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A student wishes to investigate whether the pass rate of first year actuarial science students at universities is correlated to a mark obtained in a mathematical entrance exam.

Describe the key steps to follow in the data analysis process, for this investigation, and give an example, applicable in this scenario, for each of the steps.
in Actuarial Science by Platinum (91,198 points) | 8 views

1 Answer

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- Develop a well-defined set of objectives that need to be met by the results of the data analysis
- Here the objective is to determine if mathematical entrance exam results is correlated to the Actuarial Science \(1^{\text {st }}\) year students' performance
- Identify the data items required for the analysis
'o The data items needed would be the mathematical entrance mark for the \(1^{\mathrm{st}}\) year students doing Actuarial Science in the different South African Universities and the \(1^{\text {st }}\) year final year results for such students over a period of time
- Collection of the data from appropriate sources
- The data can be obtained from the Universities offering Actuarial Science degrees.
- Processing and formatting the data for analysis, e.g. inputting into a spreadsheet, database or other model.
- The data will need to be extracted from the administrative system of the Universities and loaded into whichever statistical package is being used for the analysis.
- Cleaning data, e.g. addressing unusual, missing or inconsistent values
- For example, a student may be recorded as registered at University X but the marks may be missing or marks which are unrealistic e.g. negative numbers or marks more than \(100 \%\) per subject \(\mathrm{X}\)
- Exploratory data analysis,
o Here takes the form of inferential analysis as we are here testing the hypothesis that mathematical entrance mark is correlated with \(1^{\text {st }}\) year Actuarial Science performance at universities.
- Modelling the data.
o In this case we need to choose the correct statistical method e.g. a Chi-squared test for the analysis
- Communicating the results, which include: describing the data sources used, the analysis performed and the conclusion of the analysis.
- Monitoring the process. Updating the data and repeating the process if required.
- May mean choosing another statistical package to use, or adjusting the level of significance chosen.
by Platinum (91,198 points)

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