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Introduction to Probability and Data Analysis (On-campus)
Undergraduate | CUR-STAT1005 | 2021
Course information for 2021 intake
- Study method
- On-campus
- Assessments
- Subject may require attendance
- Entry requirements
- No ATAR needed,
- No prior study
- Duration
- 17 weeks
HECS-HELP available
Introduction to Probability and Data Analysis (On-campus)
About this subject
- identify the role of probability models in formulating random phenomena in the real-world and decide on an appropriate model for particular random phenomena
- apply probabilistic techniques to evaluate probabilities of events and to evaluate the mean, variance and other characteristics of univariate random variables and their functions
- use a statistical software environment to carry out exploratory and statistical analyse
- draw inferences from the results of statistical analyses and report the results using the output from a statistical software environment
- 1. Introduction to statistics and probability
- 2. Non-naive definition of probability, conditional probability
- 3. Discrete random variables, probability mass functions, cumulative distribution functions
- 4. Hypergeometric and Poisson distributions, Poisson approximation of binomial
- 5. Uniform, exponential, gamma, beta and normal distributions
- 6. Joint probability distributions of continuous random variables
- 7. Random sampling, types of variables, graphical and numerical summaries
- 8. Parameters and statistics, population proportion and sample proportion
- 9. Choosing sample size for the proportion
- 10. Hypothesis testing for proportion and mean
- 11. Matched pair situations
- 12. Sample covariance and sample correlation, Spearman's correlation
This unit is an introduction to methods of statistical analysis and their role in scientific inference - drawing conclusions about the world using data. It begins with an introduction to probability, which provides a foundation for understanding randomness, uncertainty, chance, and most important of all, for statistical inference. We start with a gentle introduction to definitions of probability and probability models, and then go on to discuss probability distributions, and tools and techniques that can be used to model a wide range of phenomena, as well as those that are useful in statistical inference. In the second part of the unit, we discuss descriptive statistics and inferential statistics. Throughout, we will emphasize that how data are collected determines the nature of the conclusions that we are entitled to draw from any study. Students will be introduced to the R language statistical environment, which they will use for exploring, displaying, and analysing data, and for carrying out and reporting the results of statistical inference.
1. Test 1, 25 percent, ULOs assessed 1,2;
2. Test 2, 25 percent, ULOs assessed 2,3,4;
3. Exam, 50 percent, ULOs assessed 1,2,4.- Test 1 (25%)
- Test 2 (25%)
- Examination (50%)
For textbook details check your university's handbook, website or learning management system (LMS).
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No entry requirements
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
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