Course

Unit 1:  

Data Representations and Analysis

Collection, Classification and Tabulation of data, Bar diagrams and Pie diagrams, Histogram, Frequency curve and frequency polygon, Ogives. 

Unit 2:  

Measures of Central Tendency and Dispersion

Correlation and Regression analysis: Correlations and regressions-: Relation between two variables, scatter diagram, definition of correlations, two regression lines, Karl Pearson’s coefficient of correlation, Rank correlation, Tied ranks. 

Unit 3:

Statistical Averages

Mean, median, mode, Standard deviation, curve fitting, principles of least squares,

Unit 4:  

Probability

Probability theory: Random experiments, sample space, probability theory, conditional probability. Baye’s theorem. 

Unit 5:

Random variable

Random variable,(.discrete and continuous), Probability density function(discrete and continuous), Distribution function for discrete random variable. Distribution function for continuous random variable, Joint probability distribution, Conditional and marginal distribution. Mathematical expectations: Introduction, the expected value of random variable, moments, Moment generating functions, Product moments, Conditional expectations. Standard distributions -: Uniform distribution. (Discrete and continuous). Exponential distribution, Gamma distribution, Beta distribution. Binomial distribution, Poisson distribution, Normal distributions. Standard normal distributions. 

LEARNING OBJECTIVES:

Biostatistics is a course offered to 3rd semester B.Sc., (BT &MB). We have considered distributions relating to a single characteristic. How far the two variables, corresponding to two characteristics, tend to move together in same or opposite directions. The theory of probability is a study of Statistical or Random experiments. Using these figures, it might be possible to estimate the possible level of prices at some future data so that some policy measures can be suggested to tackle the problems. Average is a value which is typical or representative of a set of data.

COURSE OUTCOMES:

After completing the course, students shall be able to  

CO1: Describe statistical methods and probability distribution relevant for molecular biology data. 

CO2: Know the application and limitations of different bioinformatics and statistical methods. 

CO3: Perform and interpret bioinformatics and statistical analyses with real molecular biology data. 

CO4: Apply descriptive techniques commonly used to summarize public health data. 

CO5: Demonstrate basic analytical techniques to generate results

CO6: Apply statistical knowledge to design and conduct research studies.

REFERENCES:  

3. Fundamentals of Biostatistics. by Irfan A Khan- 2004.
4. An introduction to Biostatistics. by P.S.S. Sunder Rao, 5^th Edition , 2012
5. Ravichandran, “Probability and Statistics for Engineers”, Revised Edition 2012, Wiley India.
6. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying Ye, Probability and Statistics for Engineers and Scientists, 8th Edition, Pearson Education Asia, 2007.