Dataset

This dataset was created by Dilara Özcerit

Contents

This statistic represents the average height of men in the top 20 countries worldwide as of 2016. On average, men are ***** centimeters tall in Bosnia & Herzegovina.

In the shown time-period the mean height of men and women has generally increased in England. According to the survey, the average height of males rose slightly during the period in consideration, from 174.4 centimeters in 1998 to 176.2 centimeters (approximately 5'9") in 2022. In comparison, the mean height of women was 162.3 centimeters (5'4") in 2022, up from 161 in 1998. Reasons for height increasing While a large part of an adult’s final height is based on genetics, the environment in which a person grows up is also important. Improvements in nutrition, healthcare, and hygiene have seen the average heights increase over the last century, particularly in developed countries. Average height is usually seen as a barometer for the overall health of the population of a country, as the most developed are usually among the ‘tallest’ countries. Average waist circumference also increasing The prevalence of obesity among adults in England has generally been trending upward since 2000. In that year, 21 percent of men and women in England were classified as obese. By 2021, however, this share was 26 percent among women and 25 percent among men. Every adult age group in England had an average BMI which was classified as overweight, apart from those aged 16 to 24, indicating there is a problem with overweightness in England.

This statistic represents the average height of men and women in selected countries worldwide as of 2008. On average, men are ***** centimeters and women are ***** centimeters tall in Australia.

This dataset provides insights into the average height of boys and girls at different ages (5, 10, 15, and 19) across multiple countries. The data has been sourced from various online sources, including government reports, research studies, and health organizations. It can be useful for analyzing trends in child growth, nutrition, and global health disparities.

Researchers, data analysts, and policymakers can leverage this dataset to compare growth patterns across countries and explore how factors like nutrition, healthcare, and socio-economic conditions impact height development over time."*

Let me know if you need further refinements! 🚀

Dataset

This dataset was created by Jaya Raghavendra

Contents

In 2020, the average height of males aged between 18 and 44 years in China figured at ***** centimeters, up *** centimeters compared to that in 2015. On the other side, obesity and overweight conditions have seen a gradual increase across the country mainly related to an unhealthy diet and a less active urban lifestyle.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fbb4705fcb27ee78ac4b92b942e338839%2Fgraph%201.png?generation=1704660812717986&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F282b6c33eed6a55530b4184ae2a12f8a%2FRplot.png?generation=1704660793224649&alt=media" alt="">

Height is influenced by a combination of genetic, environmental, and nutritional factors on a global scale. Genetic predispositions play a significant role in determining an individual's height, as certain populations may have inherent traits that contribute to taller or shorter statures. Additionally, environmental factors such as access to healthcare, sanitation, and overall socioeconomic conditions can impact growth patterns.

Nutrition is a crucial determinant of height, especially during the formative years of childhood and adolescence. Insufficient or imbalanced nutrition can stunt growth, leading to shorter stature. Conversely, adequate nutrition supports proper development and contributes to reaching one's genetic height potential.

On a global scale, variations in average height can be observed across different regions and populations. These differences are reflective of the complex interplay between genetics, environment, and nutrition. Understanding these global height factors is essential for addressing health disparities and implementing effective strategies to promote optimal growth and well-being worldwide.

According to survey results by Ipsos in Serbia, the height *** to *** feet is ranked as the perfect female height by the majority of respondents (** percent). The ideal male height for almost half of respondents was in the range from * foot ** inches to * foot one. The shortest height rated for males overall was * foot * inches.

These are the results obtained by conducting the experiment "Average Height of 19-year-old Males and Females and GDP per Capita in 2019 for 164 Countries".

The CSV file contains the raw data produced by processing, filtering and merging the input datasets. There are two rows for each of the 164 countries. In both rows, the country name, country code and GDP per capita are given. However, one row contains the average height of 19-year-old males (indicated by the value 'Boys' in the 'Sex' column) whereas the other displays the average height of 19-year-old females (indicated by the value 'Girls').

Furthermore, there are two PNG files which display the regression plots for the average height of 19-year-old males and females, respectively. Note that the x-scale (for the GDP per capita) is logarithmic.

This table contains 136080 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (126 items: Canada; Central Regional Integrated Health Authority; Newfoundland and Labrador; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...), Age group (5 items: Total; 18 years and over;18 to 34 years ...), Sex (3 items: Both sexes; Males; Females ...), Body mass index (BMI), self-reported (9 items: Total population for the variable body mass index; self-reported; Normal weight; body mass index; self-reported 18.5 to 24.9;Overweight; body mass index; self-reported 25.0 to 29.9;Underweight; body mass index; self-reported under 18.5 ...), Characteristics (8 items: Number of persons; Low 95% confidence interval; number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons ...).

All data are means ± SD. No significant differences between males and females for height, weight, and age.Average height, weight, and age.

Context

Globally, when we talk about the features to predict the sex of each person, it is undeniable that Height & Weight are typical features for that. This dataset is purposely for the beginner who recently has done studying Machine Algorithm and may want to apply their algorithm on a simple dataset.

There are just 2 features (Height, Weight) & 1 label (Sex)

• Height (cm)
• Weight (kg)
• Sex (male | female)

Content

There are 2 datasets; training set and test set

1. Training set consists of 3000 rows & 3 columns
2. Test set consists of 205 rows & 3 columns

Acknowledgements

Thank you for the great research from https://www.researchgate.net/figure/The-mean-and-standard-deviation-of-height-weight-and-age-for-both-male-and-female_tbl1_257769120,

so we can generate sample data through their mean & std.

Inspiration

• Predict the sex using 2 features; height and weight in the test set.
• then evaluate the prediction accuracy using the ROC AUC score

(Evaluation: ROC AUC score)

The average height of 17-year-old boys in Norway was around *** centimeters (cm). It increased slightly, from ***** in 2011 to ***** in 2019. The average height of 17-year-old girls remained stable at ***** cm.

File List HumanHeightWikipediaJan192011.csv HumanHeightDataFile.csv HeightHumanDataRefsFeb32011.doc Description HumanHeightWikipediaJan192011.csv contains height data originally downloaded from Wikipedia “Human Height” page, Jan. 19, 2011. HumanHeightDataFile.csv contains average per country male and female human height data with country name and latitude used for Figure 4. HeightHumanDataRefsFeb32011.doc contains publications and other sources for average per country human height data, based on sources listed on Wikipedia, supplemented with addition information and sources.

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 Height of Land township. 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 Height of Land township, the median income for all workers aged 15 years and older, regardless of work hours, was $51,750 for males and $24,167 for females.

These income figures highlight a substantial gender-based income gap in Height of Land township. Women, regardless of work hours, earn 47 cents for each dollar earned by men. This significant gender pay gap, approximately 53%, underscores concerning gender-based income inequality in the township of Height of Land township.

- Full-time workers, aged 15 years and older: In Height of Land township, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,111, while females earned $46,786, leading to a 34% gender pay gap among full-time workers. This illustrates that women earn 66 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Height of Land township, showcasing a consistent income pattern irrespective of employment status.

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 Height of Land township median household income by race. You can refer the same here

High School Heights Dataset

You will find three datasets containing heights of the high school students.

All heights are in inches.

The data is simulated. The heights are generated from a normal distribution with different sets of mean and standard deviation for boys and girls.

Height Statistics (inches)	Boys	Girls
Mean	67	62
Standard Deviation	2.9	2.2

There are 500 measurements for each gender.

Here are the datasets:

• hs_heights.csv: contains a single column with heights for all boys and girls. There's no way to tell which of the values are for boys and which ones are for girls.

• hs_heights_pair.csv: has two columns. The first column has boy's heights. The second column contains girl's heights.

• hs_heights_flag.csv: has two columns. The first column has the flag is_girl. The second column contains a girl's height if the flag is 1. Otherwise, it contains a boy's height.

To see how I generated this dataset, check this out: https://github.com/ysk125103/datascience101/tree/main/datasets/high_school_heights

Image by Gillian Callison from Pixabay

Diversification in sexual signals is often taken as evidence for the importance of sexual selection in speciation. However, in order for sexual selection to generate reproductive isolation between populations, both signals and mate preferences must diverge together. Furthermore, assortative mating may result from multiple behavioural mechanisms, including female mate preferences, male mate preferences and male-male competition; yet their relative contributions are rarely evaluated. Here, we explored the role of mate preferences and male competitive ability as potential barriers to gene flow between two divergent lineages of the tawny dragon lizard, Ctenophorus decresii, which differ in male throat coloration. We found stronger behavioural barriers to pairings between southern lineage males and northern lineage females than between northern males and southern females, indicating incomplete and asymmetric behavioural isolating barriers. These results were driven by both male and female mate preferences rather than lineage differences in male competitive ability. Intrasexual selection is therefore unlikely to drive the outcome of secondary contact in C. decresii, despite its widely acknowledged importance in lizards. Our results are consistent with the emerging view that although both male and female mate preferences can diverge alongside sexual signals, speciation is rarely driven by divergent sexual selection alone.

Methods Study species and husbandry

We used 90 adult lizards (>65mm snout-vent length; SVL) comprising 21 male and 24 female northern lineage C. decresii from Caroona Creek Conservation Park, South Australia (-33.4114°S, 139.0945°E), and 21 male and 24 female southern lineage C. decresii from private properties around Palmer, South Australia (-34.8223°S, 139.1621°E). Lizards were collected in September in 2015 and 2016, and subsequently kept in captivity at The University of Melbourne, Victoria, Australia, where they were housed individually in 55 × 34 × 38cm (length × width × height) opaque plastic enclosures containing a layer of sand and a crevice between two ceramic tiles for shelter. Housing was maintained at temperatures and lighting cycles that mimicked natural seasonal variation, with UV lights (ZooMed T8 ReptiSun® 10.0 UVB) above each enclosure (30cm), emitting both UVA and UVB radiation. A heat lamp was provided to generate a thermal gradient and allow the lizards to attain their preferred body temperatures (approx. 36°C). Lizards were misted with water for hydration and fed live crickets dusted with multi-vitamins three times per week. All behavioural trials were conducted during the breeding seasons (August–December) in 2016 and 2017. Research methods used in this study were reviewed and approved by the Animal Ethics Committee of The University of Melbourne (1413220.3) and the South Australian Wildlife Ethics Committee (25/2015).

Female-male behavioural trials

Females are receptive to mating approximately 2–3 weeks after emergence from hibernation, and after laying their first or second clutch. We conducted mate preference trials during these known receptive periods, when females were in good body condition (average mass of 16.7g ± 2.9g), though receptivity cannot be determined with certainty a priori. Each female was paired with both a southern and a northern lineage male, with half of the females paired with a southern male first and the other half with a northern male first. Females were placed into the first male’s enclosure for a period of 24 hours, and then into the second male’s enclosure for the subsequent 24 hours. Both encounters were monitored and recorded using a Swann DVR8-1525 8 channel 960H digital video recorder with a PRO-615 camera attached. We conducted a total of 147 trials, with individual females paired with one southern and one northern male per reproductive cycle, in up to 2 reproductive cycles (average of 3.34 trials, with a range of 2–4 trials, per female).

Videos were analysed using Behavioural Observation Research Interactive Software (BORIS) version 4.1.5 and both female and male behaviour was scored. For females, we recorded the number of head-bobs (pronounced nodding movement of the head), and combined the number of aggressive behaviours (biting and chasing) and times the female fled from the male as a measure of “rejection”. For males, we also recorded the number of head-bobs (courtship behaviour) as well as the number of attempts to copulate, and whether or not copulation was successful. We did not analyse the number of successful copulations as copulation was observed in only 7 of the 147 trials (although more may have taken place under the tile). Lizards were not paired for long enough to ensure mating; rather, we were interested in behaviour during initial contact as an indicator of mate preference.

We tested whether female lineage, male lineage, or their interaction predicted: 1) number of copulation attempts, 2) number of male head-bobs, 3) number of female head-bobs and 4) number of female rejection behaviours using generalised linear mixed models (lme4 package, R). Female ID, male ID and pairing number (female’s first or second trial) were included as random factors in all models to account for repeated use of individuals, and response variables were log transformed to meet model assumptions of normality. We performed pairwise comparisons by calculating least squares means and confidence intervals using the Satterthwaite’s approximation for degrees of freedom (lmerTest package, R).

Male-male behavioural trials

A previous study investigating aggression levels among morphs of the northern lineage found that orange-throated males were significantly more aggressive towards territory intruders than yellow, orange-yellow or grey-throated males. Therefore, we categorised males into three behavioural groups based on lineage and throat colour morph: southern, northern high aggression (orange), or northern low aggression (yellow, orange-yellow, grey). We designed trials such that each focal male was matched with three others, representing each of the behavioural groups, in random order. Pairs were size-matched to minimize the effect of body size on contest outcome, with an average difference of 1.59mm ± 1.16mm snout vent length (SVL) between competing males.

Contest trials were conducted in a neutral 120 × 30 × 60cm (length × width × height) enclosure (i.e. not the home enclosure of either male). An opaque divider initially separated the enclosure into two equally sized holding areas, each containing a layer of sand, ceramic tile and heat lamp. Just prior to the trial, males were weighed to obtain a measure of body condition as the residuals of a linear model of mass and SVL. The designated “focal” and “opponent” males were then placed into the separate holding areas and allowed to acclimatise for 48 hours to establish residency. At the commencement of the trial, the divider was removed and the interaction was recorded from two different angles using Panasonic HC-V770M video cameras. Trials were conducted for a maximum of 25 minutes and monitored to ensure there was no risk of injury to animals (as required under the Animal Ethics permit). Consequently, we did not record contest outcome (i.e. winner, loser) as some trials were stopped before a winner was established. To minimize stress and the potential influence of previous contest outcomes, males were not used in a subsequent trial for at least 48 hours. We conducted a total of 120 trials (involving 42 males), 26 of which were excluded due to no interaction, resulting in 94 trials which were used in the statistical analysis.

We scored focal male behaviour from the video footage using BORIS. C. decresii males perform energetic displays during territory defence prior to engaging in physical aggression. Therefore, we recorded the number of head-bobs, tail flicks and push-ups performed by the focal male as a measure of “display behaviour”, and combined the duration of chasing and wrestling (involving biting) as a measure of “physical aggression”. We also recorded the time between the start of the trial and the focal male’s emergence from beneath the tile (“latency”), as this is an indicator of individual boldness. Display behaviour and physical aggression were divided by the total trial duration (minus latency) to account for differences in trial lengths.

We tested whether behavioural group or body condition predicted: 1) focal male latency to emerge, 2) focal male display behaviour and 3) focal male physical aggression using generalised linear mixed models. We included focal male behavioural group, opponent male behavioural group and their interaction, as well as focal male body condition and opponent male body condition as predictor variables in the models. Additionally, focal male ID and focal male trial number were included as random factors in all models to account for repeated use of individuals. For models 2 (display behaviour) and 3 (physical aggression), the response variables were log transformed to meet model assumptions of normality, and we performed post hoc pairwise comparisons as detailed above.

Supplementary files for article Supplementary information files for Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants.BackgroundComparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents.MethodsFor this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.FindingsWe pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls.InterpretationThe height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks.

Dataset Description: AI-Generated Person Data

This dataset contains 1,001,000 synthetic records representing demographic and physical attributes of individuals. The data is AI-generated and designed to simulate realistic human characteristics without using personally identifiable information (PII).

Structure

Rows: 1,001,000

Columns: 6

Features

id – Unique identifier for each individual (1 to 1,001,000).

dob – Date of birth (ranging across ~32,000 unique values).

age – Age of the person (from -1 to 95, mostly realistic ages but may include outliers like -1).

gender – Binary category (Male, Female), nearly evenly distributed.

height_cm – Height in centimeters (ranging from 50 cm to 209.7 cm).

weight_kg – Weight in kilograms (ranging from 3 kg to 198.9 kg).

Statistical Highlights

Average age: ~38.1 years (with some anomalies).

Average height: ~159.3 cm (std dev ~24.7 cm).

Average weight: ~65.7 kg (std dev ~27.4 kg).

Gender distribution: ~50% Male, ~50% Female.

Applications

This dataset can be used for:

Testing and benchmarking machine learning models.

Simulating healthcare, biometric, or demographic analytics.

Data visualization and statistical analysis practice.

Building and validating data pipelines without real PII.