Biostatistics research type of statics and examples
- 1. By Hebatalla Abdelmaksoud Abdelmonsef MD Public Health and Community Medicine Department. Cairo University. lecturer of Public Health and Community Medicine. Kafr-elshiekh University. Introduction to statistics
- 2. Statistics Statistics is the science of collecting, classification, analyzing, presenting, and interpreting data to assist in making more effective decisions.
- 3. Biostatistics The science of biostatistics can be defined as the sub branch of statistics that can be applied in the field of medicine.
- 5. Types of Statistics Descriptive statistics: is concerned with the summary measures of data for a sample of a population. Inferential statistics: is concerned with the use of data from a sample of population to make inferences about the population. 5
- 6. Population Vs Sample variable Vs Value(data) Data Vs information Parameter Vs statistic
- 8. DATA Data are the basic building blocks of statistics and refer to the individual values measured or observed. Data are then interpreted to give “Information” 8
- 9. The Natural History of Disease Data Vs Information • Data (Body Temperature is 37o C.) • Information (Normal Body Temperature) 9
- 10. Data Vs Information Data Maternal Mortality Ratio showed a decrease from 174 per 100,000 live births in year 2000, to 68 per 100,000 live births in year 2005 Information Improved health services 10
- 11. Observations are classified into: Constant : These are observations which do not vary from one person to another such as number of eyes, fingers, ears… etc. Variables: These are observations, which vary from one person to another or from one group of members to others. The variables could be quantitative or qualitative. 11
- 12. What is Data what is variable Age Sex Illiterate 50 years old Smoker Stage 2 cancer Severity of disease
- 14. Data Vs information Body temperature is 37.2 Normal temperature Your blood pressure is 190/95 You are hypertensive
- 18. Example: A college dean is interested in learning about the average age of faculty members . Identify the basic terms in this situation.
- 19. TYPES OF Variable Qualitative (Categorical) variable Quantitative (Numerical) variable
- 20. When dealing with data, it is important to recognize the type of each data variable
- 21. When dealing with data, it is important to recognize the type of each data variable for the following reasons: • Summarizing data: describing a variable in mean with standard deviation or in frequency with percentage depends on the type of data variable. • Graphical presentation: choosing the proper graph to represent the data depends on the type of data variable. • Analyzing data: choosing the suitable statistical tests also depends on the type of data variables.
- 23. QUANTITATIVE VARIABLES 1. Continuous Quantitative Obtained by specific measurements A precise definition of the units of measurement must be specified Its values could be integer or fractionated value. 23 Continuous data are values on a continuous scale 0 0.01 0.02 ……1…1.1, 1.2…………1000
- 24. 1. Continuous Variables Examples: ⁻ Weight (in Kgm), ⁻ Height (in cms), ⁻ Hemoglobin level (g/dL), ⁻ Age (in years), ⁻ Income (in L.E.), ⁻ Volume of urine (in liters). 24
- 25. QUANTITATIVE VARIABLES 2. Discrete Quantitative Obtained by counting or enumeration Its value is always an integer value i.e. only certain values are possible - there are gaps between the possible values - could not be fractionated 25 0 1 2 3 4 5 6 7 Public Health & Community Medicine
- 26. 2. Discrete Variables Examples: ⁻ Pulse rate per minute ⁻ Number of children in a family ⁻ Number of live births ⁻ Number of abortions ⁻ Number of visits to a GP in a year 26 Public Health & Community Medicine
- 27. Constant data Variables QuaNtitative variables Types of data QuaLitative variables Continuous Discrete Ordinal Nominal 27
- 28. QUALITATIVE VARIABLES The variables are expressed as a description in quality and cannot be measured, but can be categorized only. The resulting data are merely counts of labels or categories. They can be ordinal or nominal 28
- 29. QuaLitative variables Ordinal Variables Categories are put in order: 1. Degree of success a. Excellent b. Very good c. Good d. Fair 2. Degree of a disease: a. Mild b. Moderate c. Severe Nominal Variables Categories are not be put in order: 1. Di-chotomous: (Two categories) ○ Sex (Male – Female) ○ Yes/no variables 2. Multi-chotomous: (More than 2) ○ Marital status: Single/ Engaged/ Married/ Divorced/ Widow) ○ Blood groups (A/B/AB/O) 29
- 30. Quantitative Quantitative Qualitative Qualitative Variable Quantitative Qualitative Age 11.5 years Child Temperature 37.9 0C Fever BMI 27.9 Kg/m2 Overweight How you collect your data will determine what you can do with it later on.
- 31. Whenever possible, collect your data at the highest level, numerical continuous or numerical discrete, as it is more accurate and can be categorized easily later on.
- 32. Quantitative Vs qualitative No of children in a family RBCs count Smoking status Education level Urine sugar Blood sugar
- 33. Sometimes, categorical variables are coded in numbers ?????
- 43. Summarising QuaNtitative Data Two parameters are needed to summarise quantitative data Representing the ‘middle’ of the data Representing the “spread” of the distribution of the data
- 45. Measures of Central Tendency Mode Mean Median Midrange
- 46. Measures of Dispersion Range Min and Max Standard deviation Percentiles, inter quartile range
- 47. The median is the point at the center of the data, where half of the values are above, and half are below it. The mean of a variable can be computed as the sum of the observed values divided by the number of observations.
- 50. Inter quartile range It is the difference between 75th and 25th percentile It contain the central 50% of the observation (50% of the observation lie between those numbers
- 52. Combining measures of central tendency and measures of dispersion