MAST 30025 Linear Statistical Models
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What the course is all about?
The linear models are important to the hypothesis and exercise of modern statistics. They are employed to model acknowledgment as a linear monopoly of explanatory variables and are the greatly wielded statistical models in exercise. Commencing with examples from a spectrum of application regions, this subject formulates an elegant centralized theory that encompasses the following:
- Appraisal of model parameters
- Quadratic forms
- Hypothesis testing by utilizing analysis of variance
- Model selection
- Diagnostics on model assumptions, and prediction.
Both the full-rank prototypes and as well as the models that are not of full rank are reckoned. Throughout the program, the hypothesis is exemplified by utilizing common models and empirical designs.
The successful learning outcomes of the program include:
- Comprehend the fundamental statistical hypothesis of linear models and the constraints of such models.
- Accommodate linear models to the data by utilizing a standard statistical computing assortment and comprehend the outcomes.
- Attain the skill to operate with unfamiliar situations and recognize pertinent solution policies.
- Amass the capability to establish and convey logical statements and to operate in abstract or general tenures to improve the transparency and efficiency of the investigation.
- The mastery to confront formal deadlines while equalizing contending commitments.
- Proficiency in manipulating statistical computing packages.
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