# Difference between revisions of "Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data"

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|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression). | |Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression). | ||

− | Access the Emory CASAS Cancer Survival Analysis Suite here: http://bbisr.shinyapps.winship.emory.edu/CASAS/ | + | <nowiki>***</nowiki> Access the Emory CASAS Cancer Survival Analysis Suite here: http://bbisr.shinyapps.winship.emory.edu/CASAS/ *** |

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## Revision as of 14:47, October 16, 2018

**October 16, 2018**

9:55 am - 10:40 am

- Presenter

- Abstract

Summary statistics such as the mortality rate, birth rate, or Total Fertility Rate can be useful for understanding some basic characteristics of a population. Often, however, we’re interested in having a more detailed understanding of how demographic events—fertility, mortality, or morbidity—relate to individual characteristics or environmental conditions. One may want to ask questions such as: Does education predict fertility, controlling for wealth? Does infant mortality vary with proximity to clean water, accounting for household-level differences? Does the interval between births depend on a parent’s age, economic strategy, or social network? This session will introduce a number of statistical models that are useful for answering these kinds of questions. We will discuss generalized regression models (customizable to model yes/no outcomes, count data, or continuous variables) as well as survival analysis (also called event history or duration analysis). These models allow us to estimate demographic rates as a function of multiple predictor variables, control for confounding variables, and take into account individual- or group-level heterogeneity.

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