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18/02/2025

Demography is the statistical study of human populations, including their size, structure, distribution, and changes due to birth, death, migration, and aging. It is essential for various fields, including economics, healthcare, and policymaking.

Key Reasons Why Demography is Important

1. Policy Planning & Development

Helps governments and organizations plan for healthcare, education, housing, and infrastructure.

Assists in setting employment and social security policies based on population trends.

2. Economic Growth & Labor Market

Provides insights into the working-age population and future workforce.

Helps businesses understand consumer demand and market potential.

3. Healthcare & Public Health

Identifies trends in birth rates, death rates, and life expectancy for healthcare planning.

Aids in the allocation of resources to combat diseases, maternal health issues, and aging-related challenges.

4. Urban Planning & Infrastructure

Guides the development of cities, transportation, and utilities based on population growth and migration patterns.

Helps prevent overcrowding and resource depletion.

5. Social Welfare & Aging Population

Helps governments plan for pensions, elderly care, and social security.

Addresses issues related to dependency ratios (ratio of working-age people to dependents).

6. Migration & Population Distribution

Studies internal and international migration for labor policies and regional development.

Helps manage immigration, refugee crises, and urban-rural migration.

7. Environmental & Resource Management

Assesses the impact of population growth on natural resources and the environment.

Supports sustainable development policies.

8. Epidemiology & Disease Control

Tracks population density and movement to predict and control disease outbreaks.

Helps in the planning of vaccination programs and healthcare facilities.

16/02/2025

Infant mortality rate
Infant Mortality Rate (IMR) is the number of infant deaths (under 1 year old) per 1,000 live births in a given year. It indicates a country's healthcare quality and living conditions.

15/02/2025

LIFE TABLE
A life table in demography is a chart that shows how long people in a population are expected to live at different ages. It helps estimate survival rates, life expectancy, and death probabilities for different age groups.

14/02/2025

Total fertility rate
Total Fertility Rate (TFR) is the average number of children a woman is expected to have in her lifetime.

Difference between Net reproduction rate Vs Gross reproduction rate
13/02/2025

Difference between Net reproduction rate Vs Gross reproduction rate

Crude death rate Vs standardized death rate
12/02/2025

Crude death rate Vs standardized death rate

12/02/2025

Demography Vs population studies
Demography is a subset of Population Studies, focusing on numerical aspects.

Population Studies includes demographic data but also examines broader causes and effects of population changes.

11/02/2025

Demography:
Demography is the statistical study of human populations, including their size, structure, distribution, and changes over time due to birth, death, migration, and aging. It helps analyze trends and patterns that impact societies, economies, and policies.

10/02/2025

The product of prior distribution and likelihood function is called posterior distribution

09/02/2025

The parameters of prior distribution are called hyperparameter

09/02/2025

Types of Non informative priors
1. Uniform Prior – Assumes all values of the parameter are equally likely.

2. Jeffreys Prior – Based on Fisher Information, ensuring invariance under reparameterization.

3. Reference Prior – Maximizes information gain from data, often problem-specific.

4. Improper Prior – Does not integrate to 1 but can still yield valid posteriors.

5. Scale-Invariant Prior – Assigns equal probability to all scales of a parameter.

09/02/2025

Difference between informative and non informative priors::
Informative priors: When you have reliable prior knowledge and want to incorporate it into the analysis.

Non-informative priors: When you want the data to dominate the inference, especially in situations where prior knowledge is weak or unavailable.

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