Statistics I - Syllabus

Embark on a profound academic exploration as you delve into the Statistics I course () within the distinguished Tribhuvan university's CSIT department. Aligned with the 2074 Syllabus, this course (STA164) 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 knowledge of descriptive statistics, correlation, regression, sampling, theoretical as

well as the applied knowledge of probability and some probability distributions

Introduction

Basic concept of statistics; Application of Statistics in the field of Computer Science &

Information technology; Scales of measurement; Variables; Types of Data; Notion of a statistical

population

Descriptive Statistics

Measures of central tendency; Measures of dispersion; Measures of skewness; Measures of

kurtosis; Moments; Steam and leaf display; five number summary; box plot

Problems and illustrative examples related to computer Science and IT

Introduction to Probability

Concepts of probability; Definitions of probability; Laws of probability; Bayes theorem; prior and

posterior probabilities

Problems and illustrative examples related to computer Science and IT

Sampling

Definitions of population; sample survey vs. census survey; sampling error and non sampling

error; Types of sampling

Random Variables and Mathematical Expectation

Concept of a random variable; Types of random variables; Probability distribution of a random

variable; Mathematical expectation of a random variable; Addition and multiplicative theorems

of expectation

Problems and illustrative examples related to computer Science and IT

Probability Distributions

Probability distribution function, Joint probability distribution of two random variables; Discrete

distributions: Bernoulli trial, Binomial and Poisson distributions; Continuous distribution: Normal

distributions; Standardization of normal distribution; Normal distribution as an approximation of

Binomial and Poisson distribution; Exponential, Gamma distribution

Problems and illustrative examples related to computer Science and IT

Correlation and Linear Regression

Bivariate data; Bivariate frequency distribution; Correlation between two variables; Karl

Pearson’s coefficient of correlation(r); Spearman’s rank correlation; Regression Analysis: Fitting

of lines of regression by the least squares method; coefficient of determination

Problems and illustrative examples related to computer Science and IT

	S. No.		Title of the practical problems  (Using any statistical software such as Microsoft Excel, SPSS, STATA etc.  whichever convenient).		No. of	practical	problems
1		Computation of measures of central tendency (ungrouped and grouped data) Use of an appropriate measure and interpretation of results and	computation of partition Values		1
2		Computation measures of dispersion (ungrouped and grouped data) and	computation of coefficient of variation.	1
3		Measures of skewness and kurtosis using method of moments, Measures of  Skewness using Box and whisker plot, normal probability plot		2
4			Scatter diagram, correlation coefficient (ungrouped data) and interpretation. Compute manually and check with computer output.			1
5		Fitting of lines of regression (Results to be verified with computer output)	1
6		Fitting of lines of regression and computation of correlation coefficient,	Mean residual sum of squares, residual plots	1
7		Conditional probability and Bayes theorem	3
8		Obtaining descriptive statistics of probability distributions	2
9		Fitting probability distributions in real data (Binomial, Poisson and Normal)	3
		Total number of practical problems	15