SOUTHWESTERN UNIVERSITY, NIGERIA
FACULTY OF PURE AND APPLIED SCIENCES
Department of Mathematics and Computer Science
STA 211 – PROBABILITY II
COURSE PARTICULARS
Course Code: STA 211
Course Title: PROBABILITY II
No of Units: 3
Course Duration: Two hours per week for 14 weeks
Status: Required
Prerequisite: STA 112
COURSE INSTRUCTOR
D. O. Daniel
Department of Mathematics and Computer Science
Southwestern University, Nigeria
Phone No: +2348166748714
Email:
COURSE DESCRIPTION
This course is designed primarily for students in mathematics, pure and applied sciences.
However, it also meets the need of students in other fields. The course’s focus is to educate the
student on the basic principles and applications of probability; several probability concepts and
techniques; and how to employ them in making generalisations and decisions on social, health
and related issues. Topics to be covered include: Probability - Discrete sample spaces, definitions
and rules of probability, combinatorial analysis, conditional probability, independence, and
Bayes’ theorem; Mean and variance of some discrete probability distribution – Bernoulli
distribution; Binomial distributions, Poisson distributions; Discrete uniform distribution,
geometric distribution; hyper-geometric distribution; Application of some discrete probability
distribution - Binomial distribution, Poisson distribution, negative binomial distribution,
, geometric distribution, hyper-geometric distribution, multi-normal distribution; Mean and
variance of some continuous probability distribution – uniform or rectangular distribution,
exponential distribution; Sampling with and without replacement.
COURSE OBJECTIVES
The objectives of this course are to educate the student on the:
Basic principles and applications of probability.
Several probability concepts and techniques.
Employability of probability in making generalization and decisions on social, health,
economic issues.
COURSE LEARNING OUTCOMES/COMPETENCES
Upon successful completion of this course, the students will be able to:
Solve related probability with combinatorial analysis, Bayes’ theorem and conditional
probability
State and prove the mean and variance of discrete and continuous distribution functions.
Solve related problems of discrete and continuous distribution functions.
Define sampling and differentiate between sampling with and without replacement.
GRADING SYSTEM FOR THE COURSE
This course will be graded as follows:
Assignments 10%
Popup Test(s) 10%
Test(s) 20%
Examination 60%
TOTAL 100%
GENERAL INSTRUCTIONS
Attendance: It is expected that every student will be in class for lectures. Attendance records
will be kept and used to determine each person’s qualification to sit for the final examination. A
student must have at least 80% in attendance before being qualified to sit for final examination.
A student must dress corporate before the student can be allowed to sign attendance sheet. In
FACULTY OF PURE AND APPLIED SCIENCES
Department of Mathematics and Computer Science
STA 211 – PROBABILITY II
COURSE PARTICULARS
Course Code: STA 211
Course Title: PROBABILITY II
No of Units: 3
Course Duration: Two hours per week for 14 weeks
Status: Required
Prerequisite: STA 112
COURSE INSTRUCTOR
D. O. Daniel
Department of Mathematics and Computer Science
Southwestern University, Nigeria
Phone No: +2348166748714
Email:
COURSE DESCRIPTION
This course is designed primarily for students in mathematics, pure and applied sciences.
However, it also meets the need of students in other fields. The course’s focus is to educate the
student on the basic principles and applications of probability; several probability concepts and
techniques; and how to employ them in making generalisations and decisions on social, health
and related issues. Topics to be covered include: Probability - Discrete sample spaces, definitions
and rules of probability, combinatorial analysis, conditional probability, independence, and
Bayes’ theorem; Mean and variance of some discrete probability distribution – Bernoulli
distribution; Binomial distributions, Poisson distributions; Discrete uniform distribution,
geometric distribution; hyper-geometric distribution; Application of some discrete probability
distribution - Binomial distribution, Poisson distribution, negative binomial distribution,
, geometric distribution, hyper-geometric distribution, multi-normal distribution; Mean and
variance of some continuous probability distribution – uniform or rectangular distribution,
exponential distribution; Sampling with and without replacement.
COURSE OBJECTIVES
The objectives of this course are to educate the student on the:
Basic principles and applications of probability.
Several probability concepts and techniques.
Employability of probability in making generalization and decisions on social, health,
economic issues.
COURSE LEARNING OUTCOMES/COMPETENCES
Upon successful completion of this course, the students will be able to:
Solve related probability with combinatorial analysis, Bayes’ theorem and conditional
probability
State and prove the mean and variance of discrete and continuous distribution functions.
Solve related problems of discrete and continuous distribution functions.
Define sampling and differentiate between sampling with and without replacement.
GRADING SYSTEM FOR THE COURSE
This course will be graded as follows:
Assignments 10%
Popup Test(s) 10%
Test(s) 20%
Examination 60%
TOTAL 100%
GENERAL INSTRUCTIONS
Attendance: It is expected that every student will be in class for lectures. Attendance records
will be kept and used to determine each person’s qualification to sit for the final examination. A
student must have at least 80% in attendance before being qualified to sit for final examination.
A student must dress corporate before the student can be allowed to sign attendance sheet. In
