STAT4026/STAT3926
Statistical Consulting
Semester 1, 2022
Week 1 Report Writing
Consulting in the past has covered many interesting topics from
social sciences, health sciences, agriculture, geology, transport,
education, etc.
It is hard to suggest one report structure that is suitable to all
type of analyses and aims:
Model/method suggestion, eg. Gpower package, imputation
method, etc
Provision of R template with simulated data, eg GLM, and
Real data analysis.
Model suggestion:
GPower package support
The aim here is to provide some general guidelines and you may
not include all the suggested sections.
Note: Before writing your report, you may ask the clients for
data (if analyses are required), some written-down research
questions and research papers (if relevant).
Report title.
Under your report title, remember to include
names of the lead, note taker, other members and the clients (if not in
the title), and
date
Executive summary (1 page max).
– Short description of the problem(s).
– What are the main findings and key figures if appropriate?
– What is the practical (biological/clinical) relevance?
Some domain knowledge from the clients.
Background/problem.
– Longer description of the scientific problem(s).
Provide background, experimental designs and the objectives/aims.
– Translation of the scientific problem into a statistical problem.
– Data summaries and visualisation (may be a separate data section:
sample size, number of variables with description, data transformations)
Line plot of continuous X factors to show the trends (over time).
These plots serve to provide check and justification to include some
significant factors and their interaction for testing and/or modelling.
Other plot types for categorical (ordinal here) Y variable:
Data transformation
Eg. Feed Conversion Ratios (FCR)
Often provided from clients. Or you may transform the data to fulfil the
normality assumption but interpretation (from the client’s of view) may be
harder.
Data aggregation
Reduce data size but may smooth out noise or periodic effects, eg from
daily to weekly.
Missing data and imputation (if necessary), etc.
Note: provide title with important information and var labels for all tables
and plots.
Limitation of data
Analysis.
– What statistical tools, such as models and measures, are used and why.
Hypothese test?
Regression?
ANOVA? Repeated measure ANOVA to include time effect?
Logistic regression?
Ordinal logistic regression?
Parametric or non-parametric?
Univariate or multivariate?
Include random effects, nested effects or interaction effects?
How to write the model?
Using formula?
Or less technical using some vocal description?
– What are the results?
Present in tables?
With coefficients?
In figures or heat maps?
With p-values only?
In formulae?
To generate an aggregate engagement score:
– Interpret the results (to clients or statisticians?).
Interpret coefficients?
Aim for factor significance or/and the effect size?
Conclusion.
– Assumption check and shortcomings to the analysis.
– Summarise important founding and practical relevance.
Reference (if applicable).
Appendix.
Codes, more results, plots, technical details, model checks, time sheet, etc.
Consulting report marking rubric