Available in 2021
Course code

STAT1300

Units

10 units

Level

1000 level

## Course handbook

### Description

Statistics provides us with a quantitative framework to utilise data for describing, summarising and modelling the world around us. This course provides students with fundamental concepts of probability, random variables and their distributions, and then applies them to provide the theoretical foundation for data analysis through statistical modelling, estimation and hypothesis testing. These concepts are motivated by real problems and illustrated with the leading statistical software platform R, a free, open-source, growing library of functions for object-oriented statistical programming.

Both the theory and the introduction to R provide the foundation for subsequent 2000 and 3000 level statistics courses. The theory is also is of general interest to users of statistical methods, reaching beyond the application of these methods to address their underlying principles, formal description and rigorous justification.

The written assignments give you the opportunity to apply the concepts we learn in lectures to a number of problems, some theoretical and others computational involving the use of R. The topics covered will align with the course content covered by that stage of the semester.

Interested in studying further statistics courses to develop your skills and improve your employability? Information about available statistics courses can be found here: https://www.newcastle.edu.au/school/mathematical-and-physical-sciences/study/statistics-courses

### Availability2021 Course Timetables

#### Callaghan

• Semester 2 - 2021

### Replacing course(s)

This course replaces the following course(s): STAT2010. Students who have successfully completed STAT2010 are not eligible to enrol in STAT1300.

### 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. Recognise 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.

### Content

• Probability
• Random variables and their distributions
• Expected values
• Functions of random variables
• Joint distributions
• Samples, statistics, sampling distributions and limit theorems
• Estimation
• Hypothesis testing

### Requisite

Students cannot enrol in this course if they have successfully completed STAT2010.

### Assumed knowledge

Integral calculus, as covered in MATH1110

### Assessment items

Written Assignment: 3 Assignments

Formal Examination: Formal Examination

### Contact hours

#### Callaghan

##### Laboratory

Face to Face On Campus 2 hour(s) per Week for 11 Weeks

##### Lecture

Face to Face On Campus 2 hour(s) per Week for 12 Weeks

The University of Newcastle acknowledges the traditional custodians of the lands within our footprint areas: Awabakal, Darkinjung, Biripai, Worimi, Wonnarua, and Eora Nations. We also pay respect to the wisdom of our Elders past and present.