Assessment Task Information
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Key details:
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Assessment title:
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Statistical Investigation
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Module Name:
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Introduction to Advanced Statistics
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Module Code:
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PM608
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Assessment will be set on:
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Beginning of Cycle 3
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Feedback opportunities:
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peer feedback in class, online feedback from class teacher 2 week before submission deadline.
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Assessment is due on:
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9:00 am UK time on 18 February 2025
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Assessment weighting:
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50%
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Assessment Instructions
What do you need to do for this assessment?
Task:
You are required to complete a written Statistical Investigation, which will involve researching a topic and question. From the VLE page, you will be provided data set and scenario for this assessment. You will introduce the topic by providing a historical or theoretical background and you will explain how the research question will be answered. You will descriptively analyze the data by use of calculations and graphs. You will complete a written analysis of the data, which will inform. your conclusion. Any sources used will be referenced and analyzed sample data will be included in an Appendices section.
Your tutor will provide support and feedback during the weeks leading up to the deadline. You must submit the Statistical Investigation on the VLE and via Turnitin by 18 February 2025, 9:00 am UK time.
Guidance:
For this assessment you should make use of the following formative activities that you have already completed. These activities have been designed to support this summative assessment:
• Peer feedback on proposal to receive feedback.
• Workshop with tutor.
Deadline for draft submission: 4 February 2025, 9:00 am UK time.
Note: Draft submission is compulsory.
You do not have to act on their feedback, but you may find it useful to enhance your final submission.
Please note:
This is an individual assessment so you should not work with any other student.
Structure:
The Statistical Investigation will be typed as a Word document, with sections and subheadings. Any graphs created using software such as Excel will be inserted into the Word document. Your report should be divided into the following sections.
1. Title page
2. Introduction
Outline the purpose of you report and the data used. Explain to the reader the questions you hope to answer and the software you will use.
3. Methodology
• Using the data, you should use an appropriate random sampling method to choose a sample of size of 30 for each category (treatment).
• Describe the methods used to analyse the data (e.g., statistical analysis, regression analysis, confidence interval).
4. Calculations and Graphs
You must include:
• A full numerical summary of the data, which includes the 5-number summary, measures of central tendency, measures of variation and skew and shape of the data.
• Appropriate graphical representations (e.g., histograms, box plots).The graphs should be presented and labelled appropriately.
• Comments on the graphs shown.
From the data provided and sample selected, you must highlight any:
• possible errors in measurement
• outliers
You must describe their effect on your conclusions. You should select and justify what statistical methods you will use to identify any statistical links between the variables in the data, and to link this with the questions you wish to answer.
5. Analysis of data
a) Regression Analysis.
You must include:
• three scatter plots
• three best fit regression lines
• three correlation coefficients,
• three residual plots.
• comments on the correlation and regression coefficient, including the possible effects of any outliers and/or high influential values.
• comments on the validity of the regression model, using all the scatterplots, regression lines, correlation coefficients, and residual plots to do this.
• comments on how the collection method of the data and the quality of the data effects the validity of the models.
b) Confidence intervals for the difference in means.
You must include:
• How you are categorizing the data and what 2-sample confidence interval you will use.
• You must think about how you are going to compare the data sets.
• You must pick two confidence levels, stating clearly what confidence level you will use and why.
• Full calculations must be included for each interval.
• A discussion on the physical interpretation of the two intervals.
• Comments on how the collection method of the data and the quality of the data effects the validity of the intervals.
6. Conclusion
You should summarize any findings in the form. of supported recommendations. The clarity of these recommendations and their reasons are paramount.
7. References
8. Appendices – sample of the dataset you received after sampling.
Theory and/or task resources required for the assessment:
You may use your textbook, notes, PowerPoints etc. You will use a variety of academic sources from which you will collect data. Your work and ideas must be your own and/or correctly referenced.
1: You will have to demonstrate skills in finding the measures of location and spread and creating charts. You will also need to compare scores on different datasets.
2: You will have to demonstrate skills in creating scatter graphs, finding correlation and regression coefficients and interpreting these results.
3: You will have to demonstrate skills in random sampling, constructing grouped frequency tables, graphing data sets, and calculating confidence intervals.
4: You will have to demonstrate skills in the use of Microsoft Excel for data analysis.
5: You will have to make a reasoned written recommendation based on your analysis of the data.
Referencing style.:
Any sources used must be referenced and included in a Harvard style. reference list at the end of your report.
There should be at least 4 references in your report.
Expected word count:
You must include all the recommended sections but there is no set word count.
Learning Outcomes Assessed:
1. Critique original research data sets relevant to their field of study selecting appropriate statistical methods.
2. Discuss the relevance, validity, and reliability of statistical methods in the context of experimental design.
3. Evaluate and interpret scientific information and data, both qualitative and quantitative, relevant to applications of their subject area