The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Background Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. Results The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. Conclusions Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

The size of the Clinical Risk Grouping Solutions market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

Grouping analysis of FIQ document heterogeneity.

Social and organizational innovations are one of the most effective ways to gain social collaboration for effective, rapid, and coordinated interventions. An analysis of the relationship among organizational performance (OP), social innovations (SI) and organizational innovation (OI) in social organizations (SOs) is little discussed in the literature and much less with main component analysis. This paper is an effort to provide some empirical evidences about social and organizational innovations that social organizations in China have implemented to address the social issues of the society. A survey of Chinese SO’s is conducted during beginning two months of 2022 in provinces of Jiangsu, Guangdong and Zhejiang to attain the statistics and assessing the insights of the executives of the SOs participating in this study with respect to organizational performance, social and organizational innovations. The technique used to select the sample is a non-probabilistic sampling and multiple linear regression model is applied to determine the partial impact of organizational innovations and social innovations on the organizational performance. The grouping of the variables is carried out through main components analysis. The empirical findings of the study highlight that Chinese SOs are innovative because they adopt management strategies to address the social issues associated with their institutional mission. There are four groups of derived components from organizational and social innovations based on the empirical evidence: SO’s innovative activities to modify the environment; inside innovative measures to enhance SO’s performance; innovative activities of SO’s to enhance their relationships with outside actors; innovative measures to improve the management of SOs related to their mission and institutional projects. The findings of this study offer an efficient solution to government and policy makers for involving SOs in terms of planning of social development in China. The social and organizational innovations are very necessary to overcome the social issues so government should encourage the establishment and sustainability of social organizations.

Yearly citation counts for the publication titled "Classification of Depressed Patients: A Cluster-Analysis-Derived Grouping".

ABSTRACT The objective of this study was to evaluate the economic viability, through the joint analysis of financial indicators expressed by animal, of the Aberdeen Angus steers feedlot finished fed diets with different levels of concentrate (CL): 25, 40, 55 or 70% (on dry matter basis). Consecutive historical quotes of years 2003 to 2014 were considered. Univariate analysis was characterized by a completely randomized design with four treatments and four replications, proceeding regression analysis. Multivariate analysis consisted of grouping (cluster). The univariate analysis showed similarity to the financial indicators with averages to gross margin of R$ 173.21; net margin of R$ 163.73; income of R$ 110.61; net present value of R$ 93.31; benefit: cost ratio of 1.048; additional return on investment of 1.17% per month; internal rate of return of 2.04% per month and discounted payback of 1.36 months. By cluster analysis, 55% CL presented greater discrepancy in relation to other levels, while 40 and 70% were the nearest. The analysis of the financial indicators indicated feasibility of feedlot steers, regardless of the concentrate level.

List of 56 characters used for cluster analysis and their significance levels from univariate test statistics using CANDISC procedure (SAS software).

Boy Bands and Members Analysis

Investigating Popular Music Groups and their Impact on Pop Culture

By Priyanka Dobhal [source]

About this dataset

This dataset provides an in-depth analysis of popular boy bands from the past and present. You can explore the detailed information about various boy bands, including their names, members, and years active. With this dataset you can trace the evolution and legacy of each band by studying their timeline over time. You can also get an insight into which bands are still active today and which ones have disbanded or changed members. All in all, this dataset will help you understand why these boy bands had such a big impact on pop culture!

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset, Boy Bands and their Members: An Analysis of provides a comprehensive analysis of data on various boy bands, their members, and the years they were active. This dataset can be used to analyze popular trends in music as well as get an overview of each boy band.

To get started with this dataset, you should first understand the columns that are included in this dataset. They are: S.No., Band, Years Active, Members, Active?, and Timeline. From these columns could gather information about the band’s timeline (when it was most active) its current activity status (if it is active or not), and the specific names of its members from which you could further explore their careers afterwards.

Once you have an understanding of what is provided in this data set - namely

• The serial number associated with a boy band;
• The name of said boy band;

• Years during which they were active ; 4) A list/breakdown of all its members; 5 ) It's current active status ('Active' or 'Inactive', accordingly); 6 ) And lastly- a sequential timeline depicting when each member joined said Band - you can begin to effectively analyze within your commands/queries each factors associated with any given Boy Band. Such field work may yield various insights derived from the actual records found within this database (examples being added depth to one's musical knowledge- more insight into musical diversity when analyzying different boys vs girl bands). Ultimately we hope that such exploration encourages well rounded investigations for readers who enjoy delving into aggregate data!

  Happy Exploring & Enjoying!

Research Ideas

• Analyzing the lifespans of boy bands and use that data to inform potential new boy band’s expectations and trajectories.
• Examining successful partnerships between members in order to encourage collaboration between similar artists.
• Creating an interactive website that showcases various themes related to specific boy band, such as sound and visual style, milestones achieved, cultural context during their reign etc

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: Boy Band.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------| | S.No. | Unique identifier for each band representing an integer value. (Integer) | | Band | Name of the boy band. (String) | | Years Active | Years in which the boy band was active. (String) |

File: Boy Band Members.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------------------------| | Band | Name of the boy band. (String) | | Members | Names of members of the boy band. (String) | | Active? | Whether or not the group is still together. (Boolean) | | Timeline | The years when each member entered/left their time with their respective groups. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

About this dataset

Context

The dataset tabulates the population of Hazel Park by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Hazel Park. The dataset can be utilized to understand the population distribution of Hazel Park by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Hazel Park. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Hazel Park.

Key observations

Largest age group (population): Male # 30-34 years (861) | Female # 25-29 years (812). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Hazel Park population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hazel Park is shown in the following column.
• Population (Female): The female population in the Hazel Park is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Hazel Park for each age group.

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 Hazel Park Population by Gender. You can refer the same here

In 2017, the County Department of Economic Development, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for Allegheny County. A similar MVA was completed with the Pittsburgh Urban Redevelopment Authority in 2016. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. The 2016 Allegheny County MVA does not include the City of Pittsburgh, which was characterized at the same time in the fourth update of the City of Pittsburgh’s MVA. All calculations herein therefore do not include the City of Pittsburgh. While the methodology between the City and County MVA's are very similar, the classification of communities will differ, and so the data between the two should not be used interchangeably. Allegheny County's MVA utilized data that helps to define the local real estate market. Most data used covers the 2013-2016 period, and data used in the analysis includes: •Residential Real Estate Sales; • Mortgage Foreclosures; • Residential Vacancy; • Parcel Year Built; • Parcel Condition; • Owner Occupancy; and • Subsidized Housing Units. The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. During the research process, staff from the County and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market. Please refer to the report (included here as a pdf) for more information about the data, methodology, and findings.

The global market for Cluster Analysis Software is experiencing robust growth, driven by the increasing adoption of big data analytics and the need for advanced data interpretation across diverse sectors. While precise market sizing data is unavailable, considering the growth observed in related fields like data analytics and AI, a reasonable estimate for the 2025 market size could be placed between $2.5 billion and $3 billion. This estimate assumes a moderate growth trajectory reflecting the maturation of the cluster analysis market and the ongoing integration of these tools into broader business intelligence platforms. Assuming a Compound Annual Growth Rate (CAGR) of 15% for the forecast period (2025-2033), the market is projected to reach a substantial size within the next decade. This growth is fueled by several key drivers, including the expanding availability of large datasets, the growing demand for data-driven decision-making across industries like BFSI (Banking, Financial Services, and Insurance), government, and commercial sectors, and the continuous development of more sophisticated algorithms and user-friendly interfaces for cluster analysis software. The cloud-based segment is expected to dominate, given its scalability and accessibility benefits, although web-based applications will continue to hold a significant market share. Geographic growth will be diverse, with North America and Europe maintaining strong positions due to advanced analytics adoption, but significant expansion is also expected in the Asia-Pacific region as technological advancement and data infrastructure improve. However, challenges like data privacy concerns, the need for skilled professionals, and the high cost of advanced software solutions could act as market restraints in certain regions. The competitive landscape is marked by a mix of established players such as IBM, Microsoft, and TIBCO Software, along with a growing number of specialized vendors and emerging technology companies. The market is characterized by ongoing innovation in areas like algorithm development, enhanced visualization capabilities, and the integration of cluster analysis with other advanced analytics tools. This continuous innovation will be a key driver in sustaining the market's high CAGR and ensuring its continued growth in the coming years. Increased focus on providing tailored solutions for specific industry verticals will likely be a strategic advantage for vendors seeking a competitive edge. The market's future hinges on its ability to effectively address the challenges of data complexity, security, and user-friendliness while continuing to deliver accurate and actionable insights.

In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes: * Residential Real Estate Sales * Mortgage Foreclosures * Residential Vacancy * Parcel Year Built * Parcel Condition * Building Violations * Owner Occupancy * Subsidized Housing Units The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. Please refer to the presentation and executive summary for more information about the data, methodology, and findings.

About this dataset

Context

The dataset tabulates the population of Lac La Belle by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lac La Belle. The dataset can be utilized to understand the population distribution of Lac La Belle by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lac La Belle. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Lac La Belle.

Key observations

Largest age group (population): Male # 15-19 years (20) | Female # 60-64 years (20). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Lac La Belle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lac La Belle is shown in the following column.
• Population (Female): The female population in the Lac La Belle is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Lac La Belle for each age group.

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 Lac La Belle Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Howards Grove by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Howards Grove. The dataset can be utilized to understand the population distribution of Howards Grove by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Howards Grove. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Howards Grove.

Key observations

Largest age group (population): Male # 50-54 years (229) | Female # 55-59 years (207). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Howards Grove population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Howards Grove is shown in the following column.
• Population (Female): The female population in the Howards Grove is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Howards Grove for each age group.

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 Howards Grove Population by Gender. You can refer the same here

Dataset

This dataset was created by Data Science

Released under CC0: Public Domain

Contents

About this dataset

Context

The dataset tabulates the population of Grosse Pointe Park by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Grosse Pointe Park. The dataset can be utilized to understand the population distribution of Grosse Pointe Park by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Grosse Pointe Park. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Grosse Pointe Park.

Key observations

Largest age group (population): Male # 55-59 years (423) | Female # 35-39 years (644). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Grosse Pointe Park population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Grosse Pointe Park is shown in the following column.
• Population (Female): The female population in the Grosse Pointe Park is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Grosse Pointe Park for each age group.

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 Grosse Pointe Park Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Tyronza by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Tyronza. The dataset can be utilized to understand the population distribution of Tyronza by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Tyronza. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Tyronza.

Key observations

Largest age group (population): Male # 45-49 years (44) | Female # 5-9 years (34). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Tyronza population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Tyronza is shown in the following column.
• Population (Female): The female population in the Tyronza is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Tyronza for each age group.

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 Tyronza Population by Gender. You can refer the same here

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.