BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY Books
All books are given below after 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, 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.
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 experiment, R- Online Statistical Software’s to Industrial and Clinical trial approach.
Unit-V
Design and Analysis of experiments:
Factorial Design: Definition, 22, 23design. Advantages of factorial design.
Response Surface methodology: Central composite design, Historical design, Optimization Techniques.
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