Basic Statistics
Course Code: STA154 Full Marks: 60 (Theory) + 20 (Practical) + 20 (Internal)Nature of Course: Theory + Laboratory Pass Marks: 24 (Theory) + 8 (Practical) + 8 (Internal)
Credit Hours: 3
Course Description
This course introduces students to the basic ideas of statistics. It explains how data is collected, organized, presented, and analyzed. The course covers graphical representation of data, descriptive statistics, probability, random variables, sampling methods, and the study of correlation and regression, with examples related to information technology.
Course Objective
The main objective of this course is to provide students with knowledge of descriptive statistics, probability, sampling, and data analysis techniques. Students will learn both theoretical concepts and practical applications of probability, correlation, regression, and probability distributions.
Course Contents
Unit 1: Introduction to Statistics (5 Hours)
This unit introduces the basic concepts of statistics and its importance in different fields, including information technology. It covers types of data, data sources, scales of measurement, variables, and basic data preparation methods such as editing, coding, and transcribing.
Unit 2: Diagrammatic and Graphical Presentation of Data (3 Hours)
This unit explains different ways to present data visually. Topics include bar diagrams, pie charts, Pareto charts, and graphs of frequency distributions.
This unit explains different ways to present data visually. Topics include bar diagrams, pie charts, Pareto charts, and graphs of frequency distributions.
Unit 3: Descriptive Statistics (7 Hours)
This unit focuses on methods used to summarize data. It includes measures of central tendency (mean, median, and mode), measures of dispersion (range, variance, standard deviation), skewness, kurtosis, moments, stem-and-leaf displays, five-number summaries, and box plots. Practical examples related to IT are also included.
This unit focuses on methods used to summarize data. It includes measures of central tendency (mean, median, and mode), measures of dispersion (range, variance, standard deviation), skewness, kurtosis, moments, stem-and-leaf displays, five-number summaries, and box plots. Practical examples related to IT are also included.
Unit 4: Introduction to Probability (7 Hours)
This unit explains the basic ideas of probability. Topics include definitions of probability, laws of probability, Bayes’ theorem, and the concepts of prior and posterior probabilities. Examples related to information technology are discussed.
This unit explains the basic ideas of probability. Topics include definitions of probability, laws of probability, Bayes’ theorem, and the concepts of prior and posterior probabilities. Examples related to information technology are discussed.
Unit 5: Random Variables and Mathematical Expectation (3 Hours)
This unit introduces random variables and their types. It covers probability distributions of random variables, mathematical expectation, and the basic rules of expectation. Practical examples related to IT are included.
This unit introduces random variables and their types. It covers probability distributions of random variables, mathematical expectation, and the basic rules of expectation. Practical examples related to IT are included.
Unit 6: Probability Distributions (6 Hours)
This unit explains important probability distributions such as binomial, Poisson, and normal distributions. Their characteristics and applications in computer science and IT-related data problems are discussed with examples.
Unit 7: Sampling and Sampling Distribution (7 Hours)
This unit focuses on sampling techniques and their importance. Topics include population and sample, census and sample surveys, sampling and non-sampling errors, types of sampling, standard error of mean and proportion, sampling distributions, estimation, and confidence intervals for mean and proportion. Practical examples related to IT are included.
Unit 8: Correlation and Linear Regression (7 Hours)
This unit explains the relationship between two variables. Topics include bivariate data, correlation, Karl Pearson’s correlation coefficient, Spearman’s rank correlation, and linear regression using the least squares method. The concept of the coefficient of determination is also discussed with IT-related examples.
Laboratory Works
Practical (Computational Statistics)
Students will solve practical problems using statistical software such as Microsoft Excel, SPSS, STATA, or similar tools.
List of Practical Problems:
- Diagrammatic and graphical presentation of data (1)
- Calculation of measures of central tendency for grouped and ungrouped data (1)
- Calculation of measures of dispersion and coefficient of variation (1)
- Calculation of skewness and kurtosis using moments and box-and-whisker plots (2)
- Scatter diagrams and calculation of correlation coefficient (manual and computer-based) (1)
- Fitting a simple linear regression model and residual analysis (1)
- Problems on conditional probability and Bayes’ theorem (3)
- Problems on binomial, Poisson, and normal distributions (2)
- Problems on sampling, sampling distributions, and confidence interval estimation (3)
Total Number of Practical Problems: 15
Text Books
- Michael Baron (2013). Probability and Statistics for Computer Scientists, 2nd Edition, CRC Press.
- Ronald E. Walpole et al. (2012). Probability and Statistics for Engineers and Scientists, 9th Edition, Prentice Hall.
Reference Books
- Douglas C. Montgomery & George C. Runger (2003). Applied Statistics and Probability for Engineers, 3rd Edition, Wiley.
- Richard A. Johnson (2001). Probability and Statistics for Engineers, 6th Edition, Pearson Education India.
