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.

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.