1-“Correlation is not causation” is a statistics mantra according to theguardian.com. “Correlation is not causation” means that just because two things correlate does not necessarily mean that one causes the other. Example provided by the article is as followed: just because people in the UK tend to spend more int eh shops when it is cold and less when it is hot, does not mean cold weather causes frenzied high-street spending; a more plausible explanation would be that cold weather tends to coincide with Christmas and the New Year sales.
Reference
Correlation Is Not Causation | Nathan Green’s S Word |n.d.| Access Date August 21, 2018| from
Nathan Green – https://www.theguardian.com/science/blog/2012/jan/06/correlation-causation
Correlation Vs. Causation: An Example – Towards Data Science |n.d.| Access Date August 21, 2018| from
100001147717970 – https://towardsdatascience.com/correlation-vs-causation-a-real-world-example-9e939c85581e
Linear Correlation | Documentation| n.d.| Access Date August 21, 2018| from
https://www.mathworks.com/help/matlab/data_analysis/linear-correlation.html
2-sure we need more information about the study variables like the sample size, age of participants, health history, smoking history and related variables so in the conclusion you can reduce errors and bias, for example.
3-
for your explanation. A correlation measures the degree of the relationship between the variables, while a casual relation means that one variable causes the other. However, a correlation between two variables does not imply causation.
