Unit-I

Introduction, Measures of Central Tendency, Dispersion, and Correlation

Introduction: This section covers the definitions and general concepts of Statistics and Biostatistics, including the scope and application of statistical methods in biological and pharmaceutical sciences. It also addresses Frequency Distribution and methods for organizing raw data into grouped and ungrouped distributions.

Measures of Central Tendency: This section details the Mean (definition, arithmetic mean calculation, and use with pharmaceutical examples like average tablet weight), the Median (definition, calculation, and utility, particularly with skewed data), and the Mode (definition, calculation, and application, such as finding the most frequent tablet hardness).

Measures of Dispersion: This covers the concept and necessity of measuring data variability (Dispersion). Specific measures include Range (simple measure of spread) and Standard Deviation (σ or s) (definition, formula, calculation for raw and grouped data, and its significance in pharmaceutical problems like batch-to-batch variation). Practical numerical problems related to these measures are included.

Correlation: This section defines the concept of Correlation (relationship between two or more variables), introduces Karl Pearson’s coefficient of correlation (r) (definition, formula, calculation, and interpretation of linear relationship), and defines Multiple correlation (relationship between one variable and a set of others, with pharmaceutical examples).

Unit-II

Regression, Probability, Sampling, and Parametric Tests

Regression: This section focuses on Curve fitting by the method of least squares, specifically fitting the linear equations and . It also covers Multiple regression and the standard error of regression, providing pharmaceutical examples for context.

Probability: The unit defines Probability and explores key Distributions: Binomial, Normal, and Poisson’s distribution, including their properties and related problems.

Sampling and Hypothesis Testing Basics: Foundational concepts are covered, including Sample, Population, large sample, small sample, the Null hypothesis and alternative hypothesis. It details the essence of sampling, various types of sampling, the nature of Error-I type and Error-II type, and the Standard error of mean (SEM), all illustrated with pharmaceutical examples.

Parametric Tests: This introduces specific statistical tests: the -test (Sample, Pooled or Unpaired, and Paired), ANOVA (One way and Two way), and the Least Significance difference method.

Unit-III

Non-Parametric Tests, Research Design, and Methodology

Non-Parametric Tests: This section details several non-parametric alternatives to the tests in Unit-II: the Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis test, and the Friedman Test.

Introduction to Research: Topics include the Need for research, the necessity for design of Experiments, the Experiential Design Technique, and the ethical issue of plagiarism.

Graphs: Various methods for data visualization are covered, including Histogram, Pie Chart, Cubic Graph, response surface plot, and Counter Plot graph.

Designing the Methodology: This crucial section addresses Sample size determination and the Power of a study. It includes practical skills like Report writing and presentation of data, creating a Protocol, and understanding different study designs: Cohorts studies, Observational studies, Experimental studies. It concludes with the process of Designing a clinical trial and its various phases.

Unit-IV

Advanced Factorials, Regression Modeling, and Statistical Software

Blocking and Confounding System: This section deals with advanced design of experiments concepts, specifically the Blocking and confounding system for Two-level factorials.

Regression Modeling: It covers Hypothesis testing in Simple and Multiple regression models.

Introduction to Practical Components of Industrial and Clinical Trials Problems: This is a practical component focusing on Statistical Analysis Using Software. The unit introduces the application of software like Excel, SPSS, MINITAB, DESIGN OF EXPERIMENTS, and R - Online Statistical Software’s to industrial and clinical trial approaches.

Unit-V

Design and Analysis of Experiments (DOE) and Optimization

Design and Analysis of Experiments: The primary focus here is Factorial Design. This includes its definition, detailed coverage of the 2² and 2³ design, and the Advantage of factorial design.

Response Surface Methodology (RSM): This advanced optimization technique is covered, including specific designs like the Central composite design and Historical design, as well as general Optimization Techniques.