PPT-From raw data to easily understood gender statistics

Haoyi Chen Social and Housing Statistics Section United Nations Statistics Division Sex a biological individual characteristic recorded during data collection in censuses surveys or administrative sources. Haoyi. Chen. Social . and Housing Statistics Section. United Nations Statistics Division. Outline. Global Gender Statistics . Programme. Overview. Main areas of work. EDGE project. Global Gender Statistics Programme. Session . 7. Dissemination and Communication. of Gender Statistics. S. erena . Lake Kivu Hotel. , . Rubavu. District. March 25-27, 2014. 1. Learning Objectives. At the completion of the module, participants should understand:. You have been taken advantage of. Now is the time to take action. If your audience is emotionally invested and engaged with your _____________________________________________________________________ Analysis. Data Management. Hypotheses. Goal. Get Data. Predict whom survived the Titanic Disaster. Woman and Children First. Read dataset into Excel, R, . etc. Some Age Missing Data, Analyze Gender Only. Owen Abbott. Office for National Statistics. ONS . Big Data . team. Launched in January 2014. The Big Data team:. focus on high priority ONS challenges that may be solved through new forms of data or the application of data science techniques to help deliver better statistics. 2018 WHM conference . Dr. Larry Lesser, Professor, Dept. of Mathematical Sciences (Lesser@utep.edu). my related dissemination. : . Mathematics Teacher . op-ed & letter; . b. ook chapter on persisting through statistics anxiety; JSM paper on . Office set up (law, . nso. system, “formal” . nss. Statistics compilation . Gender statistics. (Past) good practices. Present practice . What can be improved. FSM front. Development partners front. Session outline. Health and statistics. Gender and health. Data sources. Understanding key gender indicators. Practical exercise. Some determinants of health. :. Sex. (biology). Women need access to health care during pregnancy. the . Istat. experience. Workshop on Gender Statistics, Moldova, 3 - 4 November 2015. Maria Giuseppina Muratore. An important instrument to know what women and men have. different roles in society. August . 2014. . Maha . Muna, Gender Adviser. UNFPA . Pacific Sub-Regional Office. Global – Regional – Country. What does the national VAW Prevalence data tell us beyond Headline Statistics?. FAO & Gender. 2 main . areas of work in which the effort of mainstreaming gender is taking place. :. food . security . statistics. agricultural . statistics. . Food Security area – output 1. Sex-disaggregated . Learning Objectives. 1. Become familiar with . the concepts of data users, data producers and statistical production processes. Understand the importance of aligning the needs of users with the supply of gender data. Barbara . Cobo. Coordinator of Population and Social Indicators Division. IBGE - Brazilian Institute of Statistics and Geography. 9th Meeting of the Inter-agency and Expert Group on Gender Statistics . Ghana. 9. th. Meeting of the Inter-agency and Expert Group on . Gende. r. . 29-30 October 2015. Bangkok, Thailand. Ernestina Hope . Turkson. Senior. . Statistician. Ghana Statistical Service. 2. nd.