Fundamentals of Statistics

Description

Statistics is about using data to describe, summarise and model the world around us. Whether it's cricket scores, the stock market or global warming, we need to understand probability and data analysis in order to make informed decisions and predictions.

This course develops basic concepts of probability, random variables and their distributions, and then shows how these ideas provide the theoretical foundation for data analysis through statistical modelling, estimation and hypothesis testing.

Although a variety of real problems are used to illustrate the concepts and methods, the emphasis in this course is on statistical theory. STAT2010 is an essential part of a Statistics major providing the necessary background for most 3000 level statistics courses. STAT2010 is also suitable for those students in a range of undergraduate programs who wish to understand why statistical methods work (and not just how to operate a statistical software package without any true comprehension).

Availability

Callaghan

  • Semester 2 - 2016
  • Semester 1 - 2017

Learning Outcomes

On successful completion of the course students will be able to:

1. Explain the mathematical basis of probability

2. Define real world problems in statistical terms

3. Recognize common probability distributions and their applications

4. Solve a range of problems involving expectation and transformation of random variables.

5. Use simple Monte Carlo simulation methods

6. Use fundamental principles of estimation to derive statistical estimators

7. Explain the principles of hypothesis testing and derive and use simple hypothesis tests

8. Develop simple statistical models useful in analysing real problems.

Content

The course will include the following topics

  • Probability
  • Random variables and their distributions
  • Expected values
  • Functions of random variables
  • Joint distributions
  • Samples, statistics, sampling distributions and limit theorems
  • Estimation
  • Hypothesis testing
  • Simple statistical models - regression and ANOVA

Assumed Knowledge

MATH1110 Mathematics for Engineering, Science and Technology 1 AND STAT1070 Statistics for the Sciences OR MATH1120 Mathematics for Engineering, Science and Technology 2 OR MATH1210 Mathematical Discovery 1 OR MATH1220 Mathematical Discovery 2

Assessment Items

Written Assignment: Assignments (x4)

Formal Examination: Examination

Contact Hours

Callaghan

Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term

Lecture

Face to Face On Campus 2 hour(s) per Week for Full Term