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Lecture for Year 1 Semester 1 medical student PPSD session on 13th September 2006 by Professor Omar Hasan Kasule Sr.
CONSTANTS AND VARIABLES
A constant has only one unvarying value under all circumstances for example p=pie and c = speed of light. Few things are constant in the universe. Most phenomena exhibit variation. To humans this variation is random because of ignorance of the under-lying deterministic order. A random variable is one whose value changes. It can be quantitative (with intrinsic numerical value) or qualitative (descriptive with no intrinsic numerical value).
QUANTITATIVE RANDOM VARIABLES
A random quantitative variable results when numerical values are assigned to results of measurement or counting. It is called a discrete random variable if the assignment is based on counting. It is called a continuous random variable if the numerical assignment is based on measurement.
The numerical continuous random variable can be expressed as fractions and decimals. The numerical discrete random variable can only be expressed as whole numbers.
Choice of the technique of statistical analysis depends on the type of variable. Each type of random variable corresponds to a statistical distribution used in statistical analysis. This relation is too advanced to be discussed in more detail in this elementary course.
QUALITATIVE RANDOM VARIABLES
Qualitative variables (nominal, ordinal, and ranked) are attribute or categorical with no intrinsic numerical value. The nominal has no ordering for example male or female. The ordinal has ordering for example first class, second class, third class. The ranked has observations arrayed in ascending or descending orders of magnitude for example 1st, 2nd, 3rd.
RANDOM VARIABLES: PROPERTIES
A random variable has 6 properties. The expectation or average of a random variable is a central value around which it hovers most of the time. The variations of the random variable around the average are measured by its variance. Covariance measures the co-variability of the two random variables. Correlation measures the linear relation between two random variables. Skew ness measures the bias of the distribution of the random variable from the center. Kurtosis measures the peaked ness of the random variable is at the point of its expectation.
RANDOM VARIABLES: MATHEMATICAL TRANSFORMATIONS
Quantitative variables can be transformed into qualitative ones. Qualitative variables can be transformed into quantitative ones but this is less desirable. The continuous variable can be transformed into the discrete variable. Transformation of the discrete into the continuous may be misleading.
Discussion
- Determine the type of each variable in the class data
- Determine the type of each variable in a clinical laboratory report