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**• Know the operation of M.S. Excel, SPSS, R, and MINITAB ®, DoE (Design of Experiment)**

**• Know the various statistical techniques to solve statistical problems**

**• Appreciate statistical techniques in solving the problems.**

**To understand the applications of Biostatics in Pharmacy.****To deal with descriptive statistics, Graphics, Correlation, Regression, logistic regression Probability theory, Sampling technique, Parametric tests, Non Parametric tests, ANOVA,****Introduction to Design of Experiments, Phases of Clinical trials and Observational and Experimental studies, SPSS, R, and MINITAB statistical software’s, analyzing the statistical data using Excel**

**Module-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 correlations - Pharmaceuticals examples

**Module-2**

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, the essence of sampling, types of sampling, Error-I type, Error-II type, Standard error of the mean (SEM) - Pharmaceutical examples

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

**Module-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 the 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

**Module-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

**Module-5**

Design and Analysis of experiments:

Factorial Design: Definition, 2^2, 2^3design. Advantage of factorial design

Response Surface methodology: Central composite design, Historical design, Optimization Techniques

Measures of central tendency: Mean,

Measures of central tendency: Median, Mode- Pharmaceutical examples

Measures of dispersion: Dispersion, Range,

Measures of dispersion: Standard deviation, Pharmaceutical problems

Correlation: Definition, Karl Pearson’s coefficient of correlation,

Correlation: Multiple correlation - Pharmaceuticals examples

Regression: Curve fitting by the method of least squares,

Regression: Curve fitting by the method of least squares,

Regression: fitting the lines y= a + bx and x = a + by

Regression:Multiple regression, standard error of regression– Pharmaceutical Examples

Probability:Definition of probability, Binomial distribution

Probability: Normal distribution, Poisson’s distribution, properties - problems

Probability: Sample, Population, large sample, small sample

Probability: Null hypothesis, alternative hypothesis

Probability: sampling, essence of sampling, types of sampling, Error-I type, Error-II type

Probability: Standard error of mean (SEM) - Pharmaceutical examples

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

Parametric test: ANOVA, (One way and Two way),

Parametric test: Least Significance difference

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

Non Parametric tests: Kruskal-Wallis test, Friedman Test

Introduction to Research: Need for research, Need for the 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

Designing the methodology: Report writing and presentation of data, Protocol

Designing the methodology: Cohorts studies, Observational studies, Experimental studies, Designing clinical trial, various phases.

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

Introduction to Practical components of Industrial and Clinical Trials Problems: MINITAB®, DESIGN OF EXPERIMENTS

Introduction to Practical components of Industrial and Clinical Trials Problems: R - Online Statistical Software’s to Industrial and Clinical trial approach

Design and Analysis of experiments: Factorial Design: Definition, 2^2, 2^3design

Design and Analysis of experiments: Advantage of factorial design

Response Surface Methodology: Historical design, Optimization Techniques

Response Surface methodology: Central composite design