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 2 replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/ edited Apr 13 '17 at 12:44 There isn't a threshold of correlation that assures you significance of correlation. I suggest reading the answers to "http://stats.stackexchange.com/questions/226005/does-a-statistically-significant-correlation-always-give-predictive-power/226009#226009Does a statistically significant correlation always give predictive power?" where that question was asked in the opposite way. If you want to know significance of correlation you should do a correlation test, that is, a hypothesis test where the null hypothesis is that correlation is zero versus the alternative hypothesis that correlation is not zero. In R you can use cor.test to do that test for a pair of variables. I can't find a built in function to perform the test for all 98 variables but it should be easy to program a loop or use vectors to do it. Anyway, beware of the multiple comparisons problem. You are going to perform nearly 5000 tests at the same time (one for each pair of your 98 variables). Even if your variables were actually independent you would still be expected to get about 250 pairs with p-values below 5%. There isn't a threshold of correlation that assures you significance of correlation. I suggest reading the answers to "http://stats.stackexchange.com/questions/226005/does-a-statistically-significant-correlation-always-give-predictive-power/226009#226009" where that question was asked in the opposite way. If you want to know significance of correlation you should do a correlation test, that is, a hypothesis test where the null hypothesis is that correlation is zero versus the alternative hypothesis that correlation is not zero. In R you can use cor.test to do that test for a pair of variables. I can't find a built in function to perform the test for all 98 variables but it should be easy to program a loop or use vectors to do it. Anyway, beware of the multiple comparisons problem. You are going to perform nearly 5000 tests at the same time (one for each pair of your 98 variables). Even if your variables were actually independent you would still be expected to get about 250 pairs with p-values below 5%. There isn't a threshold of correlation that assures you significance of correlation. I suggest reading the answers to "Does a statistically significant correlation always give predictive power?" where that question was asked in the opposite way. If you want to know significance of correlation you should do a correlation test, that is, a hypothesis test where the null hypothesis is that correlation is zero versus the alternative hypothesis that correlation is not zero. In R you can use cor.test to do that test for a pair of variables. I can't find a built in function to perform the test for all 98 variables but it should be easy to program a loop or use vectors to do it. Anyway, beware of the multiple comparisons problem. You are going to perform nearly 5000 tests at the same time (one for each pair of your 98 variables). Even if your variables were actually independent you would still be expected to get about 250 pairs with p-values below 5%. 1 answered Oct 4 '16 at 8:05 Pere 5,07711 gold badge99 silver badges2424 bronze badges There isn't a threshold of correlation that assures you significance of correlation. I suggest reading the answers to "http://stats.stackexchange.com/questions/226005/does-a-statistically-significant-correlation-always-give-predictive-power/226009#226009" where that question was asked in the opposite way. If you want to know significance of correlation you should do a correlation test, that is, a hypothesis test where the null hypothesis is that correlation is zero versus the alternative hypothesis that correlation is not zero. In R you can use cor.test to do that test for a pair of variables. I can't find a built in function to perform the test for all 98 variables but it should be easy to program a loop or use vectors to do it. Anyway, beware of the multiple comparisons problem. You are going to perform nearly 5000 tests at the same time (one for each pair of your 98 variables). Even if your variables were actually independent you would still be expected to get about 250 pairs with p-values below 5%.