About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Cable town. The dataset can be utilized to gain insights into gender-based income distribution within the Cable town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Cable town, among individuals aged 15 years and older with income, there were 293 men and 367 women in the workforce. Among them, 141 men were engaged in full-time, year-round employment, while 117 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 34.04% fell within the income range of under $24,999, while 11.97% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.64% of men in full-time roles earned incomes exceeding $100,000, while 9.40% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Cable town median household income by race. You can refer the same here

Three-wave panel survey on medium-term change of use of television, leisure habits, family interaction and political attitude on introduction of cable television. Topics: In all three waves identical questions were posed: general attitude to cable television (scale); possession of a building or residential connection to cable television; applicant for the cable connection; characterization of television sets in household according to acquisition year and features; cable channel capability of the TV devices; television stations received; desire to re-equip or acquire a device with cable capability; frequency of watching cable television with others; particularly preferred cable channels and TV selections; technical equipment in household; extent and type of video recorder use; extent of borrowing video cassettes from friends as well as renting in the video store; sacrificing leisure activities and social contacts for the benefit of watching favorite broadcasts; frequency of communication about the topic cable television; interest in politics; sources of information about the day's events in Berlin; manner of opinion making after receipt of information through the media; personal opinion leadership or personal opinion allegiance and relevant topics here; TV use on working days and on weekends; attitude to television (scale); frequency of use of news broadcasts and regional reports; most important political topics in Berlin; self-classification on a left-right continuum; party preference and party identification; attitude to social and political questions (scale); importance of moral concepts and goals in life (scale); requirements and most important characteristics of an ideal job (scale); work satisfaction; perceived stress through work; work orientation and attitude to work (scale); preference for spending leisure time at home or outside; attitude to selected leisure activities; meals together with the family on working days and on Sundays; characterization of family solidarity and activities together (scale); division of tasks with jobs in household; detailed information on conduct watching television (secondary activities and communication partners); frequency of watching television and looking at video films with friends and relatives; frequency of selected leisure activities; membership in selected clubs as well as organizations and activities developed there oneself; anomy and judgement on political conduct of young people (scale); political participation and participation in conventional and unconventional political activities; frequency of contact with neighbors and relatives; self-assessment of size of circle of friends; satisfaction with social contacts and distance to important interaction partners; postmaterialism; attitude to questions on foreigners, the role of women in family and occupation (scale); attitude to technology; length of personal unemployment; residential status; religiousness; sick days in the last year. Dreiwellige Panel-Erhebung zur mittelfristigen Veränderung der Fernsehnutzung, der Freizeitgewohnheiten, der Familieninteraktion und der politischen Einstellung bei Einführung des Kabelfernsehens. Themen: In allen drei Wellen wurden identische Fragen gestellt: Allgemeine Einstellung zum Kabelfernsehen (Skala); Besitz eines Haus- bzw. Wohnungsanschlusses zum Kabelfernsehen; Antragsteller für den Kabelanschluß; Charakterisierung der im Haushalt befindlichen Fernsehgeräte nach Anschaffungsjahr und Ausstattungsmerkmalen; Kabelkanalfähigkeit der TV-Geräte; empfangene Fernsehsender; Umrüstungswunsch oder Anschaffungswunsch für ein kabelfähiges Gerät; Häufigkeit des Kabelfernsehens mit anderen; besonders präferierte Kabelkanäle und TV-Angebote; Haushaltsausstattung mit technischen Geräten; Umfang und Art der Videorecordernutzung; Umfang des Videokassettenleihens bei Freunden sowie in der Videothek; Verzicht auf Freizeitaktivitäten und Sozialkontakten zugunsten des Sehens von Lieblingssendungen; Häufigkeit der Kommunikation über das Thema Kabelfernsehen; Politikinteresse; Informationsquellen über das Tagesgeschehen in Berlin; Art der Meinungsbildung nach Erhalt von Informationen durch die Medien; eigene Meinungsführerschaft oder eigene Meinungsgefolgschaft und dabei relevante Themen; TV-Nutzung an Werktagen und an Wochenenden; Einstellung zum Fernsehen (Skala); Nutzungshäufigkeit von Nachrichtensendungen und Regionalberichten; wichtigste politische Themen in Berlin; Selbsteinstufung auf einem Links-Rechts-Kontinuum; Parteipräferenz und Parteiidentifikation; Einstellung zu gesellschaftlichen und politischen Fragen (Skala); Wichtigkeit von Wertvorstellungen und Lebenszielen (Skala); Anforderungsprofil und wichtigste Charakteristika eines idealen Arbeitsplatzes (Skala); Arbeitszufriedenheit; empfundene Belastung durch die Arbeit; Arbeitsorientierung und Einstellung zur Arbeit (Skala); Präferenz für ein Verbringen der Freizeit zu Hause oder außerhalb; Einstellung zu ausgewählten Freizeitaktivitäten; gemeinsame Mahlzeiten mit der Familie an Werktagen und an Sonntagen; Charakterisierung des familiären Zusammenhalts und gemeinsamer Aktivitäten (Skala); Aufgabenteilung bei Arbeiten im Haushalt; detaillierte Angaben über das Verhalten beim Fernsehen (sekundäre Tätigkeiten und Kommunikationspartner); Häufigkeit des Fernsehkonsums und des Anschauens von Videofilmen mit Freunden und Verwandten; Häufigkeit ausgewählter Freizeitaktivitäten; Mitgliedschaft in ausgewählten Vereinen sowie Organisationen und dabei selbst entfaltete Aktivitäten; Anomie und Beurteilung des politischen Verhaltens von Jugendlichen (Skala); politische Partizipation und Beteiligung an konventionellen und unkonventionellen politischen Aktivitäten; Kontakthäufigkeit mit Nachbarn und mit Verwandten; Selbsteinschätzung der Größe des Bekanntenkreises; Zufriedenheit mit den Sozialkontakten und Entfernung zu wichtigen Interaktionspartnern; Postmaterialismus; Einstellung zu Ausländerfragen, zur Rolle der Frau in Familie und Beruf (Skala); Einstellung zur Technik; Dauer eigener Arbeitslosigkeit; Wohnstatus; Religiosität; Krankentage im letzten Jahr. Demographie: Alter; Geschlecht; Familienstand; Schulbildung; Berufsausbildung; Berufstätigkeit; Haushaltseinkommen; Haushaltsgröße. Quasi-experiment with respondents with cable TV as experimental group and respondents without cable TV as control group. For both groups independent representative samples were drawn. Quasi-Experiment mit verkabelten Befragten als Experimentalgruppe und nicht verkabelten Befragten als Kontrollgruppe. Für beide Gruppen wurden jeweils unabhängige repräsentative Stichproben gezogen.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cable town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cable town, the median income for all workers aged 15 years and older, regardless of work hours, was $35,982 for males and $28,920 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Cable town. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Cable town.

- Full-time workers, aged 15 years and older: In Cable town, among full-time, year-round workers aged 15 years and older, males earned a median income of $58,438, while females earned $50,268, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the town of Cable town.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Cable town.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Cable town median household income by race. You can refer the same here

This data includes information on the percentage of residential end-user premises with access to high-speed services - specifically, cable modem availability where cable systems offer cable TV service. The percentages are as on June 30, 2006. States not included in the dataset did not report to maintain firm confidentiality. Original Source: Industry Analysis and Technology Division, Wireline Competition Bureau, High-Speed Services for Internet Access: Status as of June 30, 2006 (January 2007).

Here are a few use cases for this project:

1. UX/UI Design Improvement: Designers can use this model to assess and get insights about current well-performing UI designs. It can be used to create a database with successful design patterns and their contexts, informing improvements, updates or changes in future designs.

2. Competitive Analysis: Businesses can use this model to analyze competitor's app designs. By pinpointing UI components, companies can get a better understanding of the layout and design strategies implemented by their competitors.

3. Automatic UI Testing: Developers can utilize this software to automatically identify and test different UI components of the system during the software development process. It can greatly expedite QA testing, ensuring the UI components function and display as expected.

4. Educational Tools: This vision model can aid in creating educational content for design and development courses. By identifying and showcasing different UI components within real-world, functioning designs, students can have a clearer understanding of how these components function and fit together.

5. Accessibility Testing: This model can also be used to test how accessible a website or application is to people with disabilities. By identifying different UI components, it can assess whether there is enough contrast between elements, sufficient font size and appropriate spacing, contributing to the overall accessibility of the platform.

Here are a few use cases for this project:

1. Traffic Management Systems: The "signshappy" model can be used in smart traffic management systems to recognize and interpret different traffic sign classes. It could help in monitoring compliance with traffic rules and issue violations accordingly.

2. Autonomous Vehicles: Self-driving cars could utilize the model to understand traffic signs around them, aiding in making real-time navigation decisions and ensuring the safety of individuals in and around the vehicle.

3. Geo-Mapping Services: Providers like Google Maps could use the model to enhance their street view feature by recognizing and overlaying traffic signs onto the map. This could help users understand driving conditions and regulations in specific areas.

4. Training Applications: The model can be used in driver training applications to teach learners about various traffic signs. It could be used in a simulation where the learner needs to make decisions based on signs identified by the model.

5. Road Infrastructure Planning: Urban planners and road designers can leverage this model to carry out audits of existing traffic signs in a city or region. This can help in identifying areas for improvements or any missing crucial signs.

LGA based data for Dwelling Internet Connection by Indigenous Status of Household, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). A household with Aboriginal and/or Torres Strait Islander person(s) is any household that had at least one person of any age as a resident at the time of the Census who identified as being of Aboriginal and/or Torres Strait Islander origin. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

SA4 based data for Dwelling Internet Connection by Indigenous Status of Household, in Aboriginal and Torres Strait Islander People Profile (ATSIP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). A household with Aboriginal and/or Torres Strait Islander person(s) is any household that had at least one person of any age as a resident at the time of the Census who identified as being of Aboriginal and/or Torres Strait Islander origin. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

BackgroundPrevious studies have found a correlation between varicose veins (VVs) and cognitive decline, and individuals with VVs have a higher prevalence of Alzheimer’s disease (AD). However, the associations between VVs and the core pathologies of AD have not yet been investigated. The research was designed to analyze the relationships between VVs and cerebrospinal fluid (CSF) biomarkers of AD pathologies.MethodsWe included 1,298 participants from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database without dementia. Multiple linear regression (MLR) model was applied to assess the relationships between the VVs and CSF AD biomarkers. Then, we conducted subgroup analyses according to age, gender, education levels and apolipoprotein E genotype ε4 (APOE-ε4) carrier status. Additionally, mediation effects were assessed using causal mediation analyses with 10,000 bootstrapped iterations.ResultsIn total subjects, VVs had negative correlations with CSF Aβ42 (β = −0.157, p = 0.038) and CSF Aβ42/Aβ40 ratio (β = −0.272, p < 0.001), as well as positive correlations with CSF Aβ40 (β = 0.170, p = 0.024), CSF p-tau (β = 0.192, p = 0.008), CSF t-tau/Aβ42 ratio (β = 0.190, p = 0.011), and CSF p-tau/Aβ42 ratio (β = 0.248, p = 0.001), after adjusting for age, sex, education levels and APOE-ε4 carrier status. Subgroup analyses demonstrated that the relations between VVs and CSF AD biomarkers were more significant in female, mid-life adults (40–65 years), less-educated individuals and APOE-ε4 non-carriers. Moreover, CSF Aβ42/Aβ40 ratio might be a partial mediator of the association between VVs and p-tau pathology.ConclusionOur study found correlations between VVs and CSF AD biomarkers, suggesting that VVs may be a potential risk factor for the development of AD.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Cable One (CABO) Stock: Broadband Boom or Bandwidth Bust?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Cable transport lines, such as aerial tramway for people or materials, chair lift, ski lift, etc. The line is located on the cable or on the axis of the bundle of cables (if more than one). The line is repeated for each direction of travel. The supports that are of importance and stability, which can be represented to size, are returned with the entity "Trellis support" of the "Manufactured" group.