Biostatics

Bio-Statistics : Definition of statistics It is the science that is concerned with collection, organization, summarization, and analysis of data; then drawing of inferences about a .body of data when only a part of data is observed Data .Are the raw material of statistics .Simply defined as numbers :Two main kinds of data .(Result from measurement (such as body weight– .(Result from counting (such as No. of patients discharged– .Each No. is called datum Sources of data .Routinely kept records. E.g.: hospital medical records .Surveys .Experiments .External sources. E.g.: published reports, data banks, research literatures :Definitions :Biostatistics A term used when the data analyzed are derived from biological sciences .and medicine :Variable The characteristic takes different values in different persons, places or things, so we label a characteristic as variable. E.g. : blood pressure, weight, .……height, hemoglobin Quantitative variable A variable that can be measured in the usual sense. E.g.: Weight of pre…… school children, age of patients 

Qualitative variable Can not be measured as the quantitative variable, e.g. ethnic group, possessing a characteristic or not such as smokers and non-smokers. Here .we use frequencies falling in each category of the variable Definitions

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: Random variable .Results only by chance factors i.e. can not be predicted 

Continuous random variable .Does not possess gaps. E.g. height and weight 

Discrete random variable Characterized by gaps or interruptions in the values that it can assume. E.g. .No. of admissions per day, or No. of missing teeth .(Categorical (e.g. sex and blood groups .(Numerical discrete (No. of episodes of angina Definitions :Note To summarize discrete variables we measure the proportion of individuals falling within each category. For continuous variables we need measures of .central tendency and measures of dispersion 

:Independent variable .Is a factor that we are interested to study. E.g. meat intake in grams per day 

:(Dependent variable (outcome variable Is the factor observed or measured for different categories of the .independent variable. E.g. hypercholesterolemia Definitions :Population The largest collection of entities for which we have an interest at a particular .time :Sample .Part of a population Random (probability) Sampling methods

 :Simple random sampling-1 .Use random number table .(see next slide( Systematic sampling: Include individuals at regular intervals. E.g. .2 .individuals No. 4, 7, 10, 13, …. Will be included

The interval in this example is (3(, measured by dividing the No. of the .population by the required sample. E.g. 60/20 .The starting point must be chosen randomly Stratified sampling: Divide into subgroups according to age and sex for .3 .example, then take random sample :Cluster sampling .4 It results from 2 stage process. The population is divided into clusters, .and a subset of the clusters is randomly selected .Clusters are commonly based on geographic areas or districts Convenient sampling Note: It is not always possible to take a random sample, e.g. a busy physician who wants to make a study on 50 patients attending the out-patient .(clinic. This is called a convenient sampling (non random Measurements scales The nominal scale: Consists of classifying the observations into various .mutually exclusive categories. E.g. males & females The ordinal scale: Observations are ranked according to some criterion, e.g. patients status on discharge from hospital (unimproved, improved, much .(improved Measurements scales The numerical scale .Sometimes called quantitative observations :There are two types of numerical scales .Interval or continuous scales e.g. age.1 .(Discrete scales (e.g. No. of pregnancies.2 Means and standard deviations are generally used to summarize the values .of numerical measures