partial_and_semipartial_correlation
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partial_and_semipartial_correlation [2019/10/13 21:10] – [regression gpa against sat] hkimscil | partial_and_semipartial_correlation [2019/10/14 01:03] – [regression gpa against both celp and sat] hkimscil | ||
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10 550 2.9 3.13544 -0.23544 | 10 550 2.9 3.13544 -0.23544 | ||
> | > | ||
- | > round(cor(cor.gpa.sat), | + | round(cor(cor.gpa.sat), |
sat | sat | ||
sat 1.000 0.718 1.000 0.000 | sat 1.000 0.718 1.000 0.000 | ||
Line 123: | Line 123: | ||
resid 0.000 0.696 0.000 1.000 | resid 0.000 0.696 0.000 1.000 | ||
> | > | ||
- | > </ | + | </ |
Note that | Note that | ||
* r (sat and gpa) = .718 (sqrt(r< | * r (sat and gpa) = .718 (sqrt(r< | ||
Line 172: | Line 172: | ||
< | < | ||
# get cor between gpa, sat, pred, and resid from. lm.gpa.clep | # get cor between gpa, sat, pred, and resid from. lm.gpa.clep | ||
- | cor.gpa.clep <- as.data.frame(cbind(gpa, clep, lm.gpa.clep$fitted.values, | + | cor.gpa.clep <- as.data.frame(cbind(clep, gpa, lm.gpa.clep$fitted.values, |
- | colnames(cor.gpa.clep) <- c("gpa", "clep", " | + | colnames(cor.gpa.clep) <- c("clep", "gpa", " |
cor(cor.gpa.clep) | cor(cor.gpa.clep) | ||
</ | </ | ||
- | < | + | < |
- | gpa 1.0000 0.8763 0.8763 0.4818 | + | > round(cor(cor.gpa.clep),4) |
- | clep | + | clep gpa pred resid |
- | pred 0.8763 1.0000 1.0000 0.0000 | + | clep |
- | resid 0.4818 0.0000 0.0000 1.0000 | + | gpa |
- | > </ | + | pred 1.0000 |
+ | resid 0.0000 0.4818 0.0000 1.0000 | ||
+ | > | ||
+ | |||
+ | sat | ||
+ | sat | ||
+ | gpa | ||
+ | pred 1.0000 | ||
+ | resid 0.0000 0.6960 | ||
+ | > | ||
+ | </ | ||
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> | > | ||
</ | </ | ||
+ | |||
+ | '' | ||
+ | '' | ||
+ | |||
+ | '' | ||
+ | '' | ||
+ | '' | ||
+ | |||
+ | One other thing that we could do help determine a pragmatic argument is to regress GPA on both SAT and CLEP at the same time to see what happens. If we do that, we find that R-square for the model is .78, F = 12.25, p < .01. The intercept and b weight for CLEP are both significant, | ||
+ | |||
+ | * '' | ||
+ | * '' | ||
+ | * '' | ||
+ | |||
+ | In this case, we would conclude that the significant unique predictor is CLEP. Although SAT is highly correlated with GPA, it adds nothing to the prediction equation once the CLEP score is entered. (These data are fictional and the sample size is much too small to run this analysis. It's there for illustration only.) | ||
+ | |||
+ | Now suppose we wanted to argue something a little different. Suppose we had a theory that said that all measures of math achievement share a common explanation, | ||
+ | |||
+ | |||
===== checking partial cor 1 ===== | ===== checking partial cor 1 ===== | ||
< | < |
partial_and_semipartial_correlation.txt · Last modified: 2023/05/31 08:56 by hkimscil