Syllabus

Introduction: Need of statistics for Bioengineers Variables and Scales of Measurement Types and Sources of data Organization and Classification of data (Frequency distribution) Representation of data (Tabulation, Graph and, Diagram). Probability - the basis of statistical inference, Bayes Theorem, Screening tests, sensitivity, specificity and Predictive value positive and negative. Descriptive statistics: Measures of central tendency, Measures of dispersion Probability Distributions (Binomial, Poisson, Normal) Asymmetric Distributions and Measure of shape (Skewness and Kurtosis) Inferential statistics (Hypothesis testing): Basic concepts and steps Type I and type II errors Z-tests T-test: Dependent t-test, independent t-test Chi-square test, F-test ANOVA, ANCOVA, factorial ANOVA, repeated-measures designs, mixed design ANOVA, post hoc procedures Non-parametric, distribution-free tests: Sign test, Wilcoxon signed-rank test, Wilcoxon rank sum test, Mann-Whitney U test Kruskal-Wallis H test, Friedman's test, Spearman's rank correlation test. Testing normal distribution - Kolmogorov-Smimov test Testing homogeneity of variance - Levene's test Inferential statistics (Correlation and Regression): Bivariate correlation - Pearson's correlation coefficient, Spearman's correlation coefficient Partial correlation Regression - method of least squares, assessing goodness of fit multiple regression. Experimental design and clinical trials.

To understand the fundamental statistical tools.

To use various statistical tools for biomedical data analysis.

To formulate a hypothesis and arrive at a statistical inference.

Apply statistical knowledge to design and conduct research studies.
To estimate the value of various population parameters from a sample of data.

At the end of the course, the students will be able to:
1. Understand different statistical tools for biomedical data analysis.
2. Correctly choose and apply statistical techniques for solving problems.
3. Able to formulate the correct hypothesis/alternate hypothesis for the problems
4. Understand the power and weakness of statistics in deriving conclusion.
5. Apply statistical tools in experimental design and clinical trials.

• Le CT, Introductory Biostatistics, Wiley-Interscience, USA , ISBN: 0-471-41816-1

• Wayne W Daniel, Biostatistics - Basic concepts and methodology for Health Sciences, Wiley, USA , ISBN: 978-81-265-5189-7

• Geller NL, Advances in clinical trial biostatistics, Marcel Dekker Inc, USA , ISBN: 0-82479032-4

• Field A, Discovering statistics using SPSS, SAGE, USA , ISBN: 978-1-84787-936-6