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. (Chapters - 1,2,3,4) 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 t ype, 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. (Chapters - 5,6,7,8) Unit-III Non Parametric tests: Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis H test, Friedman Test Introduction to Research: Need for research, Need for design of Experiments, Experimental 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. (Chapters - 9,10,11,12) Unit-IV Blocking and confounding system for Two-level factorials Regression modeling: Hypothesis testing in Simple and Multiple regression models 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 (Chapters - 13,14,15) 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 (Chapters - 16,17)
