Historical Dataset of Range Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),Asian Student Percentage Comparison Over Years (2000-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1992-2023),Free Lunch Eligibility Comparison Over Years (2003-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2003-2023)

Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

About this dataset

Context

The dataset presents median household incomes for various household sizes in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, South Range did not include 4, 5, 6, or 7-person households. Across the different household sizes in South Range the mean income is $51,844, and the standard deviation is $18,238. The coefficient of variation (CV) is 35.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,226. It then further increased to $65,869 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/south-range-mi-median-household-income-by-household-size.jpeg" alt="South Range, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

Local adaptation at range edges influences species’ distributions and how they respond to environmental change. However, the factors that affect adaptation, including gene flow and local selection pressures, are likely to vary across different types of range edge. We performed a reciprocal transplant experiment to investigate local adaptation in populations of Plantago lanceolata and P. major from central locations in their European range and from their latitudinal and elevation range edges (in northern Scandinavia and Swiss Alps, respectively). We also characterized patterns of genetic diversity and differentiation in populations using molecular markers. Range-centre plants of P. major were adapted to conditions at the range centre, but performed similarly to range-edge plants when grown at the range edges. There was no evidence for local adaptation when comparing central and edge populations of P. lanceolata. However, plants of both species from high elevation were locally adapted when compared with plants from high latitude, although the reverse was not true. This asymmetry was associated with greater genetic diversity and less genetic differentiation over the elevation gradient than over the latitudinal gradient. Our results suggest that adaptation in some range-edge populations could increase their performance following climate change. However, responses are likely to differ along elevation and latitudinal gradients, with adaptation more likely at high elevation. Furthermore, based upon these results, we suggest that gene flow is unlikely to constrain adaptation in range-edge populations of these species.

Bold values: range outside the wild one; n.c. = data not collected;* = observation carried out only on the left side of the body.

The ranges of total mutations as well as the non-conservative mutations were calculated for each protein group including: AL-Vκ, AL-Vλ, Normal Vκ, Normal Vλ, and Multiple Myeloma.

*Calculated by using the measurement of actual flow through the turbine, at the same time as overall ACH measurement.

Historical Dataset of Target Range School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),American Indian Student Percentage Comparison Over Years (1993-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2012-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (1999-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Overall School Rank Trends Over Years (2010-2022)

Historical Dataset of Mesabi Range College is provided by CommunityCollegeReview and contain statistics on metrics:Total Faculty Trends Over Years (2008-2023),Total Enrollment Trends Over Years (2007-2023),Student-Staff Ratio Trends Over Years (2008-2023),Full-Time Students Enrollment Trends Over Years (2007-2023),Part-Time Students Enrollment Trends Over Years (2007-2023),Full-Time Undergraduate Students Enrollment Trends Over Years (2007-2023),American Indian Student Percentage Comparison Over Years (2007-2023),Asian Student Percentage Comparison Over Years (2008-2013),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2008-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Non Resident Student Percentage Comparison Over Years (2008-2018),Diversity Score Comparison Over Years (2008-2023),Tuition Fees For an In-State Student Trends Over Years (2010-2023),Tuition Fees For an Out-Of-State Student Trends Over Years (2010-2023),Financial Aid Student Percentage Comparison Over the Years (2009-2023),Completion Rates For First-Time of Full-Time Students Comparison Over Years (2008-2023),Average Graduate Earnings (10 Years) Trends Over Years (2008-2013),Median Debt For Students Who Have Completed A Certificate Or Degree Trends Over Years (2008-2023),Median Debt For Students Who Have Not Completed A Certificate Or Degree Trends Over Years (2008-2023)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Feature Shapefile (ADDRFEAT.dbf) contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. The ADDRFEAT shapefile contains a record for each address range to street name combination. Address range associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that the ADDRFEAT shapefile includes all unsuppressed address ranges compared to the All Lines Shapefile (EDGES.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefile contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

Study information

The sample included in this dataset represents five children who participated in a number line intervention study. Originally six children were included in the study, but one of them fulfilled the criterion for exclusion after missing several consecutive sessions. Thus, their data is not included in the dataset.

All participants were currently attending Year 1 of primary school at an independent school in New South Wales, Australia. For children to be able to eligible to participate they had to present with low mathematics achievement by performing at or below the 25th percentile in the Maths Problem Solving and/or Numerical Operations subtests from the Wechsler Individual Achievement Test III (WIAT III A & NZ, Wechsler, 2016). Participants were excluded from participating if, as reported by their parents, they have any other diagnosed disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, intellectual disability, developmental language disorder, cerebral palsy or uncorrected sensory disorders.

The study followed a multiple baseline case series design, with a baseline phase, a treatment phase, and a post-treatment phase. The baseline phase varied between two and three measurement points, the treatment phase varied between four and seven measurement points, and all participants had 1 post-treatment measurement point.

The number of measurement points were distributed across participants as follows:

Participant 1 – 3 baseline, 6 treatment, 1 post-treatment

Participant 3 – 2 baseline, 7 treatment, 1 post-treatment

Participant 5 – 2 baseline, 5 treatment, 1 post-treatment

Participant 6 – 3 baseline, 4 treatment, 1 post-treatment

Participant 7 – 2 baseline, 5 treatment, 1 post-treatment

In each session across all three phases children were assessed in their performance on a number line estimation task, a single-digit computation task, a multi-digit computation task, a dot comparison task and a number comparison task. Furthermore, during the treatment phase, all children completed the intervention task after these assessments. The order of the assessment tasks varied randomly between sessions.

Measures

Number Line Estimation. Children completed a computerised bounded number line task (0-100). The number line is presented in the middle of the screen, and the target number is presented above the start point of the number line to avoid signalling the midpoint (Dackermann et al., 2018). Target numbers included two non-overlapping sets (trained and untrained) of 30 items each. Untrained items were assessed on all phases of the study. Trained items were assessed independent of the intervention during baseline and post-treatment phases, and performance on the intervention is used to index performance on the trained set during the treatment phase. Within each set, numbers were equally distributed throughout the number range, with three items within each ten (0-10, 11-20, 21-30, etc.). Target numbers were presented in random order. Participants did not receive performance-based feedback. Accuracy is indexed by percent absolute error (PAE) [(number estimated - target number)/ scale of number line] x100.

Single-Digit Computation. The task included ten additions with single-digit addends (1-9) and single-digit results (2-9). The order was counterbalanced so that half of the additions present the lowest addend first (e.g., 3 + 5) and half of the additions present the highest addend first (e.g., 6 + 3). This task also included ten subtractions with single-digit minuends (3-9), subtrahends (1-6) and differences (1-6). The items were presented horizontally on the screen accompanied by a sound and participants were required to give a verbal response. Participants did not receive performance-based feedback. Performance on this task was indexed by item-based accuracy.

Multi-digit computational estimation. The task included eight additions and eight subtractions presented with double-digit numbers and three response options. None of the response options represent the correct result. Participants were asked to select the option that was closest to the correct result. In half of the items the calculation involved two double-digit numbers, and in the other half one double and one single digit number. The distance between the correct response option and the exact result of the calculation was two for half of the trials and three for the other half. The calculation was presented vertically on the screen with the three options shown below. The calculations remained on the screen until participants responded by clicking on one of the options on the screen. Participants did not receive performance-based feedback. Performance on this task is measured by item-based accuracy.

Dot Comparison and Number Comparison. Both tasks included the same 20 items, which were presented twice, counterbalancing left and right presentation. Magnitudes to be compared were between 5 and 99, with four items for each of the following ratios: .91, .83, .77, .71, .67. Both quantities were presented horizontally side by side, and participants were instructed to press one of two keys (F or J), as quickly as possible, to indicate the largest one. Items were presented in random order and participants did not receive performance-based feedback. In the non-symbolic comparison task (dot comparison) the two sets of dots remained on the screen for a maximum of two seconds (to prevent counting). Overall area and convex hull for both sets of dots is kept constant following Guillaume et al. (2020). In the symbolic comparison task (Arabic numbers), the numbers remained on the screen until a response was given. Performance on both tasks was indexed by accuracy.

The Number Line Intervention

During the intervention sessions, participants estimated the position of 30 Arabic numbers in a 0-100 bounded number line. As a form of feedback, within each item, the participants’ estimate remained visible, and the correct position of the target number appeared on the number line. When the estimate’s PAE was lower than 2.5, a message appeared on the screen that read “Excellent job”, when PAE was between 2.5 and 5 the message read “Well done, so close! and when PAE was higher than 5 the message read “Good try!” Numbers were presented in random order.

Variables in the dataset

Age = age in ‘years, months’ at the start of the study

Sex = female/male/non-binary or third gender/prefer not to say (as reported by parents)

Math_Problem_Solving_raw = Raw score on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Math Problem Solving subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Num_Ops_Raw = Raw score on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

Math_Problem_Solving_Percentile = Percentile equivalent on the Numerical Operations subtest from the WIAT III (WIAT III A & NZ, Wechsler, 2016).

The remaining variables refer to participants’ performance on the study tasks. Each variable name is composed by three sections. The first one refers to the phase and session. For example, Base1 refers to the first measurement point of the baseline phase, Treat1 to the first measurement point on the treatment phase, and post1 to the first measurement point on the post-treatment phase.

The second part of the variable name refers to the task, as follows:

DC = dot comparison

SDC = single-digit computation

NLE_UT = number line estimation (untrained set)

NLE_T= number line estimation (trained set)

CE = multidigit computational estimation

NC = number comparison

The final part of the variable name refers to the type of measure being used (i.e., acc = total correct responses and pae = percent absolute error).

Thus, variable Base2_NC_acc corresponds to accuracy on the number comparison task during the second measurement point of the baseline phase and Treat3_NLE_UT_pae refers to the percent absolute error on the untrained set of the number line task during the third session of the Treatment phase.

The dataset consists of 181 HDR images. Each image includes: * a RAW exposure stack (in raw.zip) * an HDR image (in reference.zip) * simulated camera images at two different exposures (in input.zip) * Results of 6 single-image HDR reconstruction methods: Endo et al. 2017, Eilertsen et al. 2017, Marnerides et al. 2018, Lee et al. 2018, Liu et al. 2020, and Santos et al. 2020 (in reconstructions.zip)

See README.md for more details.

Project page: https://www.cl.cam.ac.uk/research/rainbow/projects/sihdr_benchmark/

Detailed comparison of methodology of home range studies of black rhinoceros Diceros bicornis minor in Hluhluwe-iMfolozi Park, South Africa.

This statistic depicts the estimated range of savings in top medical tourism destinations, compared to the U.S. as of 2016. A patient from the U.S. could save up to ** percent of costs if he chose to go to India for a medical procedure.

About this dataset

Context

The dataset tabulates the Grass Range population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Grass Range across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Grass Range was 115, a 0.88% increase year-by-year from 2022. Previously, in 2022, Grass Range population was 114, a decline of 0% compared to a population of 114 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Grass Range decreased by 35. In this period, the peak population was 150 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Grass Range is shown in this column.
• Year on Year Change: This column displays the change in Grass Range population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Year. You can refer the same here

The National Water Model is a recently developed hydrologic model that simulates observed and forecast streamflow over the entire continental United States. The model uses an advanced system of inputs to produce three ranges of water models: short-range (18-hour forecast), medium-range (10 day forecast), and long-range (30 day forecast). In this study, short-range streamflow data from the NWM was compared to independent stream gage data gathered from 32 points within the city of Austin. A visual representation of the statistical comparison was prepared in ArcGIS pro.

According to a 2024 survey, about ** percent of Spanish Gen Z and Milliennials shared they spend between 50 and 100 euros per fashion item. Overall, this was the most common price range for all listed European countries.

Historical Dataset of Front Range Community College is provided by CommunityCollegeReview and contain statistics on metrics:Total Faculty Trends Over Years (2008-2023),Total Enrollment Trends Over Years (2007-2024),Student-Staff Ratio Trends Over Years (2008-2023),Full-Time Students Enrollment Trends Over Years (2007-2024),Part-Time Students Enrollment Trends Over Years (2007-2024),Full-Time Undergraduate Students Enrollment Trends Over Years (2007-2024),American Indian Student Percentage Comparison Over Years (2007-2023),Asian Student Percentage Comparison Over Years (2008-2023),Hawaiian Student Percentage Comparison Over Years (2018-2024),Hispanic Student Percentage Comparison Over Years (2008-2022),Black Student Percentage Comparison Over Years (2008-2023),White Student Percentage Comparison Over Years (2008-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Non Resident Student Percentage Comparison Over Years (2009-2021),Diversity Score Comparison Over Years (2008-2023),Tuition Fees For an In-State Student Trends Over Years (2008-2024),Tuition Fees For an Out-Of-State Student Trends Over Years (2008-2024),Financial Aid Student Percentage Comparison Over the Years (2007-2023),Completion Rates For First-Time of Full-Time Students Comparison Over Years (2007-2019),Completion Rates For First-Time of Full-Time Students At Four Year Institutions Comparison Over Years (2021-2024),Average Graduate Earnings (10 Years) Trends Over Years (2008-2013),Median Debt For Students Who Have Completed A Certificate Or Degree Trends Over Years (2008-2023),Median Debt For Students Who Have Not Completed A Certificate Or Degree Trends Over Years (2008-2023)

Historical Dataset of Target Range 7-8 is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1993-2023),Total Classroom Teachers Trends Over Years (1993-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1993-2023),American Indian Student Percentage Comparison Over Years (1993-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1995-2023),Black Student Percentage Comparison Over Years (2004-2023),White Student Percentage Comparison Over Years (1992-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2001-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Overall School Rank Trends Over Years (2010-2022)

Dataset

This dataset was created by HILLOL DAS

Contents