Biostatistics

Biostatistics

1. Descriptive statistics (Data summarization)

   • Methods of organizing, summarizing, and presenting data in an informative way usually for one variable (Univariate)
   • Graphical summarization
     •  - We look for the overall pattern and for striking deviations from that pattern. - Over all pattern usually described by shape, center, and spread of the data.- An individual value that falls outside the overall pattern is called an outlier. 
     • Bar charts and Pie charts are used for qualitative variables.
     • Histogram, stem and leaf and Box-plot and others are used for quantitative variables
   • Mathematical summarization
     • Measures of Central Tendency
       •   Measures which indicates where the middle of the data is. 
       • Mean
         •    Ithe average of the data.   The Population Mean is µ The Sample Mean is X bar
       • Median
       • Mode
       • To be continued
     • Measures of Dispersion
   • Numerical summarization
     • Numerical presentation of quantitative data
       • Frequency Distributions
         • Tabular
           • Simple
           • Complex
         • Charts and graphs
           • Histigram
             •   Frequently used to graphically present interval and ratio data 
           • Frequency Polygon
             • -  Another common method for graphically presenting interval and ratio data    - If the purpose of presenting is comparison with other distributions, the frequency polygon provides a good summary of the data 
           • Ogive
             •   A graph of a cumulative frequency distribution  
         • Frequency distribution / count
           •   presents the counts of observations grouped within pre-specified classes or groups 
         • Relative Frequency Distribution / proportion
           • presents the corresponding proportions of observations within the classes
         • Cumulative Frequency Distribution
           • the total of frequencies, that are distributed over different class intervals.

2. Inferential statistics

   •   The methods used to determine something about a population on the basis of a sample two or more variables (multivariate) 

3. Data

   •   - The raw material of Statistics is data, - Defined as figures ((Figures result from the process of counting or from taking a measurement)) or observation. 
   • Types of data according to sources
     • Primary data
       •   collected specifically for the analysis desired. 
       • Surveys
         •   if the data needed is about answering certain questions. 
         • Comprehensive
         • Sample
       • Experiments
         • If the data needed to answer a question are available only as the result of an experiment. 
     • Secondary data
       • Data that have already been compiled and are available for statistical analysis. 
       • Routinely kept records
         • Like hospital medical records contain huge amounts of information on patients. 
       • Literature or External sources
         •   The data needed to answer a question may already exist as: published reports, commercially available data banks, or the research literature. 
   • Constant
   • Variable
     •   It is a characteristic that takes different values in different persons, places, or things. 
     • Qualitative variable
       •   - are generally described by words or letters. - They are not as widely used as quantitative data because many numerical techniques do not apply to the qualitative data. 
       • Nominal (names)
         •   Gender, colour, name of villages 
       • Ordinal / Rank
         •   education levels Symptoms severity 
     • Quantitative variables
       •   are always numbers and are the result of counting or measuring attributes of a population. 
       • Discrete (counting)
         •   Children in family, Strokes on a golf hole 
       • Continuous (measuring)
         •   Amount of income tax paid, weight of a student
     • Dependent / Response
       •   The variable of primary interest (e.g. blood pressure in an antihypertensive drug trial). - Not controlled by the experimenter 
     • Independent/Predictor (ID)
       • Random variable
       • Covariate
         •   when not controlled. 
       • Factor
         •   - when controlled by experimenter. - It is often nominal (e.g. treatment)