# 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

#### Units

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

Pearsonâ€™s coefficient of correlation(r); Spearmanâ€™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

#### Lab works

 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