Life expectancy at birth for males and females for Middle Layer Super Output Areas (MSOAs), Leicester: 2016 to 2020The average number of years a person would expect to live based on contemporary mortality rates.For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.Life expectancy figures have been calculated based on death registrations between 2016 to 2020, which includes the first wave and part of the second wave of the coronavirus (COVID-19) pandemic.

This dataset includes estimates for life expectancy at birth and at age 65, age-standardized death rates, and total deaths, by sex, for countries and territories and subnational units globally for the year 2016.

Among the Gulf Cooperation Council (GCC) countries, in 2016, Qatar had the highest life expectancy at birth at about 78 years. The average life expectancy at birth of the GCC countries was ***** years in 2016.

This dataset presents the footprint of the average number of years a person is expected to live at birth by sex, assuming that the current age-specific death rates are experienced throughout their life. The data spans the years of 2011-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is based on the Australian Institute of Health and Welfare (AIHW) analysis of life expectancy estimates as provided by the Australian Bureau of Statistics (ABS). Life expectancies at birth were calculated with reference to state/territory and Australian life tables (where appropriate) for a three year period. The disaggregation used for reporting life expectancy at birth is PHN area. These values are provided by the ABS. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Life expectancy for 2014-2016 are based on the average number of deaths over three years, 2014-2016, and the estimated resident population (ERP) as at 30 Jun 2015.

This statistic presents a timeline of the life expectancy at birth in Chile from 2006 to 2016, sorted by gender. On average, a person born in Chile in 2016 was expected to live up to 79.5 years of age. Women had a longer life expectancy than men, 81.9 compared with 76.9 years, respectively, as of 2016.

In 2024, the average life expectancy at birth for women in Singapore was 86.5 years. Comparatively, the female life expectancy at birth was 70.9 years in Myanmar. That year, the life expectancy for men in Southeast Asia was also highest in Singapore.

🇬🇧 영국 English Life expectancy at birth for males and females for Middle Layer Super Output Area (MSOAs), Leicester: 2016 to 2020The average number of years a person would expect to live based on contemporary mortality rates.For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.Life expectancy figures have been calculated based on death registrations between 2016 to 2020, which includes the first wave and part of the second wave of the coronavirus (COVID-19) pandemic.

China Life Expectancy data was reported at 78.200 Year Old in 2021. This records an increase from the previous number of 77.930 Year Old for 2020. China Life Expectancy data is updated yearly, averaging 76.340 Year Old from Dec 1981 (Median) to 2021, with 13 observations. The data reached an all-time high of 78.200 Year Old in 2021 and a record low of 67.770 Year Old in 1981. China Life Expectancy data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region. According to the National Health Commission, from 2016 to 2017, the average life expectancy of residents per capita has increased from 76.5 to 76.7 years. For reference only. 根据国家卫生健康委员会，从2016年到2017年，居民人均预期寿命由76.5岁提高到76.7岁。以供參考。

In 2024, the average female life expectancy at birth in the Maldives was **** years. Comparatively, the average life expectancy for women at birth in Afghanistan was **** years in 2024.

Mongolia MN: Life Expectancy at Birth: Male data was reported at 65.277 Year in 2016. This records an increase from the previous number of 65.091 Year for 2015. Mongolia MN: Life Expectancy at Birth: Male data is updated yearly, averaging 57.291 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 65.277 Year in 2016 and a record low of 46.161 Year in 1960. Mongolia MN: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mongolia – Table MN.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This dataset presents life expectancy at birth estimates for males, females and persons. This dataset covers the reference period 2010-12 to 2017-19, and is based on Statistical Area Level 4 (SA4), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). For further information please visit the Australian Bureau of Statistics. Internationally, life tables are used to measure mortality. In its simplest form, a life table is generated from age-specific death rates and the resulting values are used to measure mortality, survivorship and life expectancy. The life table depicts the mortality experience of a hypothetical group of newborn babies throughout their entire lifetime. It is based on the assumption that this group is subject to the age-specific mortality rates of the reference period. Typically this hypothetical group is 100,000 persons in size. AURIN has spatially enabled the original data.

The average number of years a newborn can expect to live, assuming he or she experiences the currently prevailing rates of death through their lifespan. Source: Baltimore City Health Department Years Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018

Analysis of ‘🍷 Alcohol vs Life Expectancy’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/alcohol-vs-life-expectancye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Credit: This dataset was created by Jonathan Ortiz! All credits for the original go to the original author!

About this dataset

Intro

Findings

There is a surprising relationship between alcohol consumption and life expectancy. In fact, the data suggest that life expectancy and alcohol consumption are positively correlated - 1.2 additional years for every 1 liter of alcohol consumed annually. This is, of course, a spurious finding, because the correlation of this relationship is very low - 0.28. This indicates that other factors in those countries where alcohol consumption is comparatively high or low are contributing to differences in life expectancy, and further analysis is warranted.

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

Methods

1. Addressing Missing Values

The original drinks.csv file in the UNCC/DSBA-6100 dataset was missing values for The Bahamas, Denmark, and Macedonia for the wine, spirits, and beer attributes, respectively. Drinks_solution.csv shows these values filled in, for which I used the Mean of the rest of the data column.

Other methods were considered and ruled out:

• Deleting the Bahamas, Denmark, and Macedonia instances altogether - This is a possible route, but the data file itself is just under 200 rows, and there is only one observation for each country. Because the dataset is relatively small by number of instances, removal should be avoided in order to give the model more data to use.
• Imputing missing values with k-Nearest Neighbors - Another possible route, knn impute can yield higher accuracy in certain cases when the dataset is fairly large. However, this particular dataset only contains 3 attributes, all of which seem unrelated to each other. If we had more columns with more data like availability, annual sales, preferences, etc. of the different drinks, it would be possible to predict these values with knn, but this approach should be avoided given the data we have.
• Filling missing values with a MODE - By visualizing the data, it is easy to see that each column is fairly skewed, with many countries reporting 0 in one or more of the servings columns. Using the MODE would fill these missing entries with 0 for all three (beer_servings, spirit_servings, and wine_servings), and upon reviewing the Bahamas, Denmark, and Macedonia more closely, it is apparent that 0 would be a poor choice for the missing values, as all three countries clearly consume alcohol.
• Filling missing values with MEDIAN - Due to the skewness mentioned just above in the MODE section, using a MEDIAN of the whole column would also be a poor choice, as the MEDIAN is pulled down by several countries reporting 0 or 1. A MEDIAN of only the observations reporting 1 or more servings--or another cutoff--could be used, however, and this would be acceptable.

Filling missing values with MEAN - In the case of the drinks dataset, this is the best approach. The MEAN averages for the columns happen to be very close to the actual data from where we sourced this exercise. In addition, the MEAN will not skew the data, which the prior approaches would do.

2. Calculating New Attributes

The original drinks.csv dataset also had an empty data column: total_litres_of_pure_alcohol. This column needed to be calculated in order to do a simple 2D plot and trendline. It would have been possible to instead run a multi-variable regression on the data and therefore skip this step, but this adds an extra layer of complication to understanding the analysis - not to mention the point of the exercise is to go through an example of calculating new attributes (or "feature engineering") using domain knowledge.

The graphic found at the Wikipedia / Standard Drink page shows the following breakdown:

• Beer - 12 fl oz per serving - 5% average ABV
• Wing - 5 fl oz - 12% ABV
• Spirits - 1.5 fl oz - 40% ABV

The conversion factor from fl oz to L is 1 fl oz : 0.0295735 L

Therefore, the following formula was used to compute the empty column:
total_litres_of_pure_alcohol
=
(beer_servings * 12 fl oz per serving * 0.05 ABV + spirit_servings * 1.5 fl oz * 0.4 ABV + wine_servings * 5 fl oz * 0.12 ABV) * 0.0295735 liters per fl oz

3. Joining To External Data

The lifeexpectancy.csv datafile in the https://data.world/uncc-dsba/dsba-6100-fall-2016 dataset contains life expectancy data for each country. The following query will join this data to the cleaned drinks.csv data file:

# Life Expectancy vs Alcohol Consumption

Life Expectancy vs. Alcohol Consumption with countryTable

PREFIX drinks: <http://data.world/databeats/alcohol-vs-life-expectancy/drinks_solution.csv/drinks_solution#>
PREFIX life: <http://data.world/uncc-dsba/dsba-6100-fall-2016/lifeexpectancy.csv/lifeexpectancy#>
PREFIX countries: <http://data.world/databeats/alcohol-vs-life-expectancy/countryTable.csv/countryTable#>

SELECT ?country ?alc ?years
WHERE {
  SERVICE <https://query.data.world/sparql/databeats/alcohol-vs-life-expectancy> {
    ?r1 drinks:total_litres_of_pure_alcohol ?alc .
    ?r1 drinks:country ?country .
    ?r2 countries:drinksCountry ?country .
    ?r2 countries:leCountry ?leCountry .
  }

  SERVICE <https://query.data.world/sparql/uncc-dsba/dsba-6100-fall-2016> {
    ?r3 life:CountryDisplay ?leCountry .
    ?r3 life:GhoCode ?gho_code .
    ?r3 life:Numeric ?years .
    ?r3 life:YearCode ?reporting_year .
    ?r3 life:SexDisplay ?sex .
  }
FILTER ( ?gho_code = "WHOSIS_000001" && ?reporting_year = 2013 && ?sex = "Both sexes" )
}
ORDER BY ?country

4. Plotting

The resulting joined data can then be saved to local disk and imported into any analysis tool like Excel, Numbers, R, etc. to make a simple scatterplot. A trendline and R^2 should be added to determine the relationship between Alcohol Consumption and Life Expectancy (if any).

https://data.world/api/databeats/dataset/alcohol-vs-life-expectancy/file/raw/LifeExpectancy_v_AlcoholConsumption_Plot.jpg" alt="LifeExpectancy_v_AlcoholConsumption_Plot.jpg">

This dataset was created by Jonathan Ortiz and contains around 200 samples along with Beer Servings, Spirit Servings, technical information and other features such as: - Total Litres Of Pure Alcohol - Wine Servings - and more.

How to use this dataset

• Analyze Beer Servings in relation to Spirit Servings
• Study the influence of Total Litres Of Pure Alcohol on Wine Servings
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Jonathan Ortiz

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Average life expectancy of a newborn male in years 2015/2016/2017

This table contains forecast figures from the period survival tables (per period of 1 year) by gender and age (on 31 December) for the population of the Netherlands. The table shows how many boys or girls from a group of 100,000 newborns will reach the age of 0, 1, 2, etc. on December 31 of the year of observation. It can also be determined how old these children will be on average if the mortality probabilities of the prognosis year apply throughout their lives. This period life expectancy can therefore best be interpreted as a summary measure of the mortality probabilities in a calendar year. See section 4 for an explanation of the difference between the period survival table and a cohort survival table. The table can be broken down into the mortality probability, the number of people alive (table population), the number of deaths (table population) and the period life expectancy by gender and age. Data available: 2016-2060 Status of the figures: The figures in this table are calculated forecast figures. Changes as of December 19, 2017: This table has been discontinued. See section 3 for the successor to this table. Changes as of December 16, 2016: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations that have now become available. The forecast period now runs from 2016 to 2060. When will new figures be released? The publication frequency of this table is one-off. The new population forecast table will be published in December 2017.

Analysis of ‘LifeExpectancyData’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/just249/lifeexpectancydatacsv on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Life Expectancy Prediction Using Artificial Intelligence: Research Paper: https://docs.google.com/document/d/1Abwx7C97sMjsfow5Xk8GOCDblNaQr8T8WXh7SIJAbVo/edit?usp=sharing

Introduction: According to the survey from PwC (PricewaterhouseCoopers) report in 2016, data have shown that nearly half (47%) of 18-34 age group surveyed had changed their eating habits towards a healthier diet and further data has shown that 53% of the age 18-34 claimed that they have planned to change their eating habits to be healthier over the next year. According to research done by LiveScience, eating healthy and doing physical activity can in fact increase our life expectancy, also in one of the articles from BBC (British Broadcasting Corporation) “Do we really live longer than our ancestors? ” have stated that in 1841, a baby girl and boy was expected to live just about 40 years of age, but in 2016 a baby girl or boy was expected to live till 80 years of age. Controllable factors like eating healthy and doing exercise regularly can in fact increase our life expectancy. But can non-controllable factors like Country’s status, mortality rates, GDP, schooling, average income, government’s expenditure on health and the rate of child deaths possibly affect our life expectancy? To answer those concerns, we will input data from a Dataset called “Life Expectancy(WHO)” provided by Kumar Rajarshi in Kaggle and with the help of machine learning to process a considerable amount of data to train and analyze and make a prediction of life expectancy based on the value we feed to the algorithm.

Project Details: For this project, I have used the Dataset called “Life Expectancy(WHO)” provided by Kumar Rajarshi from Kaggle, to try to predict the total life expectancy by inputting non-controllable factors according to the data set like Country’s status, mortality rates, GDP, schooling, average income, government’s expenditure on health and the rate of child deaths to answer will non-controllable factor affect our life expectancy.

--- Original source retains full ownership of the source dataset ---

Georgia GE: Life Expectancy at Birth: Total data was reported at 73.261 Year in 2016. This records an increase from the previous number of 73.096 Year for 2015. Georgia GE: Life Expectancy at Birth: Total data is updated yearly, averaging 70.220 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 73.261 Year in 2016 and a record low of 63.651 Year in 1960. Georgia GE: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This statistics shows the death rate for all causes in the U.S. in 2015 and 2016, by age group. In 2016, there were some ***** deaths by all causes in the age between 75 and 84 years per 100,000 inhabitants in the United States.

United Kingdom UK: Life Expectancy at Birth: Total data was reported at 80.956 Year in 2016. This stayed constant from the previous number of 80.956 Year for 2015. United Kingdom UK: Life Expectancy at Birth: Total data is updated yearly, averaging 75.380 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 81.305 Year in 2014 and a record low of 70.827 Year in 1963. United Kingdom UK: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Average Age of Population 2011 to 2016