Details

Session 1: Self-study Period

        Students will work through the textbook and practice problems, as well as watch videos.

Session 2: In-person period

        Monday 16 August: In-person lecture and lab 10am-12pm and 2pm-4pm

        Tuesday 17 August: In-person lecture and lab 10am-12pm and 2pm-4pm

        Wednesday 18 August: In-person lecture and lab 10am-12pm and 2pm-4pm

Session 3: Self-study period

        Students will work through the textbook and practice problems, as well as watch videos.

Description 

This micro-credential aims to facilitate a high level understanding of statistical techniques used in the analysis of data. This course introduces students to the philosophy and methods of modern statistical data analysis and inferential methods. The course has a strong emphasis on computing and graphical methods, and uses a variety of real-world problems to motivate the theory and methods required for carrying out statistical data analysis. This course makes extensive use of R statistical analysis package interfaced through R Studio.

Topics 

1. Hypothesis testing for one and two populations
2. Analysis of variance
3. Simple linear regression
4. Multiple linear regression

Learning outcomes 

Upon successful completion, enrolees will have the knowledge and skills to:

1. Demonstrate basic knowledge of the R statistical computing language
2. Conduct and explain the results of inferential procedures, including hypothesis tests and confidence intervals
3. Carry out and interpret an analysis of variance test and compare the difference between two or more sets of data
4. Apply and interpret simple and multiple regression models

Indicative assessment 

Assignment 1: 15%, LO: 1 and 2.

Assignment 2: 25%, LO: 1, 2, 3, and 4.

Project: 60%,10-page limit, LO: 1, 2, 3, and 4.

Assumed knowledge 

Statistics for Data Analysis A is the prerequisite for Statistics for Data Analysis B. Please note that pre-requisites may be waived based on previous experience. Get in touch with us for more information.

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as part of a stack by completing Statistics for Data Analysis B.

Successful completion of Statistics for Data Analysis A and B can lead to specified credit for the course STAT7055 Introductory Statistics for Business and Finance.

Details 

Course Code: DATA33

Workload: 72 hours 

• Contact hours: 12 hours
• Individual study and assessment: 60 hours

ANU unit value: 3 units

Course Code Level: 7000

Contact: Jo Drienko (RSFAS DDE)

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

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