Biostatistics

General Foundation

After completing this course, the students will be able: - to apply the appropriate statistical techniques for clustering and cumulative expression of sampling data, - to formulate the appropriate research/statistical hypotheses concerning a specific research or animal health problem, - to apply the appropriate methods for the statistical analysis of sampling data and hypothesis testing, - to use the SPSS software for performing various methods of statistical description and anal-ysis of sampling data, - to assess inductively the findings of a survey aimed at rational decisions conserning practical problems in the veterinary field, - to understand the importance of sampling and the role of statistical inference in veterinary research.

Biostatistics (Theoretical lectures 18 hours) Teaching staff: Christos Batzios, Alexandros Theodoridis This course introduces concepts that will provide the student with a solid theoretical and empirical background for developing skills regarding the use of quantitative methods of Biostatistics for the collection, the presentatiuon, the analysis and the evaluation of sampling data. Emphasis is given on the empirical application of Biostatistics and on the statistical assessment and interpretation of bio-medical and agricultural data in order to support rational decision making regarding speifc problems in research and animal heath care. 1st hour Introduction to Statistics • The nature of Statistics • The use of computers in statistical analysis • Populations and samples • Basic statistical terms (variable, observation, populations, samples, etc.) 2nd hour Presentation and classification of statistical data • Statistical tables and charts • Frequency distributions (continuous, discrete and qualitative variables) • Graphical presentation of frequency distributions 3rd-5th hour Statistical measures • Basic measures of central tendency (arithmetical mean, weighted mean, median, mode, geomet-rical mean, quartiles, etc.) • Selection of the appropriate measure of central tendency • Measures of dispersion (range, interquartile range, mean absolute deviation, variance, standard de-viation, coefficient of variation, Tchebysheff’s theorem, empirical rule) • The effect of simple transformations on mean and variance • Measures of skewness, measures of kurtosis 6th hour Elements of probability theory, random variables • Statistical experiment, test, events etc. • The meaning of probability (classical definition of probability, definition of probability as limit of relative frequency, definition of subjective probability, axiomatic definition of probability) • Calculation of probability, basic theorems of probability, probability rules (multiplication rule, ad-dition rule, Bayes theorem) • Random variables and probability distribution (Discrete and continuous random variables and probability distributions) 7th-9th hour Theoretical distributions • Discrete theoretical distributions (Binomial distribution, Poisson distribution) • Continuous theoretical distributions (normal distribution, standard normal distribution Z, chi-squared distribution, t distribution, F distribution) 10th-11th hour Sampling (methods,distributions) • Principles of sampling (random and directed sampling) • Sampling distributions of the mean, of the proportion, of the difference between two means, of the variance, etc • The central limit theorem 12th hour Estimation • Point and interval estimation • Confidence interval of the mean, of the variance, of the difference between two means, of the proportion, etc • Sampling errors • Determination of sample size 13th-14th hour Hypothesis testing • Statistical hypotheses • Hypothesis test and errors • Hypothesis test for a (population) mean and the difference between two means • Hypothesis test of the variance and the ratio of two variances • Hypothesis test of the proportion and the difference between two proportions 15th hour Analysis of frequencies • Test of goodness-of-fit • Test of independence • Test of homogeneity 16th hour General principles of analysis of variance • One-way analysis of variance • The completely randomized design, multiple comparisons tests • Hypotheses in analysis of variance 17th hour Non-parametric hypothesis tests • Test of goodness-of-fit (K-S) • Tests for two samples, tests for k samples • Transformations and normality, etc 18th hour Simple regression and correlation • Least squares method • Interpretation of the regression equation • Linear correlation Exercises - Laboratories (16 hours) 1-2 hours • Construction of distribution tables with data classification for continuous and discrete variables of Veterinary Science interest. • Methods of graphical presentation of frequency distributions (histograms, polygonal lines etc) 3-5 hours • Examples of calculation of descriptive measures of central tendency. • Choice of appropriate measure of central tendency. • Applications of calculation of statistical measures of dispersion, skewness and kurtosis 6-8 hours • Calculation of probability. • Examples of use of tables of theoretical distributions (Binomial distribution, Poisson distribution, Z distribution, t distribution, chi-squared distribution, F distribution). • Examples of probability calculation and sampling error when sampling with and without replace-ment. Applications of the central limit theorem. • Calculation of confidence interval of the mean, the variance, the difference between two means, the proportions etc 9-11 hours • Problems of sample size calculation when the objective is the estimation of the mean or the propor-tion in simple random and stratified random sampling. • Problems of hypothesis testing for the mean, the difference between two means, the proportion, the difference between two proportions, the variance, the ratio of two variances, etc.etc. 12-13 hours • Problems of testing goodness-of-fit. • Analysis of frequencies classified in tables 2x2, 2xc, rxc with the application of test for independ-ence and homogeneity 14-16 hours • Databases for agricultural research. Retrieving data from FAO databases Agriculture/ Fisher-ies/etc). • Use of statistical package SPSS for descriptive and inferential analysis of experimental data, esti-mation and interpretation of regression equations, analysis of variance and non-parametric analysis, with emphasis given on interpretation of the results.

Biostatistics, descriptive statistics, sampling, hypothesis testing, statistical inference.

19-11-2020