Basic Statistics

Course Title: Basic Statistics

Course No: STA154

Nature of the Course: Theory + Lab

Semester: 2

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

The course familiarizes students with the basic concepts of statistics including introduction, diagrammatical and graphical representation, descriptive statistics, probability, random variables, sampling, and correlation and regression.

Course Objectives

To impart the knowledge of descriptive statistics, correlation, regression, concept of sampling and sampling distribution, theoretical as well as applied knowledge of probability and some probability distributions.

Course Contents

1. Introduction

5 hrs

1.1. Basic concept of statistics

1.2. Application of Statistics in different fields including information technology

1.3. Scales of measurement

1.4. Variables

1.5. Types of Data and data source

1.6. Data preparation-editing, coding, and transcribing

2. Diagrammatical and Graphical Presentation of Data

3 hrs

2.1. Bar diagrams

2.2. Pie diagrams

2.3. Pareto chart

2.4. Graph of frequency distribution

3. Descriptive Statistics

7 hrs

3.1. Measures of central tendency

3.2. Measures of dispersion

3.3. Measures of skewness

3.4. Measures of kurtosis

3.5. Moments

3.6. Steam and leaf display

3.7. Five number summary

3.8. Box plot

3.9. Problems and illustrative examples related to IT

4. Introduction to Probability

7 hrs

4.1. Concepts of probability

4.2. Definitions of probability

4.3. Laws of probability

4.4. Bayes theorem

4.5. Prior and posterior probabilities

4.6. Problems and illustrative examples related to IT

5. Random Variables and Mathematical Expectation

3 hrs

5.1. Concept of a random variable

5.2. Types of random variables

5.3. Probability distribution of a random variable

5.4. Mathematical expectation of a random variable

5.5. Addition and multiplicative theorems of expectation (without proof)

5.6. Problems and illustrative examples related to IT

6. Probability Distributions

6 hrs

6.1. Probability distribution function

6.2. Binomial distribution

6.3. Poisson distribution

6.4. Normal distribution and their characteristic features

6.5. Applications of these distributions in IT related data problems

6.6. Problems and illustrative examples related to computer Science and IT

7. Sampling and Sampling Distribution

7 hrs

7.1. Definitions of population

7.2. Sample survey vs. census survey

7.3. Sampling error and non sampling error

7.4. Types of sampling

7.5. Standard error of mean

7.6. Standard error of proportion

7.7. Sampling distribution of mean and proportion

7.8. Need of inferential Statistics

7.9. Concept of estimation

7.10. Confidence interval estimation for mean and proportion

7.11. Problems and illustrative examples related to IT

8. Correlation and Linear Regression

7 hrs

8.1. Bivariate data

8.2. Bivariate frequency distribution

8.3. Correlation between two variables

8.4. Karl Pearson's coefficient of correlation (r)

8.5. Spearman's rank correlation

8.6. Regression Analysis

Fitting of lines of regression by the least squares method

Coefficient of determination

8.7. Problems and illustrative examples related to IT

Laboratory Works

Practical (Computational Statistics): Practical problems to be covered in the Computerized Statistics laboratory using any statistical software such as Microsoft Excel, SPSS, STATA etc. whichever convenient.

1.Diagrammatical and graphical presentation of data
2.Computation of measures of central tendency
3.Computation of measures of dispersion
4.Measures of skewness and kurtosis
5.Scatter diagram and correlation coefficient
6.Fitting of simple linear regression model
7.Conditional probability and Bayes theorem
8.Problems related to Binomial, Poisson and Normal probability distributions
9.Problems related to sampling and sampling distribution

Text Books

1.Michael Baron (2013). Probability and Statistics for Computer Scientists. 2nd Ed., CRC Press, Taylor & Francis Group, A Chapman & Hall Book.
2.Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, & Keying Ye (2012). Probability & Statistics for Engineers & Scientists. 9th Ed., Printice Hall.

Reference Books

1.Douglas C. Montgomery & George C. Ranger (2003). Applied Statistics and Probability for Engineers. 3rd Ed., John Willey and Sons, Inc.
2.Richard A. Johnson (2001). Probability and Statistics for Engineers. 6th Ed., Pearson Education, India

Notes:

This syllabus follows the official BIT curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.

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Basic Statistics

Course Description

Course Objectives

Course Contents

Laboratory Works

Text Books

Reference Books

Notes: