Biostatistic Notes PDF

What is STATISTICS? Statistics is a useful piece of information that summarizes data. It tells us something that we need to know They are simpli cations and all simpli cations leave out something valuable Descriptive statistics is about collecting, looking and analyzing the data wher...

What is STATISTICS? Statistics is a useful piece of information that summarizes data. It tells us something that we need to know They are simpli cations and all simpli cations leave out something valuable Descriptive statistics is about collecting, looking and analyzing the data whereas inferential statistics is about drawing conclusions from the data to the whole populations What is BIOSTATISTICS? When the data being analyzed are derived from the biological sciences and medicine, we use the term biostatistics. A parameter is a characteristic of the population. A statistic is a characteristic of the sample. Data and Variables Data are often discussed in terms of variables. A variable is: Any characteristic that varies from one member of a population to another. Ex. height in centimeters, which varies from person to person. Grouped Data: Age categories —-> 20-29, 50-59, =>60 Ungrouped Data: Age numerical —-> 25, 26, 48, 57, 61, 42, 28 fi fi Continuous Data: mistad Quantitative data that is measured Can include any value within a range Represented graphically by histograms - Examples: height, weight, age, blood pressure, temperature, glucose level Discrete Data: rillund Quantitative data that is counted Contains distinct or separate values Represented graphically by bar graphs Examples: number of needle punctures, number of pregnancies, number of hospitalizations Ordinal Data: Can be rank ordered Categories can be ranked above or below each other Limited mathematical calculations can be performed Examples: satisfaction levels, academic grades Nominal Data: Cannot be rank ordered Categories are just different, not placed in any order No mathematical operations can be performed Re ects qualitative differences rather than quantitative ones Must have mutually exclusive and exhaustive categories Examples: educational programs, gender Must determine the Dependent Variable (DV) and Independent Variable (IV) ALL UNMODIFIED VARIABLES ARE INDEPENDENT VARIABLES ex. Age, Gender, Race, Ethnicity fl Independent Variables: Located on the x-axis of a graph Stands alone Happens rst Affects the value of the dependent variable Dependent Variables: Located on the y-axis of a graph Usually the "y" in a table or equation Depends on the independent variable Happens second Is a function whose value is determined by the independent variable Is tested Primary Data: Collected directly by the researcher Advantage: The researcher can collect data that speci cally ts their research objectives Secondary Data: Collected by others, such as: Ministry of Health (MOH) & Governmental organizations & International organizations fi fi 1. Pie Chart (categorical) - Shows differences in frequencies or percentages among categories of nominal or ordinal variables - Displayed as segments of a circle adding up to 100% 2. Bar Graph (categorical) - Displays differences in frequencies or percentages among categories - Categories shown as rectangles of equal width with height proportional to frequency/ percentage 3. Stacked Bar Graph - Variation of bar graph showing proportions within categories 4. Statistical Map (categorical) - Useful for describing geographical variations in variables - Requires software like Geographic Information System (GIS) 5. Histogram (numerical) - Shows differences in frequencies or percentages for numerical variables - Displayed as contiguous bars 6. Frequency Polygon (numerical) - Points representing frequencies of each category are placed above midpoint and joined by straight lines 7. Time Series Charts (numerical) - Displays changes in variables at different points in time - Time on horizontal axis, frequencies/percentages on vertical axis