diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R
index 3c7d440..f4f205c 100644
--- a/R/powerAnalysis.R
+++ b/R/powerAnalysis.R
@@ -17,14 +17,8 @@ l2p <- function(b) {
 #Matt: 
 makeDataNew <- function(n) {
   sDF <- data.frame(
-    sample(2793.638:3066.417, 1, replace = FALSE),
-    sample(27.6519:154.6866, 1, replace = FALSE),
-    sample(50.01884:96.77090, 1, replace = FALSE),
-    sample(c(1,2), 1, prob=(c(0.5,0.5)), replace = FALSE),
-    sample(1.610638: 1.684438, 1, replace = FALSE),
-    sample(-0.1961401:-0.1647757, 1, replace = FALSE)
   )
-  colnames(sDF) <- c('age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up.fac.mean')
+  colnames(sDF) <- c('formality', 'age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up.fac.mean')
   return(sDF)
 }
 
@@ -38,8 +32,7 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
   for (s in 1:nSims) {           # repeatedly we will....
     simData <- makeDataNew(n)       # make some data
     #have updated for kkex through here, now need to look at the underproduction work
-    m1.sim <- glm(up.fac.mean ~ ((mmt)/ (milestones/age)),   # give the anticipated regression a try
-                  family=gaussian(link='identity'), 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]