This online Statistical Practice 1 unit from Open Universities Australia and Swinburne University of Technology examines inferential statistics including normal distribution, sampling distributions, hypothesis testing and confidence intervals for the proportion and mean, hypothesis testing for Pearson's r, t-tests for paired and independent data and chi-square tests.
You will also use the statistical package PASW (formerly SPSS) to obtain relevant graphs and statistics.
Core learning outcomes include selecting appropriate IBM SPSS Statistics procedures, Java applets on the web, and mathematical calculations to obtain test results. The unit also provides skills in writing reports that effectively communicate investigation findings.
You cannot enrol in this unit if you have successfully completed any unit deemed academically equivalent.
You will require access to a recent version of SPSS.
In order to enrol in this unit, you must be accepted into one of the following courses:
Graduate Certificate of Science (Applied Statistics)
Graduate Diploma of Science (Applied Statistics)
Master of Science (Applied Statistics)
Areas of study
In this Statistical Practice 1 unit, the topics you will study include:
Displaying and summarising categorical data
Summarising metric data
A categorical and metric variable
Two metric variables
Two categorical variables
Correlation and causation
Modelling population distribution
Calculation of normal percentages
An empirical sampling distribution
Theoretical sampling distributions
Samples to populations
Statistical inference for the mean
Two paired samples inference for the mean
Two independent samples inference for the mean
Statistical inference for the population coefficient
The chi-square test of independence
Summary of statistical inference
This unit is a core requirement in the following courses:
Graduate Certificate of Science (Applied Statistics), Swinburne University of Technology
Graduate Diploma of Science (Applied Statistics), Swinburne University of Technology
Master of Science (Applied Statistics), Swinburne University of Technology
Content may also be relevant to other courses, and could qualify you for Recognition of Prior Learning (RPL) for a course offered by another institution.
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At the completion of the unit, you will be able to graphically display dataset information, and have core competencies in the foundations of statistical inference, and sampling distributions. You will also be able to recognise when more advanced techniques are needed, and write interpretive summary reports for both descriptive and inferential statistical analysis.
How else will I benefit from this unit?
Upon successful completion of this unit you will receive credit for competencies in Statistical Practice 1 from Swinburne University of Technology.
Studying online means you can study where you want, at your own pace.
Gain industry-relevant skills and transferrable knowledge.
Government financial support through FEE-HELP may be available.
Studying single units allows you the flexibility to work towards a qualification at your own pace.
Receive Recognition of Prior Learning (RPL) for this unit across a number of OUA’s full degree programs.
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Want more information about the career benefits of this unit? Fill out the enquiry form to the right and a study consultant will contact you with the details you need.
If you are an Australian citizen, you may be eligible to receive a government FEE-HELP loan, meaning you can defer payment of your fees.
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Want more information about financial and student support? Fill out the enquiry form to the right and a study consultant will contact you with the details you need.
You may be eligible to receive FEE-HELP for this course!
This course can be paid for through the FEE-HELP government loan scheme. If you are an Australian citizen or hold a permanent humanitarian visa, this means you don’t need to pay upfront.
Instead, the Australian government will pay your course fees on your behalf. You’ll begin repaying your loan through the tax system once you start earning more than the minimum threshold of $54,869 (2016-17 income year).
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