Statistical analysis process

In a statistical analysis, there is a typical sequence of steps.

  1. Interview the data – where did it come from? Is it high quality? Does it omit any details? Are omitted details important?
  2. Check for significant correlation – Are variables related to one another? What happens if you randomly re-distribute one or more variables? Is there a trend? Does it change with re-distribution?
  3. Check for causality – Does one variable affect the value of another?
  4. Generalize causality – Can the causality apply to broader contexts? Is causality limited to a certain area, scope, demographic, timeframe, etc?

Each step tends to refine data, questions, etc.. Some analysis attempts may not make it through the series of steps.

Landscapes of Change – peer C02 Usage Analysis

Participants in Landscapes of Change provided CO2 usage data. Eight of the 34 participants (23%) provided data specific enough for comparison, in effect describing the tons of C02 estimated to sustain their current lifestyles.

The original samples, in tons of c02 per year, are

  • 12.0
  • 30.0
  • 14.0
  • 16.0
  • 26.0
  • 8.4
  • 13.6
  • 24.0

With so few observations, it is difficult to determine any statistically significant patterns.

Following are some basic general statistics, as I learn how to use R Studio.

  • The sample average is 18 tons per year.
  • The group standard deviation is 7.7, meaning 68% of the participants use between 10.3 and 25.7 tons of c02 per year.
  • The histogram does not seem to follow a normal distribution.

C02 Usage for the Landscapes of Change program