Module 1
Chapter 1: Introduction to Biostatistics
1.1. Statistics and Biostatistics
1.1.1. Meaning and Definition
1.1.1.1. Statistics as Statistical Data
1.1.1.2. Statistics as Statistical Methods
1.1.2. Types of Statistics
1.1.3. Characteristics of Statistics
1.1.4. Scope/Applications of Statistics
1.1.5. Uses of Biostatistics
1.2. Frequency
1.2.1. Introduction
1.2.2. Frequency Distribution
1.2.3. Types of Frequency Distribution
1.2.3.1. Discrete or Ungrouped Frequency Distribution
1.2.3.2. Continuous or Grouped Frequency Distribution
1.2.3.3. Cumulative Frequency Distribution
1.3. Exercise
Chapter 2: Measure of Central Tendency
2.1. Measures of Central Tendency
2.1.1. Meaning and Definition
2.1.2. Importance of Central Tendency
2.1.3. Properties of Good Measures of Central Tendency
2.1.4. Types of Average
2.1.4.1. Mathematical Average
2.1.4.2. Positional Average
2.2. Mean
2.2.1. Introduction
2.2.2. Advantages of Mean
2.2.3. Disadvantages of Mean
2.2.4. Application of Arithmetic Mean
2.2.5. Types of Arithmetic Mean
2.2.6. Method of Calculation of Mean
2.2.6.1. Calculation of Arithmetic Mean—Individual Series
2.2.6.2. Calculation of Arithmetic Mean—Discrete Series (Ungrouped Data)
2.2.6.3. Calculation of Arithmetic Mean—Continuous Series (Grouped Data)
2.2.7. Weighted Arithmetic Mean
2.2.8. Combined Mean/Grouped Mean
2.3. Median
2.3.1. Introduction
2.3.2. Advantages of Median
2.3.3. Disadvantages of Median
2.3.4. Applications of Median
2.3.5. Method of Calculation of Median
2.3.5.1. Calculation of Median—Individual Series
2.3.5.2. Calculation of Median—Discrete Series (Ungrouped Data)
2.3.5.3. Calculation of Median—Continuous Series (Grouped Data)
2.4. Mode
2.4.1. Introduction
2.4.2. Advantages of Mode
2.4.3. Disadvantages of Mode
2.4.4. Methods of Calculation of Mode
2.4.4.1. Calculation of Mode—Individual Series
2.4.4.2. Calculation of Mode-Discrete Series (Ungrouped Data)
2.4.4.3. Calculation of Mode Continuous Series (Grouped Data)
2.5. Relation between Mean, Median, and Mode
2.6. Comparison of Mean, Median, and Mode
2.7. Pharmaceutical Examples
2.8. Exercise
Chapter 3: Measures of Dispersion
3.1. Measures of Dispersion
3.1.1. Meaning and Definition
3.1.2. Methods of Measuring Dispersion
3.2. Range
3.2.1. Introduction
3.2.2. Advantages of Range
3.2.3. Disadvantages of Range
3.2.4. Applications of Range
3.2.5. Coefficient of Range
3.2.6. Methods of Calculation of Range
3.2.6.1. Calculation of Range—Individual Series
3.2.6.2. Calculation of Range—Discrete Series (Ungrouped Data)
3.2.6.3. Calculation of Range—Grouped Series (Grouped Data)
3.3. Standard Deviation (S.D.)
3.3.1. Introduction
3.3.2. Properties of Standard Deviation
3.3.3. Advantages of Standard Deviation
3.3.4. Disadvantages of Standard Deviation
3.3.5. Methods of Calculation of Standard Deviation
3.3.5.1. Calculation of Standard Deviation—Individual Series
3.3.5.2. Calculation of Standard Deviation—Discrete Series (Ungrouped Data)
3.3.5.3. Calculation of Standard Deviation—Continuous Series (Grouped Data)
3.4. Pharmaceutical Problems
3.5. Exercise
Chapter 4: Correlation
4.1. Correlation
4.1.1. Meaning and Definition
4.1.2. Types of Correlation
4.1.2.1. Positive Correlation and Negative Correlation
4.1.2.2. Linear Correlation & Non-Linear (Curvilinear) Correlation
4.1.2.3. Simple, Partial, and Multiple Correlation
4.1.3. Degree of Correlation
4.2. Methods of Computing Correlation
4.1.4. Correlation Coefficient
4.2.1. Introduction
4.2.2. Scatter or Dot Diagram
4.2.2.1. Advantages of Scatter Diagram
4.2.2.2. Disadvantages of Scatter Diagram
4.2.3. Karl Pearson's Coefficient of Correlation
4.2.3.1. Properties of Karl Pearson's Coefficient of Correlation
4.2.3.2. Advantages of Karl Pearson's Coefficient of Correlation
4.2.3.3. Disadvantages of Karl Pearson's Coefficient of Correlation
4.2.3.4. Calculation of Karl Pearson Coefficient of Correlation
4.2.3.5. When Deviations Are Taken from Arithmetic Mean
4.2.3.6. When Deviations Are Taken from Assumed Mean
4.2.3.7. When Step Deviations Are Taken
4.2.3.8. When Actual Data Is Used (Direct Method)
4.2.3.9. Variance-Covariance Method
4.3. Multiple Correlation
4.3.1. Introduction
4.3.2. Properties of Multiple Correlation
4.3.3. Methods of Calculation
4.3.4. Advantages of Multiple Correlation
4.3.5. Disadvantages of Multiple Correlation
4.4. Pharmaceutical Examples
4.5. Exercise
Module 2: Chapter 5: Regression
5.1. Regression
5.1.1. Meaning and Definition
5.1.2. Application of Regression Analysis
5.1.3. Difference between Correlation and Regression Analysis
5.1.4. Regression Lines
5.1.5. Regression Equations
5.1.6. Regression Coefficient
5.1.7. Determination of Linear Regression Equation
5.1.7.1. Curve Fitting by the Method of Least Squares (Fitting the Lines y = a + bx and x = a + by)
5.1.7.2. Regression Equations when Deviation Taken from Actual Mean
5.1.7.3. Regression Equations when Deviation Is Taken from the Assumed Mean
5.1.8. Relationship between Correlation & Regression Coefficients
5.1.9. Standard Error in Regression Analysis
5.2. Multiple Regression
5.2.1. Introduction
5.2.2. Assumptions of Multiple Regression Analysis
5.2.3. Steps in Multiple Regression
5.2.4. Difference between Simple and Multiple Regressions
5.2.5. Methods of Calculation
5.3. Pharmaceutical Examples
5.4. Exercise
Chapter 6: Probability
6.1. Probability
6.1.1. Meaning and Definition
6.1.2. Applications of Probability
6.1.3. Characteristics of Probability Function P(A)
6.1.4. Basic Concepts of Probability
6.1.4.1. Experiment
6.1.4.2. Sample Space
6.1.4.3. Events
6.1.5. Theorems of Probability
6.1.5.1. Multiplication Theorem of Probability
6.1.5.2. Addition Theorem of Probability
6.1.6. Conditional Probability
6.1.7. Bayes' Theorem
6.1.8. Problems
6.2. Probability Distribution
6.2.1. Introduction
6.2.2. Applications of Probability Distribution
6.2.3. Types of Probability Distribution
6.2.4. Binomial Distribution
6.2.4.1. Assumptions of Binomial Distribution
6.2.4.2. Properties of Binomial Distribution
6.2.4.3. Applications of Binomial Distribution
6.2.4.4. Fitting a Binomial Distribution
6.2.4.5. Problems
6.2.5. Poisson Distribution
6.2.5.1. Definition and Probability Function
6.2.5.2 Assumptions of Poisson Distribution
6.2.5.3. Properties of Poisson Distribution 6.2.5.4. Applications of Poisson Distribution
6.2.5.5. Fitting a Poisson Distribution
6.2.5.6. Problems
6.2.6. Normal Distribution
6.2.6.1. Definition and Probability Function 6.2.6.2. Properties of Standard Normal Distribution
6.2.6.3. Standard Normal Distribution
6.2.6.4. Normal Curve
6.2.6.5. Probability and the Normal Curve
6.2.6.6. Fitting a Normal Curve
6.2.6.7. Problems
6.3. Exercise
Chapter 7: Sampling and Hypothesis Testing
7.1. Sampling
7.1.1. Introduction
7.1.2. Characteristics of a Good Sample Design
7.1.3. Population
7.1.3.1. Target Population
7.1.3.2. Statistical Population
7.1.4. Sample
7.1.4.1. Sample Frame
7.1.4.2. Sample Unit
7.1.5. Essence of Sampling
7.1.6. Sampling Process
7.1.7. Advantages of Sampling
7.1.8. Disadvantages of Sampling
7.2. Types of Sampling
7.2.1. Introduction
7.2.2. Probability Sampling
7.2.2.1. Simple Random Sampling
7.2.2.2. Systematic Sampling
7.2.2.3. Stratified Random Sampling
7.2.2.4. Cluster Sampling
7.2.2.5. Multi-Stage Sampling
7.2.2.6. Area Sampling
7.2.3. Non-Probability Sampling
7.3. Sampling Distribution
7.3.1. Meaning of Sampling Distribution
7.3.2. Sampling Distribution of Mean (X)
7.3.3. Means Sampling Distribution of the Difference between Two Independent Samples
7.3.4. Sampling Distribution of Sample Proportion (p)
7.3.5. Sampling Distribution of the Difference between Two Independent Sample Proportions
7.4. Pharmaceutical Numericals
7.5. Hypothesis
7.5.1. Introduction
7.5.2. Qualities of a Good Hypothesis
7.6. Hypothesis Testing
7.6.1. Introduction
7.6.2. Purpose of Hypothesis Testing
7.6.3. Types of Hypothesis
7.6.4. Hypothesis Decision Table
7.6.5. Element of Hypothesis Testing
7.6.6. Steps/Procedure of Hypothesis Testing
7.6.7. Advantages of the Tests of Hypothesis
7.6.8. Disadvantages of the Tests of Hypothesis
7.7. Important Terms
7.7.1. Null Hypothesis
7.7.2. Alternative Hypothesis
7.7.3. Errors in Hypothesis Testing
7.7.4. Level of Significance
7.7.5. Degree of Freedom
7.7.6. One-Tailed and Two-Tailed Tests
7.7.7. Power of Statistical Test/Measuring the Power of Hypothesis Test
7.8. Types of Hypothesis Testing
7.8.1. Small Sample Test
7.8.2. Large Sample Test
7.8.3. Parametric Tests
7.8.4. Non-Parametric Tests
7.9. Standard Error of Mean (SEM)
7.9.1. Introduction
7.9.2. Assumptions and Usage
7.10. Exercise
Chapter 8: Parametric Tests
8.1. Parametric Tests
8.1.1. Introduction
8.1.2. Assumptions about Parametric Test
8.1.3. t-Test (Student's 't' Distribution)
8.1.4. Types of T-Test
8.1.4.1. Sample t-Test
8.1.4.2. Pooled t-Test/Unpaired t-Test
8.1.4.3. Paired t-test
8.2. ANOVA (Analysis of Variance)
8.2.1. Introduction
8.2.2. Characteristics of ANOVA
8.2.3. Assumptions of ANOVA
8.2.4. Applications of ANOVA
8.2.5. Basic Principle of ANOVA
8.2.6. ANOVA Techniques
8.2.7. One-Way ANOVA
8.2.8. Two-Way ANOVA
8.3. Fisher's Least Significant Difference (LSD) Tests
8.4. Exercise
Module 3
Chapter 9: Non-Parametric Tests
9.1. Non-Parametric Tests
9.1.1. Introduction
9.1.2. Characteristics of Non-Parametric Tests
9.1.3. Assumptions about Non-Parametric Tests
9.1.4. Difference between Parametric and Non-Parametric Tests
9.2. Types of Non-Parametric Tests
9.2.1. Wilcoxon Signed-Rank Paired Test
9.2.2. Wilcoxon Rank Sum Test/Mann-Whitney Rank-Sum Test (U-test)
9.2.3. Kruskal-Wallis H Test/K-W Test
9.2.4. Friedman Test
9.3. Exercise
Chapter 10: Introduction to Research
10.1. Research
10.1.1. Meaning and Definition of Research
10.1.2. Nature of Research
10.1.3. Objectives of Research
10.1.4. Need for Research
10.1.5. Essential Criteria of Good Research
10.1.6. Types of Research
10.1.7. Significance of Research
10.1.8. Limitations of Research
10.2. Experimental Research Design/Experimentation
10.2.9. Introduction
10.2.10. Need for Design of Experiments
10.2.11. Criteria for Causality in Experimental Research Design
10.2.12. Causal Relationships
10.2.13. Treatment & Control Group in Experimental Research
10.2.14. Benefits of Experimental Research Design
10.2.15. Limitations of Experimental Research Design
10.2.16. Application of Experimental Research Designs
10.3. Experiential Design
10.3.17. Introduction
10.3.18. Categories of Experiential Design
10.3.19. Experiential Design Techniques
10.3.20. How to Choose the Right Experience
10.4. Plagiarism
10.4.21. Meaning
10.4.22. Types of Plagiarism
10.4.23. Plagiarism Detection Methods
10.4.24. Different Plagiarism Detection Software
10.5. Exercise
Chapter 11: Graphs
11.1. Graphs and Charts
11.1.1. Introduction
11.1.2. Histogram
11.1.3. Pie Charts
11.1.4. Cubic Graph
11.1.5. Response Surface Plot
11.1.6. Contour Plot
11.2. Exercise
Chapter 12: Designing the Methodology
12.1. Designing the Methodology
12.1.1. Introduction
12.1.2. Steps in Research Methodology
12.1.3. Sample Size
12.1.4. Power of a Study
12.2. Report Writing
12.2.1. Introduction
12.2.2. Steps in Writing Reports
12.2.3. Importance of Report Writing
12.3. Presentation of Data
12.3.1. Textual Presentation
12.3.2. Tabulation (Tabular Presentation of Data)
12.3.3. Graphical Presentation of Data
12.4. Protocol
12.4.1. Introduction
12.4.2. Key Aims of Protocols
12.4.3. Writing the Protocol
12.4.4. Benefits of Protocol
12.5. Cohort Studies
12.5.1. Introduction
12.5.2. Concept of a Cohort Study
12.5.3. Framework or Design of a Cohort Study
12.5.4. Types of Cohort Study
12.5.4.3. Combination of Prospective and Retrospective Cohort Study
12.5.5. Steps in Planning a Cohort Study
12.5.6. Advantages of a Cohort Study
12.5.7. Disadvantages of a Cohort Study
12.6. Observational Studies
12.6.1. Introduction
12.6.2. Types of Observation: Observational Methods
12.6.3. Process of Conducting Observation
12.6.4. Significance of Observation
12.6.5. Limitations of Observation
12.7. Experimental Studies
12.7.1. Introduction
12.7.2. Randomized Controlled Trials
12.7.3. Crossover studies
12.7.4. Experimental Study of Populations
12.8. Designing Clinical Trials
12.8.1. Introduction
12.8.2. Some Principles of Experimental Design and Analysis
12.8.3. Various Phases of Clinical Trials
12.9. Exercise
Chapter 13: Blocking and Confounding System
13.1. Blocking and Confounding System for Two-Level Factorials
13.1.1. Introduction
13.1.2. Blocking a Replicated 2k Factorial Design
13.1.3. Confounding in the 2* Factorial Design
13.1.4. Confounding the 2 Factorial Design in Two Blocks
13.1.5. Confounding the 2k Factorial Design in Four Blocks
13.2. Exercise
Module 4
Chapter 14: Regression Modelling
14.1. Hypothesis Testing
14.1.1. Introduction
14.1.2. Linear Regression
14.1.3. Hypothesis Testing in Simple Regression Models
14.1.4. Hypothesis Testing in Multiple Regression Model
14.2. Practical Components of Industrial and Clinical Trials Problems
14.3. Statistical Analysis
14.4. MS Excel
14.4.1. Introduction
14.4.2. Using Excel for Pivot Tables
14.4.3. Using Excel for Descriptive Statistics
14.4.4. Using Excel for ANOVA (Analysis of Variance)
14.4.5. Using Excel for Statistical Analysis: Moving Average
14.4.6. Using Excel for Statistical Analysis: Regression
14.5. Statistical Package for the Social Sciences (SPSS)
14.5.1. Introduction
14.5.2. Starting SPSS
14.5.3. Creating and Entering Data in SPSS
14.5.4. Computation of Descriptive Statistics
14.6. Minitab
14.6.1. Introduction
14.6.2. Entering Data
14.6.3. Viewing Descriptive Statistics
14.6.4. Creating Graphs and Charts
14.6.5. Running a Regression Analysis
14.7. Design of Experiment
14.7.1. Introduction
14.7.2. Components of Experimental Design
14.7.3. Purpose of Experimentation
14.7.4. Experiment Design Process
14.8. Online Statistical Software for Industrial and Clinical Trial Approaches
14.9. Exercise
Module 5
Chapter 15: Design and Analysis of Experiment
15.1. Factorial Design
15.1.1. Introduction
15.1.2. Definition (Vocabulary)
15.1.3. 22 Factorial Design
15.1.4. 2³ Factorial Design
15.1.5. Advantages of Factorial Design
15.1.6. Disadvantages of Factorial Design
15.2. Response Surface Methodology
15.2.1. Introduction
15.2.2. Objectives of Response Surface Methodology (RSM)
15.2.3. Experimental Strategy
15.2.4. Types of Models
15.2.5. Sequential Nature of RSM
15.2.6. Methods of RSM
15.2.7. Applications of RSM
15.3. Response Surface Designs
15.3.1. Introduction
15.3.2. Types of Response Surface Design
15.4. Historical Data Design
15.4.1. Introduction
15.4.2. Design the "Experiment"
15.4.3. A Peculiarity on Pasting Data
15.4.4. Analyze the Results
15.5. Optimization Techniques
15.5.1. Introduction
15.5.2. Optimization Using Factorial Designs
15.5.3. Sequential Optimization
15.5.4. Screen Design
15.6. Exercise
