Essentials of Math & Stats in Forensic

Offered: Fall 2025

Programme: MS-Forensic Sciences (Semester-I)

Course Code: FOR605

Organization: Amity University, Punjab 

Course Brochure: (here)

Class Attendance (here)

Assessment Scores (here)

Theory Lectures

Week-1

• Lecture-1: Number Systems and Units of Measurement (here)

• Lecture-2: Dealing with Uncertainties (here)

• Lecture-3: Mean (here)

Week-2

• Lecture-4: Mean for grouped data, Median (ungrouped) (here)

Week-3

• Lecture-5: Median (grouped data), Mode (here)

• Lecture-6: Measures of dispersion (Range, Mean deviation) (here)

• Lecture-7: Measures of dispersion (Standard deviation, Coefficient of variation) (here)

Week-4

• Lecture-8: Correlation analysis (here)

• Lecture-9: Regression analysis (here)

• Lecture-10: Functions and equations (here)

Week-5

• Lecture-11: Linear functions (here)

Week-6

• Lecture-12: Quadratic equations and its applications in Forensic science (here)

Week-8

• Lecture-13: Exponential and Introduction to Logarithms (here)

Week-10

• Lecture-14: Logarithmic Functions (here)

• Lecture-15: Application Logarithmic functions: pH Scale (here)

• Lecture-16: Application exponential functions: Forensic pharmacokinetics (here)

Week-11

• Lecture-17: Trigonometric functions for forensic science (here)

• Lecture-18: Inverse trigonometric functions, sine and cosine formulas (here)

• Lecture-19: Applications of Trigonometry (Heights & Distances) (here)

Week-12

• Lecture-20: Fundamentals of Probability (here)

• Lecture-21: Counting Principles & Probability for Forensic Science (here)

Week-13

• Lecture-22: Independent Events, Conditional Probability (here)

• Lecture-23: Law of Total Probability and Introduction to Bayes’ Theorem (here)

• Lecture-24: Application to Bayes' theorem (here)

Week-14

• Lecture-25: Random variable and Probability distribution function (here)

• Lecture-26: Cumulative distribution function (here)

• Lecture-27: Expected value and variance of a random variable (here)

Week-15

• Lecture-28: Discrete probability distributions (Bernoulli, Binomial) (here)

• Lecture-29: Continuous probability distributions (Normal Distribution) (here); Z-Tables (here)

• Lecture-30: Applications to Normal distribution (here)