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SYLLABUS

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, Experimental Design Technique, Plagiarism. Graphs: Histogram, Pie Chart, Cubic Graph, Response Surface Plot, Contour 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 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.

Unit-V

Design and Analysis of Experiments: Factorial Design: Definition, 22 , 23 Design. Advantage of Factorial Design. Response Surface Methodology: Central Composite Design, Historical Design, Optimization Techniques