Statistics II - Syllabus

Embark on a profound academic exploration as you delve into the Statistics II course () within the distinguished Tribhuvan university's CSIT department. Aligned with the 2074 Syllabus, this course (STA210) seamlessly merges theoretical frameworks with practical sessions, ensuring a comprehensive understanding of the subject. Rigorous assessment based on a 60 + 20 + 20 marks system, coupled with a challenging passing threshold of , propels students to strive for excellence, fostering a deeper grasp of the course content.

This 3 credit-hour journey unfolds as a holistic learning experience, bridging theory and application. Beyond theoretical comprehension, students actively engage in practical sessions, acquiring valuable skills for real-world scenarios. Immerse yourself in this well-structured course, where each element, from the course description to interactive sessions, is meticulously crafted to shape a well-rounded and insightful academic experience.

Course objectives:

To impart the theoretical as well as practical knowledge of estimation, testing of hypothesis,

application of parametric and non-parametric statistical tests, design of experiments, multiple

regression analysis, and basic concept of stochastic process with special focus to data/problems

related with computer science and information technology.


Sampling Distribution and Estimation

Sampling distribution; sampling distribution of mean and proportion; Central Limit Theorem;

Concept of inferential Statistics; Estimation; Methods of estimation; Properties of good

estimator; Determination of sample size; Relationship of sample size with desired level of error

Problems and illustrative examples related to computer Science and IT

Testing of hypothesis

Types of statistical hypotheses; Power of the test, concept of p-value and use of p -value in

decision making, steps used in testing of hypothesis, one sample tests for mean of normal

population (for known and unknown variance), test for single proportion, test for difference

between two means and two proportions, paired sample t-test; Linkage between confidence

interval and testing of hypothesis

Problems and illustrative examples related to computer Science and IT

Non parametric test

Parametric vs. non-parametric test; Needs of applying non-parametric tests; One-sample test:

Run test, Binomial test, Kolmogorov–Smirnov test; Two independent sample test: Median test,

Kolmogorov-Smirnov test, Wilcoxon Mann Whitney test, Chi-square test; Paired-sample test:

Wilcoxon signed rank test; Cochran’s Q test; Friedman two way analysis of variance test;

Kruskal Wallis test

Problems and illustrative examples related to computer Science and IT    

Multiple correlation and regression

Multiple and partial correlation; Introduction of multiple linear regression; Hypothesis testing of

multiple regression; Test of significance of regression; Test of individual regression coefficient;

Model adequacy tests

Problems and illustrative examples related to computer Science and IT

Design of experiment

Experimental design; Basic principles of experimental designs; Completely Randomized Design

(CRD); Randomized Block Design (RBD); ANOVA table, Efficiency of RBD relative to CRD,

Estimations of missing value (one observation only), Advantages and disadvantages; Latin

Square Design (LSD): Statistical analysis of m × m LSD for one observation per experimental

unit, ANOVA table, Estimation of missing value in LSD (one observation only), Efficiency of

LSD relative to RBD, Advantage and disadvantages.

Problems and illustrative examples related to computer Science and IT

Stochastic Process

Definition and classification; Markov Process: Markov chain, Matrix approach, Steady- State

distribution; Counting process: Binomial process, Poisson process; Simulation of stochastic

process; Queuing system: Main component of queuing system, Little’s law; Bernoulli single

server queuing process: system with limited capacity; M/M/1 system: Evaluating the system


Lab works

S. No.

Title of the practical problems 
(Using any statistical software such as SPSS, STATA etc. whichever 

No. of


1Sampling distribution, random number generation, and computation of 
sample size 
2Methods of estimation(including interval estimation) 
Parametric tests (covering most of the tests) 
4Non-parametric test(covering most of the tests) 
5Partial correlation 
6Multiple regression 
7Design of Experiments 
9Stochastic process 

Total number of practical problems