This page contains Syllabus of Statistics I of CSIT.

Title Statistics I
Short Name
Course code STA164
Nature of course Theory and Practical
Second Semester
Full marks 60 + 20 + 20
Pass marks 24 + 8 + 8
Credit Hrs 3
Elective/Compulsary Compulsary

Course Description

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 and Unit Content

1. Introduction
teaching hours: 4 hrs

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


2. Descriptive Statistics
teaching hours: 6 hrs

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

3. Introduction to Probability
teaching hours: 8 hrs

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

4. Sampling
teaching hours: 3 hrs

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

error; Types of sampling

5. Random Variables and Mathematical Expectation
teaching hours: 5 hrs

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

6. Probability Distributions
teaching hours: 12 hrs

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

7. Correlation and Linear Regression
teaching hours: 7 hrs

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

Lab and Practical works

S. No.

Title of the practical problems 

(Using any statistical software such as Microsoft Excel, SPSS, STATA etc. 

whichever convenient). 

No. of


Computation of measures of central tendency (ungrouped and grouped data) Use of an appropriate measure and interpretation of results and 
computation of partition Values 

2Computation measures of dispersion (ungrouped and grouped data) and 
computation of coefficient of variation. 

Measures of skewness and kurtosis using method of moments, Measures of  Skewness using Box and whisker plot, normal probability plot 

4Scatter diagram, correlation coefficient (ungrouped data) and interpretation. Compute manually and check with computer output. 

5Fitting of lines of regression (Results to be verified with computer output) 
6Fitting of lines of regression and computation of correlation coefficient, 
Mean residual sum of squares, residual plots 
7Conditional probability and Bayes theorem 
8Obtaining descriptive statistics of probability distributions 
9Fitting probability distributions in real data (Binomial, Poisson and Normal) 

Total number of practical problems