 Correlation and Significance - Is this approach correct?
Hello,
Could any one please confirm the following;
I have two columns of data (temperature) that I would like to compare. I would like to understand the statistical relationship between the two. I have about 1200 temperature measurements.
Is the following approach valid?
Find the correlation coefficient: ‘r’
I then find the coefficient of determination: ‘r2’
I then find the t value to test significance of a correlation coefficient using this formula:
t = r * SQRT((n-2)/(1-r^2))
If the value of t is less than the critical value, which is found from a table corresponding to the desired significance level, then the null hypothesis cannot be rejected i.e. there is no relationship. If it’s greater then that level of significance is achieved.
When I use my figures I get a r value of 0.73 and so r^2 = 0.53
When I use n = 1200 it gives a t-value = 36.85
When I look up critical values for two tailed significance the 0.0001 significance level is 3.91 for n = 1000 (this was the largest value for n given).
My questions are:
Is this approach correct?
Because 36.85>>3.91 does this mean that indeed there is a very strong positive correlation and also has a very high level of significance??
Does it matter which table of critical values I use i.e. single or two-tailed critical values?
Any help would be greatly appreciated. Thanks.  | 
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