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--- r:multiple_regression [2019/11/08 10:59] – [Prediction] hkimscil
+++ r:multiple_regression [2020/08/31 12:31] – hkimscil
@@ Line 534: / Line 534: @@
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 > </code>
+====== e.g. 5 ======
+<code>
+#packages we will need to conduct to create and graph our data
+library(MASS) #create data
+library(car) #graph data
+py1 =.6 #Cor between X1 (Practice Time) and Memory Errors
+py2 =.4 #Cor between X2 (Performance Anxiety) and Memory Errors
+p12= .3 #Cor between X1 (Practice Time) and X2 (Performance Anxiety)
+Means.X1X2Y<- c(10,10,10) #set the means of X and Y variables
+CovMatrix.X1X2Y <- matrix(c(1,p12,py1,
+                            p12,1,py2,
+                            py1,py2,1),3,3) # creates the covariate matrix
+#build the correlated variables. Note: empirical=TRUE means make the correlation EXACTLY r.
+# if we say empirical=FALSE, the correlation would be normally distributed around r
+set.seed(42)
+CorrDataT<-mvrnorm(n=100, mu=Means.X1X2Y,Sigma=CovMatrix.X1X2Y, empirical=TRUE)
+#Convert them to a "Data.Frame" & add our labels to the vectors we created
+CorrDataT<-as.data.frame(CorrDataT)
+colnames(CorrDataT) <- c("Practice","Anxiety","Memory")
+#make the scatter plots
+scatterplot(Memory~Practice,CorrDataT, smoother=FALSE)
+scatterplot(Memory~Anxiety,CorrDataT, smoother=FALSE)
+scatterplot(Anxiety~Practice,CorrDataT, smoother=FALSE)
+# Pearson Correlations
+ry1<-cor(CorrDataT$Memory,CorrDataT$Practice)
+ry2<-cor(CorrDataT$Memory,CorrDataT$Anxiety)
+r12<-cor(CorrDataT$Anxiety,CorrDataT$Practice)
+</code>