diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R
index 639f3e0..724205c 100644
--- a/R/powerAnalysis.R
+++ b/R/powerAnalysis.R
@@ -17,24 +17,27 @@ l2p <- function(b) {
 #Matt: 
 makeDataNew <- function(n) {
   tDF <- data.frame(
-    Group0=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
-    Group1=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt
-    Group2=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
-    Group3=rnorm(n=n, mean=4351.578, sd=1408.811) # age
+    up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
+    mmt=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt
+    ## this generates a 50-50 split of milestones --v
+    #milestones=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
+    milestones=rbinom(n=n, size=1, prob=.247), #milestones
+    age=rnorm(n=n, mean=4351.578, sd=1408.811) # age
   )
   #sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
-  colnames(tDF) <- c('up.fac.mean', 'mmt', 'milestones', 'age')
+  ## can name these in the data.frame constructor method directly
+  #colnames(tDF) <- c('up.fac.mean', 'mmt', 'milestones', 'age')
   return(tDF)
 }
 
 makeDataNew2 <- function(n) {
   tDF <- data.frame(
-    Group0=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
-    Group1=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
+    up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
+    formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
   )
   tDF[is.na(tDF) | tDF=="Inf"] = NA
   #sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
-  colnames(tDF) <- c('up.fac.mean', 'formal.score')
+  ##colnames(tDF) <- c('up.fac.mean', 'formal.score')
   return(tDF)
 }
 
@@ -51,10 +54,10 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
     #have updated for kkex through here, now need to look at the underproduction work
     #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
     m1.sim <- lm(up.fac.mean ~ mmt + milestones + age, data=simData)
-    p0 <- coef(summary(m1.sim))[1,4]
-    p1 <- coef(summary(m1.sim))[1,4]
-    p2 <- coef(summary(m1.sim))[1,4]
-    p3 <- coef(summary(m1.sim))[1,4]
+    p0 <- coef(summary(m1.sim))[1,4] #intercept
+    p1 <- coef(summary(m1.sim))[2,4] #mmt
+    p2 <- coef(summary(m1.sim))[3,4] #milestones
+    p3 <- coef(summary(m1.sim))[4,4] #age
     signif0[s] <- p0 <=.05
     signif1[s] <- p1 <=.05
     signif2[s] <- p2 <=.05
@@ -69,16 +72,14 @@ powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset give
   #set up some empty arrays b/c R
   signif0 <- rep(NA, nSims)
   signif1 <- rep(NA, nSims)
-  signif2 <- rep(NA, nSims)
-  signif3 <- rep(NA, nSims)
   signifM <- rep(NA, nSims)
   for (s in 1:nSims) {           # repeatedly we will....
     simData <- makeDataNew2(n)       # make some data
     #have updated for kkex through here, now need to look at the underproduction work
     #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
     m1.sim <- lm(up.fac.mean ~ formal.score, data=simData)
-    p0 <- coef(summary(m1.sim))[1,2]
-    p1 <- coef(summary(m1.sim))[1,2]
+    p0 <- coef(summary(m1.sim))[1,4] 
+    p1 <- coef(summary(m1.sim))[2,4]
     signif0[s] <- p0 <=.05
     signif1[s] <- p1 <=.05
     signifM[s] <- p0 <=.05 & p1 <=.05