R code..soil samples
In this assessment you will use the soils dataset created earlier in the course by the class to demonstrate an ability to summarise, present and statistically analyse data.
1. demonstrate an understanding of hypothesis development.
2. demonstrate the ability to summarise and plot data within R and Excel.
3. demonstrate the ability to choose and run appropriate statistical tests.
4. interpret the output of statistical tests and accept or reject hypothesis accordingly.
ASSESSMENT:
The aim of this assessment is for you to demonstrate that you are able to independently analyse a dataset you have gathered in R. Remember to differentiate between ‘treatments’, ‘independent samples’ and ‘replicates’ when choosing data for analysis (refer to first lecture and reading).
Note both Excel and Text files have been provided with your data, these files may not be in the correct format for appropriate analysis and you may want to carry out further refinement. If you choose to do this, save your edited Excel files as a text file to be imported into R (make sure the column names are sensible).
As part of this assessment you are expected to:
1. Develop two sets of hypothesis (each with both a null [H0] and alternate [H1]) which consider physical or chemical differences between the top and sub soil. These can be informed by the information known about soils from your reading and other courses (see reading list for week two). *Note* It is a well expressed and correctly developed hypothesis I am looking for here, not a technically correct one, so don’t worry if you don’t have a soils background.
2. Provide a set of descriptive statistics for each variable measured for both top and sub soil. This information must be provided in a table and the R code used to achieve this table must be listed beneath.
3. Provide a graph (export from R and insert into your document) which summarises the data used to test each hypothesis, also list the code used to generate the graph.
4. Test the appropriate data for normality; present the output and your interpretation.
5. Select and run an appropriate inferential test for each hypothesis. You must provide evidence for inferential test outputs (code, graphs and test outputs). Provide a short explanation of the process you used to choose the statistical test, i.e explain how your data fulfilled test assumptions (up to 350 words). HINT: Use lecture materials (most of the clues you will need will be available there) with independent reading and research to support this section.
6. State whether the null hypothesis have been accepted or rejected Your report should be clearly structured and should include:
– Your matriculation number as a header
– A title
– An introduction (≤ 150 words)
– A brief introduction to the report to describe what samples were analysed and what comparisons you look at in your report.
– Your hypotheses
– Two pairs of alternate and null hypotheses (See point 1, above)
Methodology (≤ 150 words)
– Very brief methodology describing how the samples were collected and analysed (remember the samples were collected from the soil pits and analysed in the lab.) – Results (≤ 350 words, see point 5, above)
– This is where you present the data analysis outlined in points 2 – 5, above.
– Remember to include the R code you used for any calculations, graphs etc.
– Remember to include a legend with brief description and figure number under each graph. (these don’t count towards word count for the section) – Discussion (≤ 200 words)
– State whether the null hypothesis has been accepted or rejected and explain what this means in the context of the samples that you analysed.
– Conclusion (≤ 150 words)
– Brief conclusion to summarise the findings of your experiment and analysis.
