lecture 2: Pearson correlation

Pearson correlation
1. Why do we use a Pearson correlation? What exactly do we test with it? What is the null hypothesis?
2. How does a Pearson correlation work? What is the main principle?
3. Slide 9: How do observations in each quadrat affect the correlation coefficient?
4. Slide 9: Is the correlation between X and Y the same as between Y and X?
5. Slide 16: What is the relationship between t, the standard error of t, and the sample size n? How does it take into account statistical uncertainty?
6. Slide 20: explain what the null distribution tells us about the observed t value. How can we calculate the probability that we observe a t-value equal to or more extreme than the estimated t-value under H0? What are the implications for the acceptance or rejection of the null hypothesis?
7. What are the important assumptions of the Pearson correlation?
8. How do we report a Pearson correlation?

answer:

1. person correlation shows the relationship between 2 numerical variables. The null hypothesis is when the person correlation equals 0

2. it is equal to the estimated covariance between both variables divided by the product of the estimated standard deviations on x and y. (see the formula on slide 9)