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Kohler Population Studies Center University of Pennsylvania Overall Structure of the Survey on Aging in SSA 2 major components: • Household Interview • Individual Interview • Both will be linked based on HH ID and individual ID Overall Sampling Design  Nationally representative stratified random sample of households that include at least 1 household member age 60 years and older  Household sample surveys:  Key source for data on social phenomena  Are among the most flexible methods of data collections  In theory almost any population-based subject can be investigated through household surveys  Only probability samples following well-established sampling procedures are suitable for making inferences from the sample population to the larger population that it is designed to represent  Snow-ball or convenience samples are not suitable for this survey Overall Sampling Design cont’d  Probability sampling in the context of household surveys:  Refers to the means by which elements of the target population are selected for inclusion in the survey  In order to be cost-effective, most household surveys are not implemented as simple random samples  Sampling procedure usually includes stratification to ensure that the selected sample actually is spread over geographic sub-areas and population subgroups  This sampling design usually uses clusters of households in order to keep costs to manageable level General Principals of the Survey on Aging in SSA Target population: individuals age 60+ and older Household sample: Nationally representative clustered random sample of households that include household members age 60+ yrs. Selection of household members: All regular household members age 60+ in the sampled household and their spouses if these are age-eligible and co-resident General Principals of the Survey on Aging in SSA Use of an existing sampling frame: clustered random sample of households can only be obtained from existing sampling frame which is a complete list of statistical units covering the target population  Census frame, complete list of villages/communities or sampling list from other nationally representative surveys  Sampling frame: is a complete list of sampling units that entirely covers the target population  Conventional sampling frame: list of enumeration areas (EA) from a recently completed census  EA: geographic area which usually groups a number of households together for convenient counting purposes General Principals of the Survey on Aging in SSA Stratification: process in which the sample is designed into sub- groups or strata that are as homogeneous as possible;  Within each stratum the sample is designed and selected independently; Two-stage cluster sampling procedure:  Cluster: a group of adjacent households which serves as the primary sampling unit (PSU) General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings Sample Size  Sample size must take into account competing needs so that costs and precisions are optimally balanced  Sample size must also address the needs of users who desire for sub-populations of sub-areas domains  Sample size is determined by the trade-offs between survey precision, data quality, organizational capacities and survey budget;  In the case of Malawi this is about 2,000 respondents (men and women) Conducting a household listing and pre-selection of households  Data quality is enhances if eligible households are preselected for participating in the study  In many SSA countries recent and reliable household listings in EAs that carefully enumerates older individuals is not available  Hence, we suggest to conduct a specific household listing in selected EAs that provides a well-grounded basis for selecting respondents  Interviewers than interview only pre-selected eligible households  STEPS:  Household listing operation conducted before the survey  Pre-selection of households from this list  Selected Households are interviewed Overall Sampling Design cont’d  Two stage sample design is well-established approach for implementing household surveys  1st stage: select a sample of EAs with probability proportional to size (PPS);  Within each stratum a sample of predetermined number of EAs is selected independently with probability proportional to size, where size is measured in terms of older individuals age 60+;  If size of pop age 60+ is not available, and variations in age structures are relatively modest, then total pop size can be used  All households in the EAs are listed  2nd stage: after complete listing in EAs, a fixed number of households with individuals age 60+ is selected by equal probability sampling in the EAs Interviewing all individuals age 60+ in the HH Advantages:  Maximize the number of respondents for a given sample of HH  Cost effective to achieve the sample size  Analytical advantages so that interactions among spouses, within and between household variation of outcomes can be investigated Disadvantages:  Lower statistical power given the within household correlation of observations  Logistical challenges in the fieldwork Sample Take per Cluster  How many eligible individuals to interview per EA  DHS recommends 25-30 individuals  Because there will be more than 1 age-eligible individual per household, less than 24-30 households per PA need to be selected  If a sampled HH has 1.5 age-eligible individuals on average, than a sample take per cluster of 25-30 individuals results in the selection of 17-20 households per cluster  With 2,000 individuals sample size: 67-80 clusters have to be selected  If sample is stratified, these considerations should be conducted stratum- specific Sample Take per Cluster  This fixed sample take per cluster is:  Easy for survey management and implementation  But requires sampling weights that vary within clusters
Language:English
Score: 776401.86 - https://www.un.org/development...07_ikohler-sampling-design.pdf
Data Source: un
How- • Base infection probabilities. Given the fact ever, in general WiFi systems, an individual may that we cannot test every individual, each untested have multiple devices, and removing the duplication individual has a base probability of being infected. is significant in this case. (...) This tion probability can be deduced from the positive chance may also be time-variant. (...) For in- probability that got infection from a contact with stance, a university randomly tested 1,000 students .
Language:English
Score: 764463.96 - https://www.itu.int/en/publica.../files/basic-html/page113.html
Data Source: un
Total collision rates is obtained by summing up all the shell contributions • Debris-Debris, Debris-Intact, and Intact-Intact collisions • Probability of “k” collisions in a given time period (in 2,000 km orbital region) Probability of k collisions in LEO (Debris-Debris, Debris-Intact, and Intact-Intact collisions) All collision probabilities are calculated beginning mid-2013. Probability of exactly 4 collision by 2030 = 18.7% Probability of 1 to 4 collision by 2030 = 50% 8 ISSUE 2 HOW “PROBABLE” IS IT FOR TWO THINGS TO GO BOOM IN SPACE? (...) ISSUE 1 WHAT HAPPENS WHEN TWO THINGS GO BOOM IN SPACE? Are the “probabilities” useful? • The probabilities determined in the previous slides are a “bird’s eye” view.
Language:English
Score: 761868.9 - https://www.unidir.org/sites/d...rin-presentation-eng-0-803.pdf
Data Source: un
ITU-R P.618. 2 Modelling of the probability of rain attenuation on Earth-space link Knowing N equally distributed points on the ground projection of the path of length L and under the rain height Hr as illustrated in Figure 1, the probability to have non-zero rain attenuation on the path, P(AR>0) , is equal to the complementary probability to not have rain on each point of the path: (1) with: and . Figure 1 Geometry of the link 2.1 Expression of the probability of rain attenuation from the spatial correlation of rain rate fields The joint probability can be expressed from the correlation between the random variables R(x1) , R(x2) ,…, R(xN) . (...) Figure 3 Probability to have rain attenuation on the link in function of the probability of rain and the ground projection of the link This contribution has presented a model of the probability to have rain attenuation of an Earth-space link.
Language:English
Score: 756038.96 - https://www.itu.int/dms_pub/it...0a/04/R0A0400007F0001MSWE.docx
Data Source: un
The implementation always uses a 0 to represent the most-probable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model. Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 754118.5 - https://www.itu.int/wftp3/av-a...e/2002_05_Fairfax/JVT-C029.doc
Data Source: un
The implementation always uses a 0 to represent the most-probable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model. Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 754118.5 - https://www.itu.int/wftp3/av-a...deo-site/0201_Gen/JVT-B033.doc
Data Source: un
The implementation always uses a 0 to represent the most-probable bit (MPB) and a 1 to represent the least probable bit (LPB). (...) The model uses an adaptive probability update rate designed to match the update rate of the current CABAC probability model. (...) Figure 8: Code to initialize the probability model. Figure 9: Code to update the probability model for each new bit.
Language:English
Score: 754118.5 - https://www.itu.int/wftp3/av-a...te/2002_01_Geneva/JVT-B033.doc
Data Source: un
Model Life Tables : Lexis Plots | United Nations Population Division | Department of Economic and Social Affairs Skip to main navigation Skip to content Welcome to the United Nations Department of Economic and Social Affairs Population Population Search HOME COMMISSION THEMES DOCUMENTS EVENTS PUBLICATIONS ABOUT US Publications Current Publications Databases Datasets Data Booklet Manuals Population Facts Newsletter Expert Paper Series Technical Paper Series Maps Archive Extended Model Life Tables : Scatter Plots Back to Extended Model Life Tables                   Analyses of Revised UN Model Life Tables Scatter plots showing relations between different quantities in the revised UN Model Life Tables with superimposed data from 5x5 life tables from Human Mortality Database (www.mortality.org) CD East_100_5g0_45g15_f CD East_100_q0_4q1_m CD North_100_q0_4q1_m CD South_100_q0_4q1_m CD West_100_q0_4q1_m UN Chilean_100_q0_4q1_m UN Far_East_Asian_100_q0_4q1_m UN General_100_q0_4q1_m UN Latin_100_q0_4q1_m UN South_Asian_100_q0_4q1_m CD East_100_q0_4q1_f CD North_100_q0_4q1_f CD South_100_q0_4q1_f CD West_100_q0_4q1_f UN Chilean_100_q0_4q1_f UN Far_East_Asian_100_q0_4q1_f UN General_100_q0_4q1_f UN Latin_100_q0_4q1_f UN South_Asian_100_q0_4q1_f CD East_100_5q0_45q15_m CD North_100_5q0_45q15_m CD South_100_5q0_45q15_m CD West_100_5q0_45q15_m UN Chilean_100_5q0_45q15_m UN Far_East_Asian_100_5q0_45q15_m UN General_100_5q0_45q15_m UN Latin_100_5q0_45q15_m UN South_Asian_100_5q0_45q15_m CD East_100_5q0_45q15_f CD North_100_5q0_45q15_f CD South_100_5q0_45q15_f CD West_100_5q0_45q15_f UN Chilean_100_5q0_45q15_f UN Far_East_Asian_100_5q0_45q15_f UN General_100_5q0_45q15_f UN Latin_100_5q0_45q15_f UN South_Asian_100_5q0_45q15_f CD East_100_e0_e60_m CD North_100_e0_e60_m CD South_100_e0_e60_m CD West_100_e0_e60_m UN Chilean_100_e0_e60_m UN Far_East_Asian_100_e0_e60_m UN General_100_e0_e60_m UN Latin_100_e0_e60_m UN South_Asian_100_e0_e60_m CD East_100_e0_e60_f CD North_100_e0_e60_f CD South_100_e0_e60_f CD West_100_e0_e60_f UN Chilean_100_e0_e60_f UN Far_East_Asian_100_e0_e60_f UN General_100_e0_e60_f UN Latin_100_e0_e60_f UN South_Asian_100_e0_e60_f CD East_100_q0_45q15_m CD North_100_q0_45q15_m CD South_100_q0_45q15_m CD West_100_q0_45q15_m UN Chilean_100_q0_45q15_m UN Far_East_Asian_100_q0_45q15_m UN General_100_q0_45q15_m UN Latin_100_q0_45q15_m UN South_Asian_100_q0_45q15_m CD East_100_q0_45q15_f CD North_100_q0_45q15_f CD South_100_q0_45q15_f CD West_100_q0_45q15_f UN Chilean_100_q0_45q15_f UN Far_East_Asian_100_q0_45q15_f UN General_100_q0_45q15_f UN Latin_100_q0_45q15_f UN South_Asian_100_q0_45q15_f CD East_100_e0_5e0_m CD North_100_e0_5e0_m CD South_100_e0_5e0_m CD West_100_e0_5e0_m UN Chilean_100_e0_5e0_m UN Far_East_Asian_100_e0_5e0_m UN General_100_e0_5e0_m UN Latin_100_e0_5e0_m UN South_Asian_100_e0_5e0_m CD East_100_e0_5e0_f CD North_100_e0_5e0_f CD South_100_e0_5e0_f CD West_100_e0_5e0_f UN Chilean_100_e0_5e0_f UN Far_East_Asian_100_e0_5e0_f UN General_100_e0_5e0_f UN Latin_100_e0_5e0_f UN South_Asian_100_e0_5e0_f CD East_100_e0_45e15_m CD North_100_e0_45e15_m CD South_100_e0_45e15_m CD West_100_e0_45e15_m UN Chilean_100_e0_45e15_m UN Far_East_Asian_100_e0_45e15_m UN General_100_e0_45e15_m UN Latin_100_e0_45e15_m UN South_Asian_100_e0_45e15_m CD East_100_e0_45e15_f CD North_100_e0_45e15_f CD South_100_e0_45e15_f CD West_100_e0_45e15_f UN Chilean_100_e0_45e15_f UN Far_East_Asian_100_e0_45e15_f UN General_100_e0_45e15_f UN Latin_100_e0_45e15_f UN South_Asian_100_e0_45e15_f CD East_100_g0_4g1_m CD North_100_g0_4g1_m CD South_100_g0_4g1_m CD West_100_g0_4g1_m UN Chilean_100_g0_4g1_m UN Far_East_Asian_100_g0_4g1_m UN General_100_g0_4g1_m UN Latin_100_g0_4g1_m UN South_Asian_100_g0_4g1_m CD East_100_g0_4g1_f CD North_100_g0_4g1_f CD South_100_g0_4g1_f CD West_100_g0_4g1_f UN Chilean_100_g0_4g1_f UN Far_East_Asian_100_g0_4g1_f UN General_100_g0_4g1_f UN Latin_100_g0_4g1_f UN South_Asian_100_g0_4g1_f CD East_100_5g0_45g15_m CD North_100_5g0_45g15_m CD South_100_5g0_45g15_m CD West_100_5g0_45g15_m UN Chilean_100_5g0_45g15_m UN Far_East_Asian_100_5g0_45g15_m UN General_100_5g0_45g15_m UN Latin_100_5g0_45g15_m UN South_Asian_100_5g0_45g15_m CD East_100_5g0_45g15_f CD North_100_5g0_45g15_f CD South_100_5g0_45g15_f CD West_100_5g0_45g15_f UN Chilean_100_5g0_45g15_f UN Far_East_Asian_100_5g0_45g15_f UN General_100_5g0_45g15_f UN Latin_100_5g0_45g15_f UN South_Asian_100_5g0_45g15_f CD East_100_g0_45g15_m CD North_100_g0_45g15_m CD South_100_g0_45g15_m CD West_100_g0_45g15_m UN Chilean_100_g0_45g15_m UN Far_East_Asian_100_g0_45g15_m UN General_100_g0_45g15_m UN Latin_100_g0_45g15_m UN South_Asian_100_g0_45g15_m CD East_100_g0_45g15_f CD North_100_g0_45g15_f CD South_100_g0_45g15_f CD West_100_g0_45g15_f UN Chilean_100_g0_45g15_f UN Far_East_Asian_100_g0_45g15_f UN General_100_g0_45g15_f UN Latin_100_g0_45g15_f UN South_Asian_100_g0_45g15_f CD East_100_e0_45g15_m CD North_100_e0_45g15_m CD South_100_e0_45g15_m CD West_100_e0_45g15_m UN Chilean_100_e0_45g15_m UN Far_East_Asian_100_e0_45g15_m UN General_100_e0_45g15_m UN Latin_100_e0_45g15_m UN South_Asian_100_e0_45g15_m CD East_100_e0_45g15_f CD North_100_e0_45g15_f CD South_100_e0_45g15_f CD West_100_e0_45g15_f UN Chilean_100_e0_45g15_f UN Far_East_Asian_100_e0_45g15_f UN General_100_e0_45g15_f UN Latin_100_e0_45g15_f UN South_Asian_100_e0_45g15_f CD East_100_5g0_e0_m CD North_100_5g0_e0_m CD South_100_5g0_e0_m CD West_100_5g0_e0_m UN Chilean_100_5g0_e0_m UN Far_East_Asian_100_5g0_e0_m UN General_100_5g0_e0_m UN Latin_100_5g0_e0_m UN South_Asian_100_5g0_e0_m CD East_100_5g0_e0_f CD North_100_5g0_e0_f CD South_100_5g0_e0_f CD West_100_5g0_e0_f UN Chilean_100_5g0_e0_f UN Far_East_Asian_100_5g0_e0_f UN General_100_5g0_e0_f UN Latin_100_5g0_e0_f UN South_Asian_100_5g0_e0_f Legend: q0 - infant mortality rate 4q1 - age-specific probability of dying from age 1 to age 5 5q0 - age-specific probability of dying from birth to age 5 45q15 - age-specific probability of dying from age 15 to age 60 g0 - logit of infant mortality rate 4g1 - logit of age specific probability of dying from age 1 to 4 5g0 - logit of age-specific probability of dying from birth to age 5 45g15 - logit of age-specific probability of dying from age 15 to age 60 e0 - life expectancy at birth 5e0 - temporary life expectancy between birth and age 5, 5L0 / l0 45e15 - temporary life expectancy between ages 15 and 60, 45L15 / l15 e60 - remaining life expectancy at age 60 Note: UN-MLT 2010 is based on the Coale-Demeny (1983) and UN (1982) model life tables and was extended to cover life expectancy up to 100+ Source: United Nations, Department of Economic and Social Affairs, Population Division (2012): United Nations Model Life Tables (UN-MLT 2010).
Language:English
Score: 751024.4 - https://www.un.org/en/developm...ortality/mlt-scatter-plots.asp
Data Source: un
PowerPoint Presentation SEA Pilot Project to Support Spatial Planning in Gurjaani Practical Aspects of Effective SEA Implementation Doug Hickman Nova Scotia, Canada Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Transport Residential Industrial Etc Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Transport New road – capacity: 500 cars/hour Residential New apartments for 2000 people Single family houses for 1000 people Industrial Light industrial buildings – 10 ha Warehousing – 4 ha Etc Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Transport New road – capacity: 500 cars/hour Land use change Noise Residential New apartments for 2000 people Single family houses for 1000 people Land use change Demand for environ- mental services Industrial Light industrial buildings – 10 ha Warehousing – 4 ha Spills of hazardous substances Etc Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Transport New road – capacity: 500 cars/hour Land use change Noise Loss of habitat Loss of habitat Loss of high- quality agricultural soil Ambiance of sites impacted by vehicle noise Removal of houses Residential New apartments for 2000 people Single family houses for 1000 people Land use change Demand for environ- mental services Loss of habitat Loss of habitat Soil pollution from Increased solid waste Increase in GHG’s from organic waste Odour from organic waste Increase in water demand exceeds rate of recharge Industrial Light industrial buildings – 10 ha Warehousing – 4 ha Spills of hazardous substances Air pollution from spilled items Water pollution from spilled items Toxic impact of spilled items Water pollution from spilled materials Etc Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend Example of Scoping Matrix Planning Item Proposed Action Stressor Biodiversity Soil Climate Air Water Landscape Protected Areas Cultural Heritage Health Socio- Economic Trans- boundary Effects Flora Fauna Transport New road – capacity: 500 cars/hour Land use change Noise Loss of habitat Loss of habitat Loss of high- quality agricultural soil Ambiance of sites impacted by vehicle noise Removal of houses Residential New apartments for 2000 people Single family houses for 1000 people Land use change Demand for environ- mental services Loss of habitat Loss of habitat Soil pollution from Increased solid waste Increase in GHG’s from organic waste Odour from organic waste Increase in water demand exceeds rate of recharge Industrial Light industrial buildings – 10 ha Warehousing – 4 ha Spills of hazardous substances Air pollution from spilled items Water pollution from spilled items Toxic impact of spilled items Water pollution from spilled materals Etc Probable significant negative effect – to be addressed in SEA Report Probable insignificant negative effect – will be briefly addressed in SEA Report No significant effect or positive effect – will not be addressed in SEA Report Legend SEA Pilot Project to Support Spatial Planning in Gurjaani Example of Scoping Matrix Example of Scoping Matrix Example of Scoping Matrix Example of Scoping Matrix Example of Scoping Matrix Example of Scoping Matrix
Language:English
Score: 748392.33 - https://unece.org/sites/defaul...EA_16%20March%202022_ENG_0.pdf
Data Source: un
Single Year Mortality Under Age 5 Latin American Pattern M a l e s Interpolation parameters T(f) Life exp at birth E(O) 35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 43.00 44.00 Probability of dying between ages X and Y Q(X-Y) Survivors at age X IOU Life exp at birth E(O) Single Year Mortality Under Age 5 Latin American Pattern -Both sexes combined Probability of dying between ages X and Y Q(X-Y) Single Year Mortality Under Age 5 Chilean Pattern - Males life exp at birth Probability of dyins between ages X and Y Q(X-Y) Survivors at age X I00 Interpolation parameters T(1) T(1) T(2) T(3) Single Year Mortality Under Age 5 Chilean Pattern - Females Life exp at birth Probability of dying between ages X and Y Q(X-Y) Survivors at age X I(X) Interpolation parameters T(I) T(1) T(2 ) T(3) .06980 .I7984 .32596 .06630 .I7446 .32 181 .06295 .I6930 .3 1779 .05973 .I 6412 .31373 .05665 .I5917 .30978 .05368 .I5428 .30585 ,05085 .1496 1 .30202 .04812 .I4495 .298 17 .04550 .I4038 .29434 .04299 .I3596 .29059 Single Year Mortality Under Age 5 Chilean Pattern - Both sexes combined Life exp at birth Probability of dying between ages X and Y Q(X-Y) Survivors at age X I00 Single Year Mortality Under Age 5 South Asian Pattern - Females Life exp Pmbability of dying between ages X and Y Survivors at age X at birth Q(X-Y) !(XI E(O) Q(O-1) ~ ( 1 - 2 ) ~ ( 2 - 3 ) ~(3-11 ~ ( 4 - 5 ) l(1) l(2) 1(3) 1(4) 1(5) Interpolation parameters T(I) T(1) 70 ) T(3) .I9922 .97869 ,50836 .I8883 .94452 .49894 .I7884 .91124 .48968 .I6932 .87950 .48074 .I601 1 .84808 .47186 .15 132 .8 1795 .46322 .I4291 .78877 .45481 13486 .76053 .44659 .I271 2 .73285 .43843 .I1970 .70597 .43044 Single Year Mortality Under Age 5 South Asian Pattern - Both sexes combined Life exp Probability of dying between ages X and Y Survivors at age X at birth QOC-Y) Io() E(O) Q(0-1) Q(1-2) Q(2-3) Q(3-4) Q(4-9 l(1) l(2) l(3) l(4) l(5) Single Year Mortality Under Age 5 Far Eastern Pattern - Males Life exp Probability of dying between ages X and Y at birth Q(X-Y) - ~ ( 0 ) ~ ( 0 - 1 ) ~ ( 1 - 2 ) ~ ( 2 - 3 ) ~ ( 3 - 4 ) ~(4-5) 1(1) Su~ivors at age X I(X) Interpolotian parameters T(I) T(1) T(2) T(3) Single Year Mortality Under Age 5 Far Eastern Pattern - Females Life exp Probability of dying between ages X and Y Survivors at age X at birth Q(X-Y) I(X) E(O) a(o-1) Q(I-2) a(2-3) ~ ( 3 - 4 ) a(4-a I(I) 1(2) 1(3) ~ ( s ) r(5) Interpolation parameters T(I) T(1) T(2) T(3) Single Year Mortality Under Age 5 Life .xp at birth E(0) Far Eastern Pattern - Both sexes combined Probability of dying between ages X and Y Q(X-Y) Survivors at age X 100 1(2) 1(3) ~ 4 ) 80106 77706 76128 80810 78527 77024 81498 79326 77895 82170 80106 78746 82825 80866 79574 8 6 8160 80383 84095 182335i 8 1 172 84708 83043 81942 85309 83735 82695 85895 84410 83427 Single Year Mortality Under Age 5 General Pattern - Males Life exp Probability of dying between ages X and Y at birth Q(X-Y) E(O) a(o-1) a(r-2) Q(2-3) ~ ( 3 - 4 ) Survivors at age X I(X) Interpolation parameters T(I) Single Year Mortality Under Age 5 General Pattern - Females Life exp Probability of dying between ages X and Y Survivors at age X at birth Q(X-Y) I()o E(0) Q(0-1) Q(1-2) Q ( 2 4 0 0 - 4 ) Q(4-9 l(1) l(2) l(3) 1(4) J(5) Interpolation parameters T(I) Single Year Mortality Under Age 5 General Pattern - Both sexes combined Life exp at birth Probability of dying between ages X and Y Q(X-Y ) Survivors at age X I(X)
Language:English
Score: 745240.7 - https://www.un.org/en/developm...ls/model/lifetables/annex2.pdf
Data Source: un