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,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,单击此处编辑母版标题样式,*,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,Postgraduate books recommended by Degree Management and Postgraduate Education Bureau,Ministry of Education,Medical Statistics,(the 2nd edition),Postgraduate books recommended,Arrangement:total 72 class hours,two classes each week,Arrangement:total 72 class ho,chapter 1 Introduction,Key definitions,the steps for medical statistics,Brief history of Statistics,chapter 1 Introduction,Statistics,The science for data collection,sorting,and analysis.,Statistics,Definition,：,the science that study the collection,sorting and analysis of medical data.,Characteristics,：,1,、,Using the quantity to reflect the quality 2,、,Using chance events(uncertainty)to reflect the inevitability(rules),Medical Statistics,Definition：the sc,Learning objectives,：,1,、,Basic principles and methods of Statistics,（,Learning Emphasis,）,2,、,Application Statistics(Clinical Medicine,Preventive Medicine,and Health Care Management),Medical Statistics,Learning objectives：Medical St,Purpose,：,a tool for medical research,Emphasis:,statistical indicators used for calculating or comparing the quantitative characteristics of population,Example,:health expectation,infant mortality,Medical Statistics,Purpose：a tool for medical res,Section 1.Key definitions,Section 1.Key definitions,variable,individual,sample and population,variable,individual,sample,individual,（,observatory unit,）：,the basic unit in statistical research,it depends on the purpose.,variable,（,indicator,）：,individual characteristics,examples,:height,、,weight,、,gender,、,blood type,、,treatment effect etc.,individual（observatory unit,Variable value,：,the value of variables,Examples:,height 1.65 meters weight 52 kg,gender female blood type “O”,laboratory test negative,treatment effect better,Data:,composed of a lot of variable values.,Example:,Data for blood glucose,Variable value：the value of va,homogeneity,：,common characteristics for the given individuals,example,：,the heights of the boys with the age of 7 living in Changsha 2004,variation:,difference existing among the variable values of homogeneity individuals,example:the different heights of the boys with the age of 7 living in Changsha 2004,homogeneity：common charact,Definition,：,the whole homogeneity individuals determined by specific purpose.,example,：,all the heights of boys at 7 that lived in Changsha 2004,finite population,：,the space,time and population for a specific population have been limited.,infinite population,：,no time and space limits for the population.Such populations only exist in imagination,so it is called infinite population.,population,Definition：the whole homog,definition,：,the set of variable values of some individuals sampled from the population at random.,Example:the heights of 200 boys at 7 from Changsha.,sample,definition：the set of variable,Sampling study,Sample information,(statistic),Population characteristics,（,parameter,）,inference,note,：,sampling is only the way to get information,inferring the population is our purpose,Sampling studySample informati,、,variable and data,、variable and data,measurement data,:it is also called as quantitative or numerical data.Its value is quantitative.Measurement data always has measurement units.,example,：,height data,weight data,measurement data:it is als,enumeration data,:qualitative or count data.For such data,it needs to classify the observation units before and count them.Its value appear different characteristics and sorts.,Binomial:gender,live or death,yes or no.,Multiple,：,blood type,A,、,B,、,O,、,AB.,enumeration data:qualitativ,ranked data,:ordinal or semi-quantitative data.It need to classify observatory units into different classes according the extent before calculate the frequencies of each groups.There exists obvious differences among different classes.,example:to evaluate the treatment effect of one drug on heart failure,we use the indicator(cured,better,worsen,dead)to assess the treatment effect.,Choosing of statistical methods depends on the data type to a great extent,。,ranked data:ordinal or se,Data transformation,Quantitative data,ranked data,（,multiple,）,binomial data,Data transformationQuantitativ,example,：,WBC,（,1/m,3,）,count of five persons,：,3000 6000 5000 8000 12000 quantitative variable,lower normal normal normal higher qualitative variable,Binomial data,:normal 3 persons;abnormal 2 persons,Multiple category data,:lower 1 person;normal;3 persons;higher 1 person,example：WBC（1/m3）count of five,error,error,definition,：,the difference between measurement value and true value.,1,、,rand error,：,unstable and changing at random,errors that caused by uncontrolled factors.Commonly,rand errors are referred to those errors appearing during repeated measurements and sampling.,Often,measurement error is extremely lower than sampling error.In Statistics,sampling error is the main study co
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