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.

Find detailed analysis in Market Research Intellect's Midrange High Chairs Market Report, estimated at USD 1.2 billion in 2024 and forecasted to climb to USD 1.8 billion by 2033, reflecting a CAGR of 5.5%.Stay informed about adoption trends, evolving technologies, and key market participants.

Find details of Itt Goulds Pumps Midrange Warehouse Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

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

Script for calculate variance partition method and hierarchical partition method for scales regional and local

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.

According to Cognitive Market Research, the global mid range hotel market size is USD XX billion in 2023 and will grow at a compound annual growth rate (CAGR) of 6.00% from 2023 to 2030

The demand for mid range hotel market is rising due to therise of online booking platforms and travel websites has greatly enhanced the prominence of mid-range hotels.
Demand for one double bed remains higher in the mid-range hotel market.
The online booking category held the highest mid-range hotel market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific mid-range hotel market will experience the strongest growth until 2030.

Market Dynamics of MID Range Hotel Market

Key Drivers of MID Range Hotel Market

Enhanced Guest Experience and Amenities to Provide Viable Market Output

The mid-range hotel market is the constant focus on enhancing the guest experience. Mid-range hotels are increasingly investing in amenities and services that appeal to a wide range of travelers, including families, business professionals, and tourists. These hotels are incorporating modern technology, such as mobile check-in services and high-speed Wi-Fi, to cater to the needs of tech-savvy guests.

In January 2023, Marriott revealed the inauguration of the first-ever Westin Hotels and Resorts establishment in Uttarakhand, India. The Westin Resort and Spa, Himalayas, is now open for business.

Additionally, they are expanding their offerings to include on-site restaurants, fitness centers, conference facilities, and recreational activities. By providing a diverse array of services, mid-range hotels create a compelling value proposition for guests, ensuring customer satisfaction and loyalty. This focus on guest experience drives positive reviews, repeat business, and positive word-of-mouth referrals, contributing significantly to the growth of the mid-range hotel sector.

Strategic Location and Accessibility to Propel Market Growth

The strategic location of mid-range hotels plays a pivotal role in driving their success. These hotels are often situated in prime areas, offering easy accessibility to popular tourist attractions, business districts, transportation hubs, and entertainment venues. Their convenient locations make them an attractive choice for travelers seeking both comfort and accessibility. Mid-range hotels frequently capitalize on their proximity to key points of interest, allowing guests to explore the local culture and attractions effortlessly. Moreover, their accessibility to public transportation options and major highways makes them convenient choices for travelers, ensuring a steady flow of guests throughout the year.

Restraint Factors of Mid Range Hotel Market

Rising Economic Fluctuations to Hinder Market Growth

The mid-range hotel market is its sensitivity to economic fluctuations. During periods of economic uncertainty, consumers tend to reduce their travel budgets, opting for more budget-friendly accommodation options or cutting down on travel altogether. Mid-range hotels often find themselves in a precarious position, as they need to balance providing quality services with competitive pricing. Economic downturns can lead to reduced occupancy rates and lower average room prices, impacting the overall revenue of mid-range hotels. Additionally, these hotels face pressure from both ends: the need to maintain a certain level of service quality to attract guests and the necessity to keep prices affordable.

Pressure from Alternative Accommodation Platforms

One of the key restraints impacting the mid-range hotel market is the growing competition from alternative accommodation providers, such as Airbnb, Vrbo, and other short-term rental platforms. These alternatives often offer larger spaces, home-like amenities, and flexible pricing, which can be more appealing to families, groups, and long-stay travelers. Many travelers now prefer the personalized, local experience that these platforms promote something mid-range hotels may struggle to replicate within their standardized service models. As consumer preferences shift toward more authentic and cost-effective lodging options, mid-range hotels face the challenge of redefining their value proposition to retain market share, especially in leisure-driven travel segments.

Opportunity for mid range hotel market

Rising Demand for Affordable Yet Comfortable Travel Options is C...

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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.

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.

In survival analyses, inverse-probability-of-treatment (IPT) and inverse-probability-of-censoring (IPC) weighted estimators of parameters in marginal structural Cox models (Cox MSMs) are often used to estimate treatment effects in the presence of time-dependent confounding and censoring. In most applications, a robust variance estimator of the IPT and IPC weighted estimator is calculated leading to conservative confidence intervals. This estimator assumes that the weights are known rather than estimated from the data. Although a consistent estimator of the asymptotic variance of the IPT and IPC weighted estimator is generally available, applications and thus information on the performance of the consistent estimator are lacking. Reasons might be a cumbersome implementation in statistical software, which is further complicated by missing details on the variance formula. In this paper, we therefore provide a detailed derivation of the variance of the asymptotic distribution of the IPT and IPC weighted estimator and explicitly state the necessary terms to calculate a consistent estimator of this variance. We compare the performance of the robust and the consistent variance estimator in an application based on routine health care data and in a simulation study. The simulation reveals no substantial differences between the two estimators in medium and large data sets with no unmeasured confounding, but the consistent variance estimator performs poorly in small samples or under unmeasured confounding, if the number of confounders is large. We thus conclude that the robust estimator is more appropriate for all practical purposes.

The possibility to use colour data, a fast and inexpensive method of proxy data generation, extracted from two selected loess-paleosol sequences is discussed here. We compare the outcome from analysing outcrop images taking by digital cameras in the field and spectral colour data as determined under controlled laboratory conditions. By nature, differences can be expected due to differences in illumination, moisture, and sample preparation. Outcrop inclination may be an issue for photographs; correcting for this is possible when marks can be used for rectification. In both cases the data extracted from images match the visual impression of photos well, and are useful for obtaining a more quantitative measure for field observations. Smoothness (as measured by autocorrelation) is high for an image from Achenheim/France, where an image with a width of ca. 1.1 m and a depth of 1.6 m was analysed. Data from a narrower image part from Sanovita/Romania are noisier. In both example cases, a significant correlation between data extracted by digital image analysis and laboratory measurements could be established, suggesting that image analysis may be a useful tool where outcrop- and light-conditions allow useful photographs, especially where high resolution proxy data is required.

As of August 2025, theTesla K40m achieved the best PassMark performance score among high mid-range video cards with a score of 3,143. The majority of the top 10 high mid-range video cards are either provided by Radeon or by Nvidia, such as Tesla.

Plan Information Plan name: 1k Variance or Less Description: Eastern Plains, Western Slope, Front Range Mountains, Denver Metro, and Colorado Springs districts. County boundaries are only split in the metro area counties. All deviations from target are 1,000 or less.Plan ObjectivesThe primary goal of this plan is to get every district below a variance of 1,000 people. It is similar to the "Mountains, Plains, Urban" plan I submitted, with the following differences:* Custer and Huerfano Counties are an isolated part of D3; they've probably got more common cause with Pueblo and Las Animas than with the San Luis Valley.* D4 includes the rural parts of Adams, Arapahoe, and Douglas Counties; drawing the border in Douglas felt particularly arbitrary and may split some cohesive neighborhoods.* Clear Creek County is in D2 rather than D3; residents there probably share concerns with other east-slope mountain communities in D2 and makes the commute to a district office shorter and safer in the winter.* Denver Metro districts shift, with Aurora joining with Adams and north metro rather than with Arapahoe for D6, D7 covering parts of Jefferson plus Douglas counties rather than staying west/north of Denver, and D8 becoming a north metro district rather than south metro

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Blockchain data query: Standard deviation (Volatility) of USD0 Price

Responses obtained in the 24-1 fractional factorial experimental design and triplicate at the central points to calculate the average, standard deviation and relative standard deviation.

Comparison of SINR calculation for conventional MVDR, PSO-MVDR, GSA-MVDR, SLGSA-MVDR [28] and ECGSA-MVDR for user at 0° and interference at 30°.

Note that response latency is measured from the beginning of the trial.