BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY (Theory)

Unit-I 

Introduction: Statistics, Biostatistics, Frequency distribution

Measures of central tendency: Mean, Median, Mode- Pharmaceutical examples

Measures of dispersion: Dispersion, Range, standard deviation, Pharmaceutical problems

Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlation – Pharmaceuticals examples

Unit-II          

Regression: Curve fitting by the method of least squares, fitting the lines y= a + bx and x

= a + by, Multiple regression, standard error of regression– Pharmaceutical Examples Probability:Definition of probability, Binomial distribution, Normal distribution, Poisson’s distribution, properties – problems

Sample, Population, large sample, small sample, Null hypothesis, alternative hypothesis, sampling, essence of sampling, types of sampling, Error-I type, Error-II type, Standard error of mean (SEM) – Pharmaceutical examples

Parametric test: t-test(Sample, Pooled or Unpaired and Paired) , ANOVA, (One way and Two way), Least Significance difference

Unit-III  

Non Parametric tests: Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis test, Friedman Test

Introduction to Research: Need for research, Need for design of Experiments, Experiential Design Technique, plagiarism

Graphs: Histogram, Pie Chart, Cubic Graph, response surface plot, Counter Plot graph Designing the methodology: Sample size determination and Power of a study, Report writing and presentation of data, Protocol, Cohorts    studies, Observational studies, Experimental studies, Designing clinical trial, various phases.

Unit-IV         

Blocking and confounding system for Two-level factorials

Regression modeling: Hypothesis testing in Simple and Multiple regressionmodels Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB^®, DESIGN OF EXPERIMENTS, R – Online Statistical Software’s to Industrial and Clinical trial approach

Unit-V    

Design and Analysis of experiments:

Factorial Design: Definition, 2², 2³design. Advantage of factorial design Response Surface methodology: Central composite design, Historical design, Optimization Techniques