STA116

COURSE DESCRIPTION

This subject will provide students with the basic knowledge of probability and statistics and its application in other disciplines. The topics included in this subject will provide a platform for students who will be taking statistics at higher level. Among the concepts introduced are descriptive statistics, probability concepts and special probability distributions. The method of teaching and learning includes lecture, tutorial and discussion, and the assessments consist of test, assignment, group project and final examination. 

COURSE OUTCOMES

At the end of the course, students should be able to:

1) Apply appropriate methods in solving problems regarding descriptive statistics and probability. (C3 - Applying)

2) Demonstrate interpersonal skills through a group project on descriptive statistics. (A3 - Valueing)

3) Apply numeracy skills in solving problems regarding probability. (C3 - Applying)

Chapter 1: Descriptive Statistics

• Definition of Statistics and Types of Statistics

• Variables, Types of Variables, Types of Data and Level of Measurement

• Sampling Techniques and Data Collection Methods

• Organizing and Displaying Data

• Data Descriptions (Measures of Central Tendency, Measures of Dispersion, Measures of Skewness, Shape of Distribution, Coefficient of Variation)

Chapter 2: Probability and Counting Rules

• Sample Spaces and Probability

• Counting Rules (Permutations, Combinations)

• The Addition Rules of Probability

• The Multiplicative Rules and Conditional Probability

• Bayes’ Theorem

Chapter 3: Discrete Random Variables and Probability Distributions

• Concept of a Random Variable

• Discrete Probability Distributions (Probability Mass Functions, Mathematical Expectations)

• Special Discrete Probability Distributions (Uniform, Binomial, Poisson, Poisson Approximation to Binomial)

Chapter 4: Continuous Random Variables and Probability Distributions

• Introduction to Integration of Polynomial Functions

• Continuous Probability Distributions (Probability Density Functions, Mathematical Expectations)

• Special Continuous Probability Distributions (Uniform, Normal, Normal Approximation to Binomial, Normal Approximation to Poisson)