# Statistics I Syllabus

 Title Statistics I Short Name Course code STA164 Nature of course Theory and Practical Semester 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

population

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 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