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Course Module Information
Course Modules
ST311: Applied Statistics I
Semester 1 | Credits: 5
An introduction to methods and applications in applied statistical inference. This module builds on the statistical inferential methods demonstrated in pre-requisite module ST2120 or ST2002 or similar modules. The module builds on regression modelling, where topics covered include model estimation, model checking and inference for simple linear regression and multiple linear regression models, and procedures in variable selection. Models discussed are applicable for a single quantitative response with quantitative and/or qualitative predictors.
Learning Outcomes
- calculate and interpret correlations between variables and make inferences about relationships;
- formulate a linear regression model, calculate and interpret estimated coefficients and make statistical inferences on the fitted model by carrying out statistical tests using parameter estimates and using the ANOVA table. Regression models discussed include a single quantitative response explained by a single explanatory variable or multiple explanatory variables which include quantitative and/or categorical explanatory variables and interactions between variables;
- obtain fitted values and predictions at new data points, together with associated prediction and confidence intervals;
- by calculating regression diagnostics and producing relevant plots check the adequacy of the model specification for the data presented and to check model assumptions, including linearity, normality, constant variance, independence and the presence of outliers and influential points;explore the need for transformations of response and explanatory variables;
- interpret and use output from variable selection procedures to choose adequate models, including the best subsets procedure and step-wise;
- complete introductory time series analysis, including descriptive analysis via calculation of various index numbers, recognize time series components, apply smoothing models such as Moving-average models, Exponential Smoothing, and Holt-Winters Smoothing, apply seasonal regression models using additive models with indicator variables, describe autocorrelation and carry out the Durbin-Watson test, use models to forecasting trends and seasonality, measure Forecasting accuracy using MAD, MAPE, and RMSE.
- carry out the regression analysis with the use of software, R;
- compile a statistical report, i.e. prepare a typed document which introduces the statistical research question being explored, describes the data collection method applicable to the research, describes relevant features of the sample data obtained, and outlines conclusions from inferential statistical analysis carried out using the sample data, incorporating output and plots from statistical software.
Assessments
- Written Assessment (65%)
- Continuous Assessment (35%)
Teachers
- COLLETTE MCLOUGHLIN:
Research Profile |
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- EMMA HOLIAN:
Research Profile |
Email
Reading List
- "Applied Linear Regression Models" by Kutner, Nachtsheim & Neter
Publisher: McGraw Hill - "STAT2" by Ann R. Cannon,George W.. Cobb,Bradley A.. Hartlaub,Julie M.. Legler,Robin H.. Lock
ISBN: 1-4641-4826-0.
Publisher: W H Freeman & Company
Note: Module offerings and details may be subject to change.