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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA

“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA. DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division www.medinfo.umft.ro/dim 2004 / 2005. BIOSTATISTICS STATISTICAL PARAMETERS. COURSE 3. 1. STATISTICAL INFERENCE. 1.1. GENERAL CONCEPTS

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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA

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  1. “Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division www.medinfo.umft.ro/dim 2004 / 2005

  2. BIOSTATISTICSSTATISTICAL PARAMETERS COURSE 3

  3. 1. STATISTICAL INFERENCE • 1.1. GENERAL CONCEPTS • a) population, individual • b) definition: Biostatistics = science of estimating population characteristics and comparing populations • c) methods: • census - all individuals; the same time • screening - large number; selection criteria • sampling - subset of population

  4. d) STATISTICAL INFERENCE • EXTENDING PROPERTIES COMPUTED FOR A SAMPLE TO A POPULATION • e) REPRESENTATIVE SAMPLE • CRITERIA: • EQUIPROPBABILITY • INDEPENDENCE • f) SELECTION METHODS • SIMPLE SELECTION • RANDOM NUMBERS ASSOCIATED • MULTIPLE LAYER SELECTION • MIXED SELECTION • CLUSTERS

  5. 1.2. VARIABLES • a) DEFINITION: • a population characteristic which is studied and measured on all sampled individuals • b) STARTING A STUDY • variable selection • measurement accuracy • sample size • c) TYPES OF VARIABLES: • NUMERICAL • ORDINAL (rank) • NOMINAL (qualitative)

  6. Accuracy – PrecisionAccuracy: how close to real valuePrecision: reproductibility Ac.~ok, Pr. low Ac. low, Pr. high

  7. 1.3. DATA COLLECTION • tables • graphs: • histograms • pies • lines • scattergrams • maps

  8. 2. STATISTICAL PARAMETERS • 2.1. EXAMPLE: • study of children development • population: 10 years old children, from Timisoara, in 1996 • size: 400 children • collected data: height, in cm • accuracy : 1 cm • data table and histogram

  9. 2.2. TABLE &HISTOGRAM

  10. Conclusions: • extreme values - rarely • central values - more often • CENTRAL TENDENCY INDICATORS • variability • DISPERSION INDICATORS

  11. 2.3. CENTRAL TENDENCY INDICATORS a) ARITHMETIC MEAN:

  12. b) MEDIAN • THE VALUE DIVIDING THE SAMPLE INTO TWO EEQUAL PARTS • Ex: for odd or even number of elements • Recommended for ordinal variables • c) MODE • THE MOST FREQUENT • MODAL CLASS • UNI~, BI~ AND MULTIMODAL DISTRIBUTIONS • recommended for nominal variables

  13. d) RELATIVE POSITION • SYMMETRICAL DISTRIBUTIONS: • X = Me = Mo • ASSYMMETRICAL DISTRIBUTIONS (skewed distributions): • X = the most sensitive • Mo = least sensitive

  14. DISPERSION INDICATORS. Numerical variables Ordinal (rank) variables Qualitative variables (proportions)

  15. 2.4. DISPERSION PARAMETERS A For NUMERICAL VARIABLES • a) Standard deviation s • b) Variation coefficient

  16. c) NORMAL DISTRIBUTION (GAUSS BELL)

  17. d) Intervals with “s”

  18. EXAMPLE • A STUDY ON CHILDREN SOMATIC DEVELOPMENT • N = 25 children, age 10, Timisoara, 1997 • mean X = 137 cm • standard deviation s = 5 cm

  19. C. NOMINAL VARIABLES • Class Proportion (percent) pi = Ni / N (. 100) • Proportion standard deviation:

  20. 2.4. SKEWNESS • PEARSON’S • a = (Mo - x) / s • assimetry • 2.5. KURTOSIS • flatness

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