I am trying to learn a bit of R and statistics so as "toy project" I have started from the famous Iris dataset (150 rows, 3 classes). I have applied to two features the "correlation test" in this way:
cor.test(iris$Sepal.Length, iris$Petal.Length) # # Pearson's product-moment correlation # # data: iris$Sepal.Length and iris$Petal.Length # t = 21.646, df = 148, p-value < 2.2e-16 # alternative hypothesis: true correlation is not equal to 0 # 95 percent confidence interval: # 0.8270368 0.9055083 # sample estimates: # cor # 0.8717542
which uses by default the Pearson's correlation.
Based on 2 hypothesis how do I interpret the confidence interval? I tried to use a t-table but I am still not sure if the value "0.8717542" is a good value or not based on the df.
I am using the t-table here (pdf).
I cannot see the DF = 148 but from 100 to 1000 looks there is not too much shift. So is it correct if I affirm that the interval of confidence is between 60% and 70% and based on that, there is an acceptable positive correlation between the two features?