Course:  B Pharmacy          Semester: 8th   /4th Year    
Subject Code: BP801T
Unit Topic (Contents) Learning Objective ( By the end of lesson should be able to) Teaching Guidelines


Methodology Time (Hrs)
1 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


By the end of lesson the student should be able to understand and explain Introduction of biostatics ·         Statistics

ü  Introduction


·         Measures of central tendency:

ü  Mean

ü  Median

ü  Mode

ü  Pharmaceutical problems

·         Measures of dispersion:

ü  Dispersion

ü  Range

ü  Standard Deviation

ü  Pharmaceutical Problems


·         Correlation

ü  Definition

ü  Karl Pearson’s coefficient of    correlation

ü  Multiple correlation

ü  Pharmaceutical problems

ü  Didactic Lecture


ü  Problem base assignments



2 Hours 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



Upon completion of this unit the student should be able to understand and explain Hours regression and parametric test ·         Regression

ü  Definition

ü  Curve Fitting method of least square

ü  Multiple Regression

ü  Standard error of regression

ü  How to calculate Standard error regression

ü  Pharmaceutical problems


·         Probability

ü  Definition

ü  Binomial distribution

ü  Normal distribution

ü  Poisson’s distribution

ü  Sample

ü  Population

ü  large sample

ü  small sample

ü  Null hypothesis

ü  Alternative hypothesis Sampling

ü  Types of sampling

ü  Error-I type

ü  Error-II type

ü  Standard error of mean (SEM)

ü   Pharmaceutical examples


Parametric test

ü  t-test(Sample, Pooled or Unpaired and Paired) ,

ü  ANOVA, (One way and Two way),

ü  Pharmaceutical calculations


ü  Instructive

ü  Case history


ü  Problem based power point presentations


ü  Problem based assignments

3 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

to understand and explain Nan parametric tests and introduction to research methodology ·         Non Parametric tests:


ü  Wilcoxon Rank Sum Test

ü  Mann-Whitney U test

ü  Kruskal-Wallis test

ü  Friedman Test


·         Research

ü  Introduction

ü  Need of research

ü  Experimental design

ü  Plagiarism


·         Graphs

How to make histogram, Pie Chart, Cubic Graph, response surface plot, Counter Plot graph



·         Designing the methodology

ü  Sample size Determination

ü  Report writing

ü  Presentation of data

ü  Observational studies

ü  Experimental studies

ü  Designing clinical trial

ü  Various phase

ü  Preclinical phase

ü  Phase I

ü  Phase II

ü  Phase III

ü  Phase IV

Didactic lectures through power point presentations






Problem based assignments

4 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


the student should be able to explain regression modeling


Explain statistical softwares

·         To cover Blocking and confounding system for Two-level factorials

·         Regression modeling

ü  Hypothesis testing

ü  Multiple regression Models

·         Practical components of Industrial softwares



Didactic lectures


Power point presentations


Online statistical software handling presentations

5 Design and Analysis of experiments:


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


able to  explain design and analysis of experimentations ·         Factorial Design

ü  Definition

ü  l22, 23design

ü  Advantage of factorial design


·         Response Surface methodology:

ü  Centra composite design,

ü  Historical design,

ü  Optimization Techniques


Power point presentations