MA25C13 Probability and Statistics – Semester III – CSD

Subject Code & Name: MA25C13 – Probability and Statistics

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Computer Science and Design (CSD)

Credits / L-T-P: 4 Credits | L-T-P: 3-1-0

Course Objectives

To introduce data collection methods, classification techniques, and graphical representation of data using charts and plots.
To explain the fundamental concepts of descriptive statistics, probability theory, random variables, and hypothesis testing for analyzing data.
To demonstrate the application of statistical techniques such as experimental design and process control using R/Python for data-driven decision-making.

Full Unit-wise Syllabus

Unit I – Descriptive Statistics

Collection of Data – Classification – Tabulation – Graphical Representation – Simple Bar Chart – Pie Chart – Measures of Central Tendency: Arithmetic Mean, Median and Mode – Measures of Variation: Range, Quartile Deviation – Standard Deviation and Coefficient of Variation – Five Number Summary – Box Plot Technique.

Activities: Application of descriptive statistics and data presentation methods using R/Python programming and Analysing data using Box Plots using R/Python programming.

Unit II – Probability and Random Variables

Axioms of probability – Conditional probability – Total probability – Bayes' theorem – Random variable – Distribution function – properties – Probability mass function – Probability density function – Moments – Standard Distributions – Binomial, Poisson and Normal Distributions – Problems, Uniform Distribution and Exponential Distribution (Simple Problems).

Activities: Application of various distributions using R/Python programming.

Unit III – Two-Dimensional Random Variables

Joint distributions – Marginal and conditional distributions – Expected values of functions of two variables – Correlation and regression (for discrete data only) – Central limit theorem – Statement and Simple Problems.

Activities: Applications of Correlation and Regression using R/Python programming.

Unit IV – Testing of Hypothesis

Large sample tests for single mean and difference of means – Small samples tests based on t and F distributions (single mean, difference of means, paired t-test and variance ratio test) – Chi-square test for independence of attributes and goodness of fit.

Activities: Application of Student – t test, F test, Chi-square test using R/Python programming.

Unit V – Design of Experiments

Analysis of Variance (ANOVA) – Completely Randomized Design (CRD) – Randomized Block Design (RBD) – Latin Square Design (LSD).

Activities: Application and visualization of One-way ANOVA and Two-way ANOVA using R/Python programming.

Course Outcomes (COs)

CO1: Understand concepts of descriptive statistics, probability theory and testing of hypothesis.
CO2: Apply probability distributions and statistical methods to solve engineering problems.
CO3: Analyze data using correlation, regression, and probability models.
CO4: Utilize hypothesis testing, ANOVA for data-driven decision-making.

Assessment Pattern (Quick Note)

Weightage: Continuous Theory Assessment 20% | Continuous Lab Assessment 20% | End Semester Examinations 60%
Assessment Methodology: Quiz 10%, Assignments 20%, Lab Manual 15%, Lab Examination 15%, Internal Examinations 40%

Source: Official Anna University – B.E. Computer Science and Design R-2025 Syllabus
Last Updated: September 2026

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