## Statistics I Syllabus

**General Information**

Course | B.SC. CSIT |
---|---|

Course Title | Statistics I |

Course No | STA169 |

Nature of the course | Theory + Lab |

Semester | II (Second) |

Full Marks | 60 + 20 + 20 |

Pass Marks | 24 + 8 + 8 |

Credit Hrs. | 3 |

**CHAPTER LIST: ****Statistics I**

S.N. | Chapter | Time |
---|---|---|

Unit 1 | Introduction | 4 Hrs |

Unit 2 | Descriptive Statistics | 6 Hrs |

Unit 3 | Introduction to Probability | 8 Hrs |

Unit 4 | Sampling | 3 Hrs |

Unit 5 | Random Variables and Mathematical Expectation | 5 Hrs |

Unit 6 | Probability Distributions | 12 Hrs |

Unit 7 | Correlation and Linear Regression | 7 Hrs |

**Course Description:** This course contains basics of statistics, descriptive statistics, probability, sampling, random variables and mathematical expectations, probability distribution, correlation and regression.

**Course Objectives:** The main objective of this course is to impart the knowledge of descriptive statistics, correlation, regression, sampling, theoretical as well as applied knowledge of probability and some probability distributions.

## Course Contents:

Unit 1: Introduction

**(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

Unit 2: Descriptive Statistics

**(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

Unit 3: Introduction to Probability

**(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

Unit 4: Sampling

**(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

**(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

Unit 6: Probability Distributions

**(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

Unit 7: Correlation and Linear Regression

**(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

## Laboratory Works:

The laboratory work includes using any statistical software such as Microsoft Excel, SPSS, STATA etc. whichever convenient using Practical problems to be covered in the Computerized Statistics laboratory

**Practical problems**

S. No. | Title of the practical problems | No. of practical problems |
---|---|---|

1 | Computation of measures of central tendency (ungrouped and grouped data) | 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 | 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 plot | 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 |

## Statistics I Books

### 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.