The 'Statistics in Practice' project is partly a team project where the main task is to analyse existing data, present it as a group and prepare a individual infographic for management based on the results of your data analysis. This assessment requires students (in teams of 4-5) to present and discuss an application of data analysis.
Data for your project can come from your workplace (preferable) or it can be a publicly available database. In either case, please check the suitability of the data for your project with your lecturer first.
The statistics project has two main objectives:
1) The description of the data set is designed to develop your skills in: correct use of statistical tools for data summarisation; interpretation and report of univariate and bivariate descriptive stats.
2) The regression analysis part is designed to further develop your skills in: correct use of statistical techniques and interpretation of statistical analyses.
The project should include the objective and research questions, clear definition and measurement of the key variables you intend to use for analysis, numerical and graphical summaries of the data, as well as hypotheses for the relationships you want to test. results of the regression model and findings and conclusions.
In addition, the group presentation should cover the following:
- data collection methods, data reliability and trustworthiness of the source;
- Estimation of two (competing/altemative) regression models (using a minimum of five predictors) and discussing results in detail;
- Include References (Harvard Style - please consult the UWA Library referencing guide).
Contents of the Infographic
Although not prescriptive, the A3 infographic should include the following (max 800 words):
1. Title and name.
2. Abstract. A single sentence or 2-3 bullet points (50 words) summarising the problem and the most important finding(s).
3. Some background information and questions of interest. Key variables and relevant assumptions/hypotheses should be included.
4. Data Analysis. Present appropriate descriptives and charts. Provide your best regression model and interpret it. Briefly comment on the assumptions of the model (residual diagnostics).
5. Conclusions. Summarise the key findings and make recommendations.
Attachment:- car_sales_data.rar