原文：

Life Expectancy (WHO)

Statistical Analysis on factors influencing Life Expectancy

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:?Immunization related factors, Mortality factors, Economical factors and Social factors.

The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The datasets are made available to public for the purpose of health data analysis. The dataset related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single dataset. On initial visual inspection of the data showed some missing values. As the datasets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo,Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model dataset. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:?Immunization related factors, Mortality factors, Economical factors and Social factors.

譯：

預(yù)期壽命（世衛(wèi)組織）

影響預(yù)期壽命因素的統(tǒng)計(jì)分析

盡管考慮到人口變量、收入構(gòu)成和死亡率，過(guò)去對(duì)影響預(yù)期壽命的因素進(jìn)行了大量研究。結(jié)果發(fā)現(xiàn)，過(guò)去沒(méi)有考慮免疫和人類(lèi)發(fā)展指數(shù)的影響。此外，過(guò)去的一些研究考慮了基于所有國(guó)家一年數(shù)據(jù)集的多元線(xiàn)性回歸。因此，在考慮所有國(guó)家2000年至2015年期間的數(shù)據(jù)時(shí)，通過(guò)基于混合效應(yīng)模型和多元線(xiàn)性回歸建立回歸模型，這為解決上述兩個(gè)因素提供了動(dòng)力。重要的免疫接種，如乙型肝炎、脊髓灰質(zhì)炎和白喉也將被考慮。簡(jiǎn)而言之，本研究將關(guān)注免疫因素、死亡率因素、經(jīng)濟(jì)因素、社會(huì)因素以及其他與健康相關(guān)的因素。由于該數(shù)據(jù)集的觀測(cè)結(jié)果基于不同的國(guó)家，因此一個(gè)國(guó)家更容易確定導(dǎo)致預(yù)期壽命降低的預(yù)測(cè)因素。這將有助于建議一個(gè)國(guó)家應(yīng)該重視哪個(gè)地區(qū)，以便有效地提高其人口的預(yù)期壽命。

該項(xiàng)目依賴(lài)于數(shù)據(jù)的準(zhǔn)確性。世界衛(wèi)生組織（WHO）下屬的全球衛(wèi)生觀測(cè)站（GHO）數(shù)據(jù)存儲(chǔ)庫(kù)跟蹤所有國(guó)家的健康狀況以及許多其他相關(guān)因素。這些數(shù)據(jù)集可供公眾使用，以進(jìn)行衛(wèi)生數(shù)據(jù)分析。193個(gè)國(guó)家與預(yù)期壽命、健康因素有關(guān)的數(shù)據(jù)集是從同一世衛(wèi)組織數(shù)據(jù)庫(kù)網(wǎng)站收集的，其相應(yīng)的經(jīng)濟(jì)數(shù)據(jù)是從聯(lián)合國(guó)網(wǎng)站收集的。在所有類(lèi)型的健康相關(guān)因素中，只有那些更具代表性的關(guān)鍵因素被選中。據(jù)觀察，與過(guò)去30年相比，在過(guò)去15年中，衛(wèi)生部門(mén)有了巨大的發(fā)展，導(dǎo)致了人類(lèi)死亡率的提高，尤其是在發(fā)展中國(guó)家。因此，在本項(xiàng)目中，我們考慮了193個(gè)國(guó)家2000-2015年的數(shù)據(jù)，以供進(jìn)一步分析。各個(gè)數(shù)據(jù)文件已合并到一個(gè)數(shù)據(jù)集中。在最初的目視檢查中，數(shù)據(jù)顯示了一些缺失值。由于數(shù)據(jù)集來(lái)自世衛(wèi)組織，我們沒(méi)有發(fā)現(xiàn)明顯的錯(cuò)誤。在R軟件中使用Missmap命令處理丟失的數(shù)據(jù)。結(jié)果表明，大部分缺失數(shù)據(jù)是關(guān)于人口、乙肝和GDP的。缺少的數(shù)據(jù)來(lái)自瓦努阿圖、湯加、多哥、佛得角等鮮為人知的國(guó)家。很難找到這些國(guó)家的所有數(shù)據(jù)，因此，我們決定將這些國(guó)家排除在最終模型數(shù)據(jù)集中。最終合并的文件（最終數(shù)據(jù)集）由22列2938行組成，這意味著20個(gè)預(yù)測(cè)變量。然后將所有預(yù)測(cè)變量分為幾個(gè)大類(lèi)：?免疫相關(guān)因素、死亡因素、經(jīng)濟(jì)因素和社會(huì)因素。

世界衛(wèi)生組織（WHO）下屬的全球衛(wèi)生觀測(cè)站（GHO）數(shù)據(jù)存儲(chǔ)庫(kù)跟蹤所有國(guó)家的健康狀況以及許多其他相關(guān)因素。這些數(shù)據(jù)集可供公眾使用，以進(jìn)行衛(wèi)生數(shù)據(jù)分析。193個(gè)國(guó)家與預(yù)期壽命、健康因素有關(guān)的數(shù)據(jù)集是從同一世衛(wèi)組織數(shù)據(jù)儲(chǔ)存庫(kù)網(wǎng)站收集的，其相應(yīng)的經(jīng)濟(jì)數(shù)據(jù)是從聯(lián)合國(guó)網(wǎng)站收集的。在所有類(lèi)型的健康相關(guān)因素中，只有那些更具代表性的關(guān)鍵因素被選中。據(jù)觀察，與過(guò)去30年相比，在過(guò)去15年中，衛(wèi)生部門(mén)有了巨大的發(fā)展，導(dǎo)致了人類(lèi)死亡率的提高，尤其是在發(fā)展中國(guó)家。因此，在本項(xiàng)目中，我們考慮了193個(gè)國(guó)家2000-2015年的數(shù)據(jù)，以供進(jìn)一步分析。各個(gè)數(shù)據(jù)文件已合并到一個(gè)數(shù)據(jù)集中。在最初的目視檢查中，數(shù)據(jù)顯示了一些缺失值。由于數(shù)據(jù)集來(lái)自世衛(wèi)組織，我們沒(méi)有發(fā)現(xiàn)明顯的錯(cuò)誤。在R軟件中使用Missmap命令處理丟失的數(shù)據(jù)。結(jié)果表明，大部分缺失數(shù)據(jù)是關(guān)于人口、乙肝和GDP的。缺少的數(shù)據(jù)來(lái)自瓦努阿圖、湯加、多哥、佛得角等鮮為人知的國(guó)家。很難找到這些國(guó)家的所有數(shù)據(jù)，因此，我們決定將這些國(guó)家從最終模型數(shù)據(jù)集中排除。最終合并的文件（最終數(shù)據(jù)集）由22列2938行組成，這意味著20個(gè)預(yù)測(cè)變量。然后將所有預(yù)測(cè)變量分為幾個(gè)大類(lèi)：?免疫相關(guān)因素、死亡因素、經(jīng)濟(jì)因素和社會(huì)因素。

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