Probability and Statistics
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Follow on Instagram for UpdatesProbability and Statistics Syllabus — Unit-wise Content
The official GTU curriculum for Probability and Statistics (subject code ME01095011) as prescribed for AIAD · Semester 1 divides the course into 5 units, covering 27 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME01095011)Unit 1 Basic Probability Concepts
- Definitions and Rules of Probability
- Sample Spaces and Events
- Conditional Probability and Independence
- Random Variables and Probability Distributions
- Discrete and Continuous Random Variables: Probability Mass Functions (PMF) and Probability Density Functions (PDF)
- Cumulative Distribution Functions (CDF)
Unit 2 Discrete Random Variables and Their Distributions
- Binomial Distribution
- Poisson Distribution
- Geometric and Negative Binomial Distributions
- Expectation and Variance of Discrete Random Program Name: Master of Engineering Level: PG Branch: Computer Engineering (Artificial Intelligence And Data Science) Course / Subject Code : ME01095011 Subject Name: Probability and Statistics Variables: Properties of Expectation
- Functions of Random Variables
- Applications and Examples
Unit 3 Continuous Random Variables and Their Distributions
- Uniform Distribution
- Normal Distribution
- Exponential and Gamma Distributions
- Expectation and Variance of Continuous Random Variables
Unit 4 Sampling Distribution of the Mean and The Sampling Distribution of
- the Variance
- Point Estimation and Properties of Estimators
- Interval Estimation and Confidence Intervals
- Maximum Likelihood Estimation
Unit 5 Hypothesis Testing
- Hypothesis Testing Framework
- Hypothesis Tests for Single Mean
- Single Proportion and Single Variance
- Hypothesis Tests for Difference Between Two Population Means and Difference Between Two Population Proportions
- F-Ratio Test for Equality of two Variances
- Single-Factor ANOVA
- Multiple Comparisons in ANOVA
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