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Quantitative Techniques- II Notes: ALL UNITS
Unit 1: Probability Theory
Probability Theory is a branch of mathematics that deals with the analysis of random phenomena and the likelihood of events occurring.
Unit 2: Descriptive Statistics
Descriptive Statistics is a branch of statistics that focuses on summarizing and organizing data to make it more understandable and interpretable.
Unit 3: Correlation and Regression Analysis
Correlation and Regression Analysis are statistical methods used to explore the relationship between two or more variables.
Unit 4: Sampling and Estimation
Sampling and Estimation are key concepts in statistics used to draw conclusions about a population based on a sample.
Unit 5: Hypothesis Testing and Analysis of Variance (ANOVA)
Hypothesis Testing and Analysis of Variance are statistical techniques used to make inferences and test assumptions about populations.
Syllabus: Quantitative Techniques – II
UNIT – 1
(a) Introduction to Probability:
Definition, terminology – experiment, event, sample space
Classical and empirical approaches
Laws of probability – addition and multiplication
Conditional probability and Bayes’ theorem
(b) Probability Distributions:
Discrete and continuous distributions
Binomial, Poisson, and Normal distributions – properties and applications
UNIT – 2
Index Numbers:
Meaning, uses, and types
Methods of constructing index numbers – Laspeyres, Paasche’s, and Fisher’s index
Cost of living index
Problems and limitations of index numbers
UNIT – 3
Time Series Analysis:
Meaning and components – trend, seasonal, cyclical, and irregular variations
Methods of measuring trend – Moving averages, Method of Least Squares
Business forecasting using time series data
UNIT – 4
Decision Theory:
Decision-making under certainty, risk, and uncertainty
Payoff tables and decision trees
Expected value criterion, Maximin, Maximax, Minimax regret
UNIT – 5
Linear Programming:
Introduction, assumptions, and applications
Formulation of Linear Programming Problems (LPP)
Graphical solution method – maximization and minimization problems
Simplex method – basic concept (only formulation and interpretation, not computation-heavy)