Cost predictions at quartile measures of quality: Summed events measure of quality.

Basic characteristics of participants according to quartiles of RBC count in males.

Model 1: adjusted for age at follow-up, gender, intervention group.Model 2: as model 1 plus adjustment for z-score of birth weight, father's social class, lifetime smoking, alcohol intake and exercise.1Insulin Sensitivity Index whilst fasting = 104/(I0×G0).2Corrected Insulin Response at 30 minutes = 100×I30/(G30×(G30−70).†Outcomes were natural-log transformed, and coefficients and confidence intervals represent a change in ratio of geometric means per quartile of formula/cows' milk intake.*Reference category is those in the lowest quartile of infant formula/cow's milk intake, amongst those who received infant formula/cow's milk.

NY State Community Health Indicators

Obesity and Diabetes Related Indicators 2008–2012

By Health Data New York [source]

About this dataset

This dataset contains New York State county-level data on obesity and diabetes related indicators from 2008 - 2012. It includes information about counties' population health status, such as the number of events, percentage/rate, 95% confidence interval, measured units and more. Analyzing this data provides insight into how communities across New York State are impacted by these diseases and how we can work together to create healthier living environments for everyone. This dataset is released under a Terms of Service license agreement – make sure to read through and understand the details if you plan to use it in any research or commercial application

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How to use the dataset

This dataset contains county-level data on obesity and diabetes related indicators in New York State. As such, it can be used to research indicators related to general health in various counties of the state.

To use this dataset effectively, first become familiar with the columns included and their meanings: - County Name: The name of the county. (String) - County Code: The code of the county. (Integer) - Region Name: The name of the region. (String) - Indicator Number: The number of the indicator. (Integer) - Total Event Counts: The total number of events related to the indicator.(Integer)
- Denominator: The denominator used to calculate the percentage/rate.(Integer) - Denominator Note: Any additional notes related to the denominator.(String) - Measure Unit :The unit of measure used for this rate/percentage .(String). - Percentage/Rate :The percentage/rate calculated using denominator and observed count data .(Float). - 95% CI :The 95% confidence interval associated with any defined rate or percentage.(Float). - Data Comments :Any additional comments relevant to this data source or indicator .(String ). - Data Years :Years covered by this particular indicator observation .(String ). - Data Sources :Sources from which we have drawn our data for indicators involving counties from different regions .(Strings). - Quartile :Quartiles are derived when all geographic entities are ranked according to a specific metric score ,and are then cut into quartiles based on speed score =0= bottom quarter; =1= middle two quarters combined; =2= top quarter..(Integer). - Mapping Distribution ;A visual representation that includes mapping details regarding how Indicators relating either disease rates or characteristics are positioned across States, regions and counties as well as any trends plus other pertinent mapping information ,such as health resource availability.(In pair plot form form otherwise text will present an informational string.). Location ;Area where distribution around space occurs..e point feature with a single location ID retrieved from geoplanet proxy service.. (string ).

Using these columns, you can find out demographic information about your chosen county such as obesity rate and diabetes incidence etc., enabling you better understand its health situation overall. Additionally,this dataset also provides important comparison features such as quartiles rankings

Research Ideas

• Analysing the geographic distribution of obesity and diabetes related indicators by county in New York State, in order to identify areas which may require greater levels of intervention and preventative health measures.

• Evaluating trends over time for different counties to assess whether policies or programs have had an impact on indicators relating to obesity and diabetes within the given area.

• Using machine learning techniques such as clustering analysis or predictive modelling, to identify patterns within the data which can be used to better inform preventative health interventions across New York State

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: community-health-obesity-and-diabetes-related-indicators-2008-2012-1.csv | Column name | Description | |:-------------------------|:-----------------------------------------------------------------------------------------| | **Count...

Sex- and age-adjusted effects and corresponding 95% confidence intervals (95% CI) on body mass index (BMI) in linear regression models of the joint effects of tertiles of a BMI-associated genetic risk score (GRSBMI) and socioeconomic position indicators, calculated separately for income quartiles and education categories, with the group of having a low genetic risk score and the highest socioeconomic position as reference.

Data were presented as means with SDs or number with percentage. MAP, mean arterial pressure; PaCO2, partial pressure of carbon dioxide; PaO2, partial pressure of oxygen; BUN, blood urea nitrogen; AST, aspartate transaminase; ALT, alanine transaminase.*Plasma PQ concentration performed in 79 cases out of a total of 136 patients.

AL refers to the axial length, CCT to the central corneal thickness, ACD to the external phakic anterior chamber depth measured from the corneal front apex to the front apex of the crystalline lens, LT to the central thickness of the crystalline lens, R1 and R2 to the corneal radii of curvature for the flat and steep meridians, Rmean to the average of R1 and R2, PIOL to the refractive power of the intraocular lens implant, and SEQ to the spherical equivalent power achieved 5 to 12 weeks after cataract surgery.

Differences between lower and upper quartiles of scales.

Explore the dynamic world of IPL cricket auctions with this comprehensive dataset covering player details, countries, teams, base prices, winning bids (in INR lakhs), and auction years from 2013 to 2023. Dive into the exciting transactions, revealing the financial dynamics and team selections that shaped each IPL season. Uncover insights into player valuations, team strategies, and auction trends across the years, encapsulating the essence of one of cricket's premier leagues.

Key Features

How to Use the IPL Auction Dataset

1. Exploring Player Details:

   • Investigate individual player details by examining columns like 'Player Name,' 'Country,' 'Team,' 'Base Price,' 'Winning Bid,' and 'Year of Auction.'

2. Analyzing Auction Trends:

   • Calculate statistics like mean, median, and quartiles for 'Base Price' and 'Winning Bid' to understand the distribution of auction prices.

3. Team-wise Insights:

   • Explore team-wise player distributions over the years to identify teams' strategies and preferences during auctions.

4. Visualizing Insights:

   • Visualize trends using plots to highlight patterns, fluctuations, and correlations within the auction data.

5. Deriving Strategic Insights:

   • Derive strategic insights by comparing winning bids, identifying player performance trends, and understanding how team dynamics have evolved over the years.

6. Cross-Referencing Data:

   • Cross-reference player details with match performance data or external sources to gain a comprehensive understanding of player contributions and team dynamics.

7. Extracting Actionable Insights:

   • Extract actionable insights to inform strategic decisions for upcoming IPL seasons, player acquisitions, and team management.

8. Sharing Findings:

   • Share your findings, visualizations, and analyses with the community on platforms like Kaggle, fostering collaborative insights within the cricket and data science communities.

Use this dataset as a valuable resource to unravel the complexities of IPL auctions, enhance your analytical skills, and contribute to the collective understanding of cricket's premier league dynamics.

Characteristics of the study population by platelet count quartile.

BasEPC = Basal count of EPCAMI = Acute myocardial infarctionBMI = Body mass indexAB = Atherosclerotic BurdenIMT = Intima-media thicknessNVE = New vascular eventACS = Acute coronary syndromeACV = Acute cardiovascular event.Bivariate comparison between study variables and EPC quartiles.

BasCEC = Basal count of circulating endothelial cellsAMI = Acute myocardial infarctionBMI = Body mass indexAB = Atherosclerotic BurdenIMT = Intima media thicknessNVE = New vascular eventACS = Acute coronary syndromeACV = Acute cardiovascular event.Bivariate comparison between study variables and CEC quartiles.