104 BIOSTATISTICS AND COMPUTER APPLICATIONS
Unit I
1. Introduction to Biostatistics, Common terms, notions and Applications
2. Statistical population and Sampling Methods
3. Classification and tabulation of Data
4. Diagrammatic and graphical presentation
5. Frequency Distribution, Measures of central value
6. Measures of variability; Standard deviation, standard Error, Range, Mean Deviation,
Coefficient of variation, Analysis of variance
Unit II
1. Basic tests, Test of significance; t-test, chi-square test.
2. Regression; Basic of regression, regression analysis, Estimation, Testing, prediction,
checking and residual analysis.
3. Multivariate Analysis.
4. Design of Experiments, randomization, replication, local control, complimentary
Randomized, randomized block design
Unit III
1. Factor Analysis.
2. Path analysis
3. Introduction to data mining
4. Virtuous Cycle.
Unit IV
1. Classification and Discriminant Analysis Tools: CART, Random forests,
2. Fisher's discriminant functions.
3. Neural networks.
4. Multilayer perception, predictive ANN model building using back propagation
algorithm, exploratory data analysis.
Unit V
1. Introduction to computer basics, concept of hardware windows XP and LINUX
2. Concept of file, folders, directories and their management by windows XP and LINUX
3. Office applications : MS- Office, MS- Word, MS- Excel and MS- PowerPoint
4. Open Office on Linux: Word Processor, spread sheets, Impress
5. Statistical Packages: Sigma plot etc.
Reference Books:
1. An Introduction to computational Biochemistry by C Stan T Sai
2. Statistics for Agricultural Sciences by Nageswara Rao
3. Fundamental of Statistics by Goon et al 1962
4. Methods in Biostatistics by B.K. Mahajan
5. Statistical Methods by S P Gupta
6. Statistical Methods by G W Snedecor and W G Cochran
7. Fundamental of Artificial Neural Networks, Prentice-Hall of India, N.Delhi
105 Lab Course: I
Consists of Practical Exercises listed out under 101 & 102
106 Lab Course: II
Consists of Practical Exercises listed out under 103 & 104
