+1 862 207 3288 This is obviously a major part. You should include information on how the data were selected, reviewed,
checked for goodness (and transformed if necessary), and justification of your choice of statistical
method. So it’s not just, “we did a one-way ANOVA”. It tests your reasons for choosing a technique, to
make sure you understand why you would choose a particular technique (ANOVA andt-test). Your
description of the data analysis should include your reasoning. You need to be able to argue that your
data meet the assumptions of the chosen method (this does not necessarily mean things like formal tests
of normality of data, but you do need to be able to argue the point), or at least explain what
constraint/s you are having to ignore (with sensible reasons if you have to break some data
preconditions) . There are many projects that have quite small data sets, and so only simple analyses
are possible. However, the reason for the simple choice comes back to the data you have collected, and
is still appropriate for description in this section. We don’t believe we’ve seen any projects where no
analysis was possible. If there are any out there, then you are describing your reasons for NOT being
able to analyze data, and then describing how you present those data to tell the story of your results
(see below).
Results
These should always be brief in terms of words used. Avoid the temptation to interpret any of your
results – that is for the discussion. Use tables and figures as appropriate, but don’t present the same
data in both a table and a figure (that’s a waste of space).
Discussion
This where you relate your results to the original hypotheses, conceptual models, and literature. As
mentioned in lectures a few times, ‘tell the story’ of your data. What do the results actually mean? Do
they agree with your hypotheses? If not, can you offer a plausbile explanation of why not? Flag any
problems you had with the collected data. If you have access to team reports from similar projects in
the past, you can use these as another source of ‘prior information’ similar to other published
literature. Speculate about how you might improve your monitoring program if you had the chance to do
it again. This section is where you get to be thoughtful.
S1 S2 S3 S4
3c 0.007 0.006 0.007 0.007
S1 S2 S3 S4
10 0.014 0.034 0.008 0.004
S1 S2 S3 S4
11 0.040 0.050 0.049 0.043
S1 S2 S3 S4
32 0.007 0.008 0.020 0.005
S1 S2 S3 S4
6 0.005 0.010 0.008 0.014
S1 S2 S3 S4
7 0.009 0.007 0.005 0.007
S1 S2 S3 S4
15 0.516 0.540 0.525 0.448
S1 S2 S3 S4
8 0.031 0.029 0.03 0.029
S1 S2 S3 S4
4 0.005 0.005 0.004 0.004
S1 S2 S3 S4
9 0.007 0.007 0.007 0.010
Calculation: mg/L
Dams Nitrate concentration(mg/L) a
b
3c S1 S2 S3 S4 Average Standards Curve Equation
y=0.0009x-0.0003 0.0009 0.0003 R^2=0.99512
8.11 7.00 8.11 8.11 7.83 y axis:absorbance
10 S1 S2 S3 S4 Average x axis:amount of NO3-N(ug)
15.89 38.11 9.22 4.78 17.00 Volume of water sample(ml)
11 S1 S2 S3 S4 Average Concentration?
44.78 55.89 54.78 48.11 50.89
32 S1 S2 S3 S4 Average
8.11 9.22 22.56 5.89 11.44
6 S1 S2 S3 S4 Average
5.89 11.44 9.22 15.89 10.61
7 S1 S2 S3 S4 Average
10.33 8.11 5.89 8.11 8.11
15 S1 S2 S3 S4 Average
573.67 600.33 583.67 498.11 563.94
8 S1 S2 S3 S4 Average
34.78 32.56 33.67 32.56 33.39
4 S1 S2 S3 S4 Average
5.89 5.89 4.78 4.78 5.33
9 S1 S2 S3 S4 Average
8.11 8.11 8.11 11.44 8.94
