We show that the expected value of the largest order statistic in Gaussian samples can be accurately approximated as (0.2069 ln (ln (n))+0.942)4, where n∈[2,108] is the sample size, while the standard deviation of the largest order statistic can be approximated as −0.4205arctan(0.5556[ln(ln (n))−0.9148])+0.5675. We also provide an approximation of the probability density function of the largest order statistic which in turn can be used to approximate its higher order moments. The proposed approximations are computationally efficient, and improve previous approximations of the mean and standard deviation given by Chen and Tyler (1999).

Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

Descriptive statistics including mean (M) and standard deviation (SD) are shown for each dependent measure across age cohorts and language groups at baseline.

Descriptive statistics including mean and standard deviation of dependent and independent variables for all villages and for villages in warm humid and warm sub-humid zones.

The Midrange Speakers market plays a pivotal role in the audio industry, offering a critical balance of sound quality that captures the rich, detailed frequencies between bass and treble. As an essential component in sound systems ranging from home theaters to professional audio setups, midrange speakers excel at de

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

This article will discuss how to find weighted standard deviation of groups while there is no data about the individuals inside the groups. Sometimes we have partial information about averages values and groups with weight of the group but how can we find out the standard deviation of the whole groups without the measurements of each individual? a suggestion, verification and practical example will be shown.

Monte Carlo simulations are commonly used to test the performance of estimators and models from rival methods under a range of data generating processes. This tool improves our understanding of the relative merits of rival methods in different contexts, such as varying sample sizes and violations of assumptions. When used, it is common to report the bias and/or the root mean squared error of the different meth- ods. It is far less common to report the standard deviation, overconfidence, coverage probability, or power. Each of these six performance statistics provides important, and often differing, information regarding a method’s performance. Here, we present a structured way to think about Monte Carlo performance statistics. In replications of three prominent papers, we demonstrate the utility of our approach and provide new substantive results about the performance of rival methods.

What you get:

Upvote! The database contains +40,000 records on US Gross Rent & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 325,272 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to upvote. Upvote right now, please. Enjoy!

Get the full free database with coupon code: FreeDatabase, See directions at the bottom of the description... And make sure to upvote :) coupon ends at 2:00 pm 8-23-2017

Gross Rent & Geographic Statistics:

• Mean Gross Rent (double)
• Median Gross Rent (double)
• Standard Deviation of Gross Rent (double)
• Number of Samples (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The data set originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 325,272 location for Free Database Coupon Code:

Don't settle. Go big and win big. Optimize your potential**. Access all gross rent records and more on a scale roughly equivalent to a neighborhood, see link below:

• Website: Golden Oak Research make sure to upvote

A small startup with big dreams, giving the every day, up and coming data scientist professional grade data at affordable prices It's what we do.

In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preprocessing phase has been to divide the data by the standard deviation along each dimension. Like division by the standard deviation, the great majority of scaling techniques can be said to have roots in some sort of statistical take on the data. Here we explore the use of multidimensional shapes of data, aiming to obtain scaling factors for use prior to clustering by some method, like k-means, that makes explicit use of distances between samples. We borrow from the field of cosmology and related areas the recently introduced notion of shape complexity, which in the variant we use is a relatively simple, data-dependent nonlinear function that we show can be used to help with the determination of appropriate scaling factors. Focusing on what might be called “midrange” distances, we formulate a constrained nonlinear programming problem and use it to produce candidate scaling-factor sets that can be sifted on the basis of further considerations of the data, say via expert knowledge. We give results on some iconic data sets, highlighting the strengths and potential weaknesses of the new approach. These results are generally positive across all the data sets used.

A variance is required when an application has submitted a proposed project to the Department of Permitting Services and it is determined that the construction, alteration or extension does not conform to the development standards (in the zoning ordinance) for the zone in which the subject property is located. A variance may be required in any zone and includes accessory structures as well as primary buildings or dwellings. Update Frequency : Daily

Descriptive statistics of the input data with mean, standard deviation (SD), median, minimum and maximum, 0.5%, 5%, 95% and 99.5% quantiles (90% and 99% confidence intervals).

Chen, Stacey H, (2008) "Estimating the Variance of Wages in the Presence of Selection and Unobservable Heterogeneity." Review of Economics and Statistics 90:2, 275-289.

Mean-variance data collections for portfolio optimization problems based on time series of daily stock prices for New York stock exchange. These data collections can be used for investment portfolio optimization research.NYSE includes over 2000 stocks. These data includes randomly selected sets of size equal to 200, 300, 400, 500, 600, 700, 800 and 900. Each set include the succesive yearly data from 2014, 2015, 2016, 2017, 2018, and 2019 grouped in a single folder. While each folder includes the data saved in a text file following the format used by J. E. Beasley in OR Library (http://people.brunel.ac.uk/~mastjjb/jeb/orlib/portinfo.html).Example structure of files in the 200 set is presented below:- 2014 + JKMP2_200_2014_1 + JKMP2_200_2014_2 + JKMP2_200_2014_3 ... + JKMP2_200_2014_12- 2015 + JKMP2_200_2015_1 + JKMP2_200_2015_2 + JKMP2_200_2015_3 ... + JKMP2_200_2015_12- 2016 + JKMP2_200_2016_1 + JKMP2_200_2016_2 + JKMP2_200_2016_3 ... + JKMP2_200_2016_12- 2017 + JKMP2_200_2017_1 + JKMP2_200_2017_2 + JKMP2_200_2017_3 ... + JKMP2_200_2017_12- 2018 + JKMP2_200_2018_1 + JKMP2_200_2018_2 + JKMP2_200_2018_3 ... + JKMP2_200_2018_12- 2019 + JKMP2_200_2019_1 + JKMP2_200_2019_2 + JKMP2_200_2019_3 ... + JKMP2_200_2019_12A detailed description of the data collections can be found in the README file of the dataset. For each set of stocks, we estimated the correlation matrix and the vector of mean returns, based on the corresponding time series.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

Phenotypic variance should have a hierarchical structure because differences arise between species, between individuals within species, and, for labile phenotypes, also within individuals across circumstances. Within-individual variance could exist because of responses to variable environments (plasticity) or because exhibiting variance per se has fitness consequences. To evolve, the latter requires between-individual variance in within-individual variance. Here, we investigate the parental behavior of female red-winged blackbirds (Agelaius phoeniceus) and assess if the distribution of within-individual variance also differs between individuals or changes with respect to environmental conditions. We used a statistical approach that models both the mean and variance iteratively. We found that the amount of food delivered per second on each visit was influenced by female identity, nestling age, and the location (on vs. off territory) where the female foraged. Moreover, we also found that unexplained within-individual variance (residual variance), after controlling for mean effects, independently declined with nestling age and was smaller when females foraged off their mate's territory. In addition, females differed in residual variance more than expected by chance. These results confirm that phenotypic variance has a hierarchical structure and they support preconditions for the evolution of mean phenotypic values as well as the variance in phenotype. In the case of provisioning as a form of parental care, our data suggest that female red-winged blackbirds could be managing stochastic variance either directly through choice of foraging location or indirectly in how they budget their time, and we discuss these patterns in relation to adaptive variance sensitivity.

A sieve analysis (or gradation test) is a practice or procedure commonly used in civil engineering to assess the particle size distribution (also called gradation) of a granular material.

As part of the Sediment Analysis and Geo-App (SAGA) a series of data processing web services are available to assist in computing sediment statistics based on results of sieve analysis. The Standard Deviation first computes the percentiles for D5, D16, D35, D84,D95 and then uses the formula, (D16-D84)/4)+(D5-D95)/6

Percentiles can also be computed for classification sub-groups: Overall (OVERALL), <62.5 um (DS_FINE), 62.5-250um (DS_MED), and > 250um (DS_COARSE)

Parameter #1: Input Sieve Size, Percent Passing, Sieve Units.

• Semi-colon separated. ex: 75000, 100, um; 50000, 100, um; 37500, 100, um; 25000,100,um; 19000,100,um
• A minimum of 4 sieve sizes must be used. Units supported: um, mm, inches, #, Mesh, phi
• All sieve sizes must be numeric

Parameter #2: Subgroup

• Options: OVERALL, DS_COARSE, DS_MED, DS_FINE
• The statistics are computed for the overall sample and into Coarse, Medium, and Fine sub-classes
• Coarse (> 250 um) DS_COARSE
• Medium (62.5 – 250 um) DS_MED
• Fine (< 62.5 um) DS_FINE
• OVERALL (all records)

Parameter #3: Outunits

• Options: phi, m, um

Standard deviation of responses for 'Life Satisfaction' in the First ONS Annual Experimental Subjective Wellbeing survey.

The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.

• Overall, how satisfied are you with your life nowadays?
• Overall, to what extent do you feel the things you do in your life are worthwhile?
• Overall, how happy did you feel yesterday?
• Overall, how anxious did you feel yesterday?

This dataset presents results from the first of these questions, "Overall, how satisfied are you with your life nowadays?". Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.

Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.

The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘life satisfaction’ question answers in the range 0-6 are taken to be low wellbeing.

This dataset contains the standard deviation of the responses, alongside the corresponding sample size.

The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.

At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.

The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.

The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.

The original data is available from the ONS website.

Detailed information on the APS and the Subjective Wellbeing dataset is available here.

As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.

ABSTRACT In order to search for an ideal test for multiple comparison procedures, this study aimed to develop two tests, similar to the Tukey and SNK tests, based on the distribution of the externally studentized amplitude. The test names are Tukey Midrange (TM) and SNK Midrange (SNKM). The tests were evaluated based on the experimentwise error rate and power, using Monte Carlo simulation. The results showed that the TM test could be an alternative to the Tukey test, since it presented superior performances in some simulated scenarios. On the other hand, the SNKM test performed less than the SNK test.

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented. This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.