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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.
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