Probability & Statistics (MATH-102)

INTRODUCTION

This course describe the concepts of data analysis, presentation, counting techniques, probability and decision making. The course contents of this course covers all the main aspects of statistics. They easily describe the real life applications of statistics as well as the use of statistics in their field. Furthermore, the estimation of unknown parameters with some basic touch of regression analysis are also described in this course and on the basis of these unknown parameters how prediction and forecasting will be carried out are also learned in this subject. R- Language software will also be taught to easily deal with big data.

COURSE CODE: MATH-102

CREDIT HOURS: 03

PREREQUISITES: None

COURSE LEARNING OUTCOMES

At the end of the course the students will be able to:

The students will be able to understand data analysis, modeling and predictions in their respective fields.
The content of this course covers all the descriptive statistics and probability models along with some basic touch of regression analysis.
In addition, R- Language software will also be taught so that they were easily get insight into the challenges with big data.

TEXT BOOKS: The recommended text books for this study is:

Probability and Statistics for Engineers and Scientists by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying E. Ye, Pearson; 9th Edition (January 6, 2011). ISBN-10: 0321629116
Probability and Statistics for Engineers and Scientists by Anthony J. Hayter, Duxbury Press; 3rd Edition (February 3, 2006), ISBN-10: 0495107573
Schaum's Outline of Probability and Statistics, by John Schiller, R. Alu Srinivasan and Murray Spiegel, McGraw-Hill; 3rd Edition (2008). ISBN-10: 0071544259.
Probability: A Very Short Introduction by John Haigh, Oxford University Press (2012). ISBN-10: 0199588481

COURSE CONTENTS

Introduction to Statistics and Data Analysis with some practices on R-Language software.
Different concepts of Probability.
Random Variables and Probability Distributions.
Mathematical Expectation.
Discrete Probability Distributions.
Continuous Probability Distributions.
Fundamental Sampling Distributions and Data Descriptions, One- and Two-Sample Estimation Problems with some practices on R-Language software.
Single Sample, One- and Two-Sample Tests Concerning Variances.
Simple Linear Regression and Correlation with some practices on R-Language software.
Multiple Linear Regression with some practices on R-Language software.