000 % in Sep 2010. CSI: Expected Inflation: Next Yr: Up by 6-9% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

1Light Drinkers.2Heavy Drinkers.*Mean reaction time (± standard deviation) to Go stimuli and commission error rates (in %) of light and heavy drinkers in each context (neutral, alcohol-related and non-alcohol-related).†Statistically significant at p = 0.039.

CSI: Expected Inflation: Next 5 Yrs data was reported at 2.400 % in Jul 2018. This records a decrease from the previous number of 2.600 % for Jun 2018. CSI: Expected Inflation: Next 5 Yrs data is updated monthly, averaging 2.900 % from Feb 1979 (Median) to Jul 2018, with 382 observations. The data reached an all-time high of 9.700 % in Feb 1980 and a record low of 2.300 % in Dec 2016. CSI: Expected Inflation: Next 5 Yrs data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data was reported at 2.500 % in May 2018. This stayed constant from the previous number of 2.500 % for Apr 2018. United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data is updated monthly, averaging 3.200 % from Feb 1979 (Median) to May 2018, with 380 observations. The data reached an all-time high of 10.900 % in Feb 1980 and a record low of 2.200 % in Apr 1999. United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

United States CSI: Expected Inflation: Next Yr: Up by 15%+ data was reported at 1.000 % in May 2018. This records a decrease from the previous number of 2.000 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: Up by 15%+ data is updated monthly, averaging 3.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 27.000 % in Mar 1980 and a record low of 0.000 % in Sep 2004. United States CSI: Expected Inflation: Next Yr: Up by 15%+ data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

The statistic shows the average conversion rates for the travel industry in 2015, by device and region. In the United States, *** percent of smartphone visits to travel sites were converted into purchases. U.S. desktop visits had a *** percent conversion rate.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/.

This dataset contains counts and rates (per 10,000 residents) of asthma (ICD9-CM, 493.0-493.9) emergency department visits among California residents by County and age group (all ages, 0-17, 18+). The data are derived from the Department of Health Care Access and Information emergency department databases.

These data include emergency department visits from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes (ICD9-CM).

Starting in 2019, HCAI classified non-Hispanic individuals who identified with two or more races as "Multiracial." Previously these were assigned to a single other race.

NOTE: Rates are calculated from the total number of Asthma ED Visits (not the unique number of individuals).