医学统计学ppt英文课件CH01introduction.ppt

Postgraduate books recommended by Degree Management and Postgraduate Education Bureau, Ministry of EducationMedical Statistics(the 2nd edition) Arrangement: total 72 class hours, two classes each week chapter 1 Introduction1. Key definitions2. the steps for medical statistics3. Brief history of StatisticsStatisticsThe science for data collection, sorting, and analysis. 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 StatisticsLearning objectives：1、Basic principles and methods of Statistics （Learning Emphasis） 2、Application Statistics(Clinical Medicine, Preventive Medicine, and Health Care Management) Medical StatisticsPurpose：a tool for medical researchEmphasis: statistical indicators used for calculating or comparing the quantitative characteristics of populationExample: health expectation infant mortalityMedical StatisticsSection 1. Key definitions variable, individual, sample and population 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. Variable value：the value of variablesExamples: height 1.65 meters weight 52 kggender female blood type “O”laboratory test negative treatment effect betterData: composed of a lot of variable values. Example: Data for blood glucose 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 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.populationdefinition：the set of variable values of some individuals sampled from the population at random.Example: the heights of 200 boys at 7 from Changsha.sampleSampling studySample information(statistic)Population characteristics（parameter）inferencenote：sampling is only the way to get information, inferring the population is our purpose、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 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. 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。 Data transformationQuantitative data ranked data（multiple）binomial dataexample：WBC（1/m3）count of five persons： 3000 6000 5000 8000 12000 quantitative variablelower normal normal normal higher qualitative variable Binomial data: normal 3 persons; abnormal 2 personsMultiple category data: lower 1 person; normal; 3 persons; higher 1 person errordefinition：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 contents. 2. Nonrandom error is divided into systematic error and non systematic error:Systematic error: it is produced in experiment and keeps constant or changes according certain rules. Usually, its reasons are known and controllable. Nonsystematic error（gross error）: it is always caused by obvious grosses. 、frequency and probability 1Frequency Given the same condition, repeat a trial for n times independently. Among n trials, A appears for m times，so the ratio of m/n is called the frequency of random event A among n trials. (A)Pm n 2probability: the likelihood of random events. Given the same condition, repeat a trial for n times independently. Among n trials, A appears for times，so the ratio of is called the frequency of random event A. As n increases gradually, the frequency will approach a cons