{"title":"期望和方差的性质","authors":"","doi":"10.1002/9781119513469.app2","DOIUrl":null,"url":null,"abstract":"It is often referred to as the first moment of Y, since it describes the location of the center of the distribution. The precise definition of the expectation of Y is that it is a weighted average of all the possible values of Y, with weights determined by the probabilities associated with each possible value. The variance of Y, often denoted by o-2 = Var(Y), is a measure of the dispersion or variability around the mean or expected value of Y. The variance is often referred to as the second central moment of Y and is defined as","PeriodicalId":330426,"journal":{"name":"Applied Longitudinal Analysis","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Properties of Expectations and Variances\",\"authors\":\"\",\"doi\":\"10.1002/9781119513469.app2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is often referred to as the first moment of Y, since it describes the location of the center of the distribution. The precise definition of the expectation of Y is that it is a weighted average of all the possible values of Y, with weights determined by the probabilities associated with each possible value. The variance of Y, often denoted by o-2 = Var(Y), is a measure of the dispersion or variability around the mean or expected value of Y. The variance is often referred to as the second central moment of Y and is defined as\",\"PeriodicalId\":330426,\"journal\":{\"name\":\"Applied Longitudinal Analysis\",\"volume\":\"144 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Longitudinal Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119513469.app2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Longitudinal Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119513469.app2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
It is often referred to as the first moment of Y, since it describes the location of the center of the distribution. The precise definition of the expectation of Y is that it is a weighted average of all the possible values of Y, with weights determined by the probabilities associated with each possible value. The variance of Y, often denoted by o-2 = Var(Y), is a measure of the dispersion or variability around the mean or expected value of Y. The variance is often referred to as the second central moment of Y and is defined as