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.
Units
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
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
performance.
Lab works
| S. No. | Title of the practical problems | (Using any statistical software such as SPSS, STATA etc. whichever | convenient). | No. of practical problems | ||||
| 1 | Sampling distribution, random number generation, and computation of | sample size | 1 | |||||
| 2 | Methods of estimation(including interval estimation) | 1 | ||||||
3 | Parametric tests (covering most of the tests) | 3 | ||||||
| 4 | Non-parametric test(covering most of the tests) | 3 | ||||||
| 5 | Partial correlation | 1 | ||||||
| 6 | Multiple regression | 1 | ||||||
| 7 | Design of Experiments | 3 | ||||||
| 9 | Stochastic process | 2 | ||||||
| Total number of practical problems | 15 |