Report outline

A general section structure for a data analysis report is

  • Introduction
  • Data
  • Methods
  • Results
  • Discussion

Please note that these do not need to be the titles of the sections. Using informative titles provides readers with a quick understand of the report topic and approach being used. For example, consider the following section titles.

  • Radon rates in Minnesota
  • Floor-specific radon measurements in MN counties
  • Regression model with CAR spacial random effects for counties
  • Results
    • Decreased radon measurements in upper floors
    • Radon higher in northeastern MN
  • Discussion
    • Observational study
    • Radon mitigation

These section titles still use the general structure with informative section names that allow the reader to understand the entirety of the report without needing to read all the details.

The contents of each section are described below.

Introduction

This section should provide a brief introduction to the subject matter you are discussing and should present the scientific question of interest.

Data

The data section should provide relevant details about the data that you will be modeling. It should include information like the range of values for each variable including units, the relationship between the variables, the number of observations, etc. This section can provide tables or figures to help the reader understand the data.

Methods

The methods section should introduce the model that you are fitting to the data using statistical notation. The model discussion should include how the parameters in this model will be estimated and how this model will address the scientific question of interests.

Introduced notation, e.g. Y_i, X_i, etc, should be defined. Statistical notation does not need to be defined, e.g. ind, N(m,C), etc, but conventions used in this class should be followed, e.g. C is the variance in N(m,C). If there is a doubt about the convention, then it is best to be explicit, e.g. specify if b in Ga(a,b) is the rate or the scale.

Since the methods in this project will likely depend on MCMC, you need to specify how convergence in the MCMC will be assessed.

Results

The results section should provide summaries of the results of applying methods from the Methods section on the data in the Data section. Before discussing the actual estimates of parameters, you should provide details of the MCMC, e.g. number of burn-in and inferential iterations, as well as an assessment of convergence.

After summarizing convergence, summarize the posterior distribution of the model parameters. This is typically best done by plots of posterior distributions or, if there are too many parameters, plots of credible intervals. In addition, the answer to the scientific question should be presented.

Discussion

The discussion section provides an interpretation of the results from the results section. It also provides an opportunity to discuss any shortcomings in the model or the data.