Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

In 2016, non-interpretive streamflow statistics were compiled for streamgages located throughout the Nation and stored in the StreamStatsDB database for use with StreamStats and other applications. Two previously published USGS computer programs that were designed to help calculate streamflow statistics were updated to better support StreamStats as part of this effort. These programs are named “GNWISQ” (Get National Water Information System Streamflow (Q) files) and “QSTATS” (Streamflow (Q) Statistics). Statistics for 20,438 streamgages that had 1 or more complete years of record during water years 1901 through 2015 were calculated from daily mean streamflow data; 19,415 of these streamgages were within the conterminous United States. About 89 percent of the 20,438 streamgages had 3 or more years of record, and 65 percent had 10 or more years of record. Drainage areas of the 20,438 streamgages ranged from 0.01 to 1,144,500 square miles. The magnitude of annual average streamflow yields (streamflow per square mile) for these streamgages varied by almost six orders of magnitude, from 0.000029 to 34 cubic feet per second per square mile. About 64 percent of these streamgages did not have any zero-flow days during their available period of record. The 18,122 streamgages with 3 or more years of record were included in the StreamStatsDB compilation so they would be available via the StreamStats interface for user-selected streamgages.

El Salvador SV: Time Required to Get Electricity data was reported at 56.000 Day in 2017. This stayed constant from the previous number of 56.000 Day for 2016. El Salvador SV: Time Required to Get Electricity data is updated yearly, averaging 62.000 Day from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 62.000 Day in 2014 and a record low of 56.000 Day in 2017. El Salvador SV: Time Required to Get Electricity data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank: Company Statistics. Time required to get electricity is the number of days to obtain a permanent electricity connection. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

This statistic shows the share of U.S. survey respondents by where they get their food, when eating on the go in 2015. During the survey, 45 percent of survey respondents said they get food from a drive-thru restaurant.

Get statistical data on greenhouse industry statistics for Ontario and Canada.

This dataset includes:

• square footage
• sales
• employee numbers
• selected input costs

Ivory Coast CI: Time Required to Get Electricity data was reported at 55.000 Day in 2017. This stayed constant from the previous number of 55.000 Day for 2016. Ivory Coast CI: Time Required to Get Electricity data is updated yearly, averaging 59.000 Day from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 59.000 Day in 2015 and a record low of 55.000 Day in 2017. Ivory Coast CI: Time Required to Get Electricity data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank: Company Statistics. Time required to get electricity is the number of days to obtain a permanent electricity connection. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

As of 2023, around 38 percent of U.S. LGBTQ youth who wanted mental health care were unable to get it because they could not afford it. The statistic illustrates the share of U.S. LGBTQ youth who wanted mental health care but were unable to get it for select reasons as of 2023.

Find information using interactive applications to get statistics from multiple surveys.

Financial overview and grant giving statistics of Get Up and Go Ministries Corporation

The statistic presents the cost of a 30-second TV spot during How to Get Away with Murder in the United States from 2014/15 to 2019/20 TV season. A survey among media buying agencies showed that a 30 second TV ad during the broadcast of How to Get Away with Murder during the 2019/20 season cost 99.9 thousand U.S. dollars.

High School Heights Dataset

You will find three datasets containing heights of the high school students.

All heights are in inches.

The data is simulated. The heights are generated from a normal distribution with different sets of mean and standard deviation for boys and girls.

There are 500 measurements for each gender.

Here are the datasets:

• hs_heights.csv: contains a single column with heights for all boys and girls. There's no way to tell which of the values are for boys and which ones are for girls.

• hs_heights_pair.csv: has two columns. The first column has boy's heights. The second column contains girl's heights.

• hs_heights_flag.csv: has two columns. The first column has the flag is_girl. The second column contains a girl's height if the flag is 1. Otherwise, it contains a boy's height.

To see how I generated this dataset, check this out: https://github.com/ysk125103/datascience101/tree/main/datasets/high_school_heights

Image by Gillian Callison from Pixabay

Financial overview and grant giving statistics of Get to Work Illinois Inc.

This web service shows the centroids for countries for which there are minerals statistics data (Imports, Exports, Production) in the World Mineral Statistics database. A GetFeatureInfo request can retrieve some of the data for the country queried, but to get all data you should used the associate WFS.

Tanzania TZ: Time Required to Get Electricity data was reported at 109.000 Day in 2017. This stayed constant from the previous number of 109.000 Day for 2016. Tanzania TZ: Time Required to Get Electricity data is updated yearly, averaging 109.000 Day from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 382.000 Day in 2009 and a record low of 109.000 Day in 2017. Tanzania TZ: Time Required to Get Electricity data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Company Statistics. Time required to get electricity is the number of days to obtain a permanent electricity connection. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Working with SPSS, SAS, Shazam, Excel and STATA users - why are there so many statistical packages and how do we keep our users happy while making our lives easier, outside of therapy?

Find out about retirement trends in PBGC's data tables. The tables include statistics on the people and pensions that PBGC protects, including how many Americans are in PBGC-insured pension plans, how many get PBGC benefits, and where they live. This data set will be updated periodically. (Updated annually)

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

This statistics bank shows how business has made use of ordinary and extraordinary support schemes throughout the corona crisis.

A number of measures were initiated to increase activity in Norwegian business, prevent unnecessary closures and to get as many people as possible into work during the corona crisis. Several actors in the industry-oriented instrument apparatus were given additional tasks and new extraordinary measures were created, such as the compensation scheme through the Tax Agency.

In order to be able to monitor the use of the measures, the Ministry of Trade and Fisheries has commissioned Innovation Norway to expand its reporting to include regularly updated data on how the measures affect business. Innovation Norway has, with assistance from Societal Economic Analysis, also obtained information on schemes other than its own in order to get a more complete picture of the use of measures.

The statistics bank contains statistics on allocations per week from the business-oriented policy apparatus. The statistics bank is updated every month and contains data from week 1 of 2020.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D