= read.csv("~/Papers/22_Ochem_Models_across_the_Curriculum/Analysis/Modelusage_cleandataset_July2022_v2.csv",header = TRUE)
allData which(allData$Exam..2 == "EX"),]$Exam..2 = NA
allData[$Exam..2 = as.numeric(allData$Exam..2)
allDatawhich(allData$Exam..3 == "EX"),]$Exam..3 = NA
allData[$Exam..3 = as.numeric(allData$Exam..3)
allData#for some reason, theres a lot of different types of answers for the main question: "Did you use the model".
# making a decision that if it's not yes, its no.
which(allData$Did.you.use.the.model.kit. == "N/A"),]$Did.you.use.the.model.kit. = "N"
allData[which(allData$Did.you.use.the.model.kit. == ""),]$Did.you.use.the.model.kit. = "N"
allData[which(allData$Did.you.use.the.model.kit. == "No answer"),]$Did.you.use.the.model.kit. = "N"
allData[which(allData$Did.you.use.the.model.kit. == "Both"),]$Did.you.use.the.model.kit. = "Y"
allData[
$Did.you.use.the.model.kit. = gsub("\\s+", "", allData$Did.you.use.the.model.kit.)
allData
= allData[which(allData$Question == "Newman Projection (1.1)"),]
newman = allData[which(allData$Question == "Diastereomers model (1.6)"),]
diaste = allData[which(allData$Question == "Diastereomer Meso (1.3)"),]
dimeso = allData[which(allData$Question == "Enantiomers Ring (1.2)"),]
enanto = allData[which(allData$Question == "Diastereomers Wedge and Dash (1.5)"),]
didash #assuming the order of students is the same in allData
#all(newman$ID == diaste$ID )
#all(newman$ID == dimeso$ID )
#all(newman$ID == enanto$ID )
#all(newman$ID == didash$ID )
= newman[,c(1:11)]
students
= function(df){
makeColumn4ModelUse = c()
column for (i in 1:nrow(df)){
if ( df$Did.you.use.the.model.kit.[i] == "Y" ){
="Using"
anselse if ( df$I.preferred.to.use.other.methods..R.and.S[i] == "Y" ||
} $I.preferred.to.use.other.methods..Visualizing.in.my.head[i] == "Y"){
df="NotNeed"
anselse{
}="NotUsing"
ans
}= append(column,ans)
column
}return(column)
}= function(df){
makeColumn4ModelUse2 = c()
column for (i in 1:nrow(df)){
if ( df$Did.you.use.the.model.kit.[i] == "Y" ){
="Using"
anselse{
}="NotUsing"
ans
}= append(column,ans)
column
}return(column)
}= function(df){
makeColumn4ModelUseCorrect = c()
column for (i in 1:nrow(df)){
if ( df$Did.you.use.the.model.kit.[i] == "Y" ){
if (df$Question.correct[i] == "correct"){
= "Using Correct"
ans else{
}= "Using Incorrect"
ans
}else{
}if (df$Question.correct[i] == "correct"){
= "Notusing Correct"
ans else{
}= "Notusing Incorrect"
ans
}
}= append(column,ans)
column
}return(column)
}= function(df,cat){
getExamBlock = max(df[[cat]])
high = min(df[[cat]])
low = high-low
range = c()
grade for (i in 1:nrow(df)){
=df[[cat]][i]
xif (x > low+range*0.8){
= append(grade,100)
grade else if (x > low+range*0.6){
}= append(grade,80)
grade else if (x > low+range*0.4){
}= append(grade,40)
grade else if (x > low+range*0.2){
}= append(grade,20)
grade else {
}= append(grade,0)
grade
}
}return(grade)
}
$newman = makeColumn4ModelUse(newman)
students$diaste = makeColumn4ModelUse(diaste)
students$enanto = makeColumn4ModelUse(enanto)
students$didash = makeColumn4ModelUse(didash)
students$dimeso = makeColumn4ModelUse(dimeso)
students
$newman2= makeColumn4ModelUse2(newman)
students$diaste2= makeColumn4ModelUse2(diaste)
students$enanto2= makeColumn4ModelUse2(enanto)
students$didash2= makeColumn4ModelUse2(didash)
students$dimeso2= makeColumn4ModelUse2(dimeso)
students
$newmanComb= makeColumn4ModelUseCorrect(newman)
students$diasteComb= makeColumn4ModelUseCorrect(diaste)
students$enantoComb= makeColumn4ModelUseCorrect(enanto)
students$didashComb= makeColumn4ModelUseCorrect(didash)
students$dimesoComb= makeColumn4ModelUseCorrect(dimeso)
students
$newmanQ= newman$Question.correct
students$diasteQ= diaste$Question.correct
students$enantoQ= enanto$Question.correct
students$didashQ= didash$Question.correct
students$dimesoQ= dimeso$Question.correct
students#students$Exam1block = getExamBlock(newman,"Exam..1")
write.csv(students,file = "out.csv")
= function(df){
addAnswerColumn
$answers = NA
dffor (i in 1:nrow(df)){
if ( df$Question.correct[i] == "correct" & df$Did.you.use.the.model.kit.[i] == "Y" ) { df$answers[i] = "Yes&Yes"}
if ( df$Question.correct[i] != "correct" & df$Did.you.use.the.model.kit.[i] == "Y" ) { df$answers[i] = "No&Yes"}
if ( df$Question.correct[i] == "correct" & df$Did.you.use.the.model.kit.[i] != "Y" ) { df$answers[i] = "Yes&No"}
if ( df$Question.correct[i] != "correct" & df$Did.you.use.the.model.kit.[i] != "Y" ) { df$answers[i] = "No&No"}
}= df[order(df$answers),]
df return(df)
}
library(ggplot2)
library(ggpubr)
library(psych)
= function(df,myx,myy,mytitle,myylab){
plotGGbox = df[complete.cases(df[[myy]]),]
df = max(df[[myy]])
maxy ggboxplot(df, x = myx, y = myy,
title = mytitle,
color = myx, add = "jitter", legend="none",ylab = myylab) + rotate_x_text(angle = 45) +
geom_hline( yintercept = mean(df[[myy]]), linetype = 2) +
stat_compare_means(method = "anova", label.y = maxy*1.10) +
coord_cartesian(ylim = c(0, maxy*1.2)) +
stat_compare_means(label = "p.format", size=2.5, method = "t.test", ref.group = ".all.",label.y = maxy*1.05)
}= function(df,myx,myy,mytitle,myylab){
getAnova #get anova
<- TukeyHSD( aov(df[[myy]] ~ df[[myx]]))
a<-as.data.frame(a$`df[[myx]]`[,4])
bcolnames(b) = c("Testing statistical significance: p-values")
print(knitr::kable(b, caption = paste("Anova: ",mytitle)))
}= function(df,myx,myy,mytitle,myylab){
plotAndTable print(plotGGbox(df,myx,myy,mytitle,myylab))
= describeBy(df[[myy]],df[[myx]],mat=TRUE,digits = 2)
table print(knitr::kable(table[,c(2,4,5,6,7,10,11,12)],caption=paste("Statistics of ",myylab," based on getting the question correct (Yes/No) & using the models (Yes/No)")))
getAnova(df,myx,myy,mytitle,myylab)
}
library(dplyr)
library(corrplot)
= function(a){
plotChi #I need to use droplevels otherwise it was showing Expert with zeros as a ghost category?
=chisq.test(table(droplevels(a)))
bcat(paste("<p><b>The Chi-square analysis gives a p=",round(b$p.value,5),"</b></p>"))
cat(paste("<p><b>Residuals analysis:</b></p>"))
cat("A negative residual implies that the measured value is lower than expected and a positive value higher than expected</br>")
corrplot(b$residuals, is.cor = FALSE)
}= function(df,myx,myy,myxlabel,myylabel,mytitle){
plotBarAndCorr #myx is the course or demographic variable, the independent variable
#myy is typically the clusterLetter, the dependent variable
#remove experts, not useful for the chisquare analysis
#select the two categorical variables
= df[,c(myy,myx)]
a print(plotBarCategories(a,myx,myy,myxlabel,myylabel,mytitle))
plotChi(a)
}= function(a,myx,myy,myxlabel,myylabel,mytitle){
plotBarCategories #using aes_string instead of aes because colnames are variables
#ggplot(a, aes_string(x=myx,fill=myy)) + geom_bar()
%>%
a count(!!sym(myy),!!sym(myx)) %>%
group_by(!!sym(myx)) %>%
mutate(lab = paste0(round(prop.table(n) * 100, 2), '%')) %>%
ggplot(aes(!!sym(myx),n, fill=!!sym(myy))) +
geom_col() + geom_text(aes(label=lab),position='stack',vjust=1.5) +
labs(x=myxlabel,y=myylabel,title=mytitle)
}
Newman | Diastereomer | Enantiomer Ring | Wedge Dash | Diastereomer Meso |
From the table below we can see:
= function(df){
calcDistribution = nrow(df)
n = sum(df$Did.you.use.the.model.kit. == "Y" )
using = sum(df$Did.you.use.the.model.kit. == "Y" & df$Question.correct == "correct")
usingCorrect = using - usingCorrect
usingIncorrect = n - using
notusing = sum(df$Did.you.use.the.model.kit. != "Y" & df$Question.correct == "correct")
notusingCorrect = notusing - notusingCorrect
notusingIncorrect return(
list(
c(using,usingCorrect,usingIncorrect,notusing,notusingCorrect,notusingIncorrect),
c( "%" ,signif(usingCorrect/using*100,digits = 2), signif(usingIncorrect/using*100,digits = 2),"%",signif(notusingCorrect/notusing*100,digits = 2),signif(notusingIncorrect/notusing*100,digits = 2))
)
)
}
= data.frame(matrix(ncol=0,nrow=6))
distribution
$Newman = unlist( calcDistribution(newman))[1:6]
distribution$NewmanPercent = unlist( calcDistribution(newman))[7:12]
distribution=chisq.test(table(newman$Did.you.use.the.model.kit.,newman$Question.correct))
a$NewmanChiSquare = c("Chi-squared","p = ",round(a$p.value,4)," "," "," ")
distribution
$Diastereomer = unlist( calcDistribution(diaste))[1:6]
distribution$DiastereomerPercent = unlist( calcDistribution(diaste))[7:12]
distribution=chisq.test(table(diaste$Did.you.use.the.model.kit.,diaste$Question.correct))
a$DiasteChiSquare = c("Chi-squared","p = ",round(a$p.value,4)," "," "," ")
distribution
$EnantiomerRing = unlist( calcDistribution(enanto))[1:6]
distribution$EnantiomerRingPercert = unlist( calcDistribution(enanto))[7:12]
distribution=chisq.test(table(enanto$Did.you.use.the.model.kit.,enanto$Question.correct))
a$EnantoChiSquare = c("Chi-squared","p = ",round(a$p.value,4)," "," "," ")
distribution
$WedgeDash = unlist( calcDistribution(didash))[1:6]
distribution$WedgeDashPercent = unlist( calcDistribution(didash))[7:12]
distribution=chisq.test(table(didash$Did.you.use.the.model.kit.,didash$Question.correct))
a$DidashChiSquare = c("Chi-squared","p = ",round(a$p.value,4)," "," "," ")
distribution
$DiastereomerMeso = unlist( calcDistribution(dimeso))[1:6]
distribution$DiastereomerMesoPercent = unlist( calcDistribution(dimeso))[7:12]
distribution=chisq.test(table(dimeso$Did.you.use.the.model.kit.,dimeso$Question.correct))
a$DimesoChiSquare = c("Chi-squared","p = ",round(a$p.value,4)," "," "," ")
distribution
rownames(distribution) = c("Using Models N-Total","Using Models N-Correct","Using Models N-Incorrect",
"Not Using N-Total","Not Using N-Correct","Not Using N-Incorrect")
::kable(t(distribution),digits = 1) knitr
Using Models N-Total | Using Models N-Correct | Using Models N-Incorrect | Not Using N-Total | Not Using N-Correct | Not Using N-Incorrect | |
---|---|---|---|---|---|---|
Newman | 80 | 59 | 21 | 70 | 46 | 24 |
NewmanPercent | % | 74 | 26 | % | 66 | 34 |
NewmanChiSquare | Chi-squared | p = | 0.3719 | |||
Diastereomer | 80 | 61 | 19 | 70 | 44 | 26 |
DiastereomerPercent | % | 76 | 24 | % | 63 | 37 |
DiasteChiSquare | Chi-squared | p = | 0.108 | |||
EnantiomerRing | 36 | 24 | 12 | 114 | 72 | 42 |
EnantiomerRingPercert | % | 67 | 33 | % | 63 | 37 |
EnantoChiSquare | Chi-squared | p = | 0.8546 | |||
WedgeDash | 94 | 64 | 30 | 56 | 37 | 19 |
WedgeDashPercent | % | 68 | 32 | % | 66 | 34 |
DidashChiSquare | Chi-squared | p = | 0.9407 | |||
DiastereomerMeso | 49 | 35 | 14 | 101 | 75 | 26 |
DiastereomerMesoPercent | % | 71 | 29 | % | 74 | 26 |
DimesoChiSquare | Chi-squared | p = | 0.8645 |
We will show again the same information as in the table above but with graphs. The idea is that even if the Chi-square test gives us no significance, in some cases there are trends that must be recognized
plotBarAndCorr(newman,"Did.you.use.the.model.kit.","Question.correct","Using Models","N of students","Newman projection question")
The Chi-square analysis gives a p= 0.37193
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(diaste,"Did.you.use.the.model.kit.","Question.correct","Using Models","N of students","Diastereomer question")
The Chi-square analysis gives a p= 0.10802
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(enanto,"Did.you.use.the.model.kit.","Question.correct","Using Models","N of students","Enantiomer ring question")
The Chi-square analysis gives a p= 0.85463
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(didash,"Did.you.use.the.model.kit.","Question.correct","Using Models","N of students","Wedge Dash question")
The Chi-square analysis gives a p= 0.9407
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(dimeso,"Did.you.use.the.model.kit.","Question.correct","Using Models","N of students","Diastereomer Meso question")
The Chi-square analysis gives a p= 0.86454
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
We define the group “Dont need models” those students who not using models they replied that it is because either they visualize it in their head or they’re using the R/S approach.
With the performance of these three groups shown in the table below, we can extract the following observations:
= function(df){
calcDistribution3 = nrow(df)
n = sum(df$Did.you.use.the.model.kit. == "Y" )
using = sum(df$Did.you.use.the.model.kit. == "Y" & df$Question.correct == "correct")
usingCorrect = using - usingCorrect
usingIncorrect
= sum(
notNeeded $I.preferred.to.use.other.methods..R.and.S == "Y" | df$I.preferred.to.use.other.methods..Visualizing.in.my.head == "Y")
df=
notNeededCorrect sum( (df$I.preferred.to.use.other.methods..R.and.S == "Y" | df$I.preferred.to.use.other.methods..Visualizing.in.my.head == "Y") &
$Question.correct == "correct"
df
)= notNeeded - notNeededCorrect
notNeededIncorrect
= n - using - notNeeded
notusing =
notusingCorrect sum(
$Did.you.use.the.model.kit. != "Y" &
df$I.preferred.to.use.other.methods..R.and.S != "Y" & df$I.preferred.to.use.other.methods..Visualizing.in.my.head != "Y") &
( df$Question.correct == "correct"
df
)= notusing - notusingCorrect
notusingIncorrect
= matrix(c(usingCorrect,usingIncorrect,notNeededCorrect,notNeededIncorrect,notusingCorrect,notusingIncorrect),nrow = 3,ncol = 2,byrow = TRUE)
mymat =chisq.test(mymat)
a# I cannot return a vector because it is a combination of characters and numbers
return(
list(
c(using,usingCorrect,usingIncorrect,notNeeded,notNeededCorrect,notNeededIncorrect, notusing,notusingCorrect,notusingIncorrect),
c( "%" ,signif(usingCorrect/using*100,digits = 2), signif(usingIncorrect/using*100,digits = 2),
"%",signif(notNeededCorrect/notNeeded*100,digits = 2),signif(notNeededIncorrect/notNeeded*100,digits = 2),
"%",signif(notusingCorrect/notusing*100,digits = 2),signif(notusingIncorrect/notusing*100,digits = 2)),
c(a$p.value)
)
)
}
= data.frame(matrix(ncol=0,nrow=9))
distribution3
=unlist( calcDistribution3(newman))
a$Newman = a[1:9]
distribution3$NewmanPercent = a[10:18]
distribution3$NewmanChiSquare = c("Chi-squared","p = ",round(as.numeric(a[19]),4)," "," "," "," "," "," ")
distribution3
=unlist( calcDistribution3(diaste))
a$Diastereomer = a[1:9]
distribution3$DiastereomerPercent = a[10:18]
distribution3$DiasteChiSquare = c("Chi-squared","p = ",round(as.numeric(a[19]),4)," "," "," "," "," "," ")
distribution3
=unlist( calcDistribution3(enanto))
a$EnantiomerRing = a[1:9]
distribution3$EnantiomerRingPercert = a[10:18]
distribution3$EnantiomerChiSquare = c("Chi-squared","p = ",round(as.numeric(a[19]),4)," "," "," "," "," "," ")
distribution3
=unlist( calcDistribution3(didash))
a$WedgeDash = a[1:9]
distribution3$WedgeDashPercent = a[10:18]
distribution3$WedgeDashChiSquare = c("Chi-squared","p = ",round(as.numeric(a[19]),4)," "," "," "," "," "," ")
distribution3
=unlist( calcDistribution3(dimeso))
a$DiastereomerMeso = a[1:9]
distribution3$DiastereomerMesoPercent = a[10:18]
distribution3$DiastereomerChiSquare = c("Chi-squared","p = ",round(as.numeric(a[19]),4)," "," "," "," "," "," ")
distribution3
rownames(distribution3) = c("Using Models N-Total","Using Models N-Correct","Using Models N-Incorrect",
"Not Needed N-Total","Not Needed N-Correct","Not Needed N-Incorrect",
"Not Using N-Total","Not Using N-Correct","Not Using N-Incorrect")
::kable(t(distribution3),digits = 1) knitr
Using Models N-Total | Using Models N-Correct | Using Models N-Incorrect | Not Needed N-Total | Not Needed N-Correct | Not Needed N-Incorrect | Not Using N-Total | Not Using N-Correct | Not Using N-Incorrect | |
---|---|---|---|---|---|---|---|---|---|
Newman | 80 | 59 | 21 | 26 | 21 | 5 | 44 | 27 | 17 |
NewmanPercent | % | 74 | 26 | % | 81 | 19 | % | 61 | 39 |
NewmanChiSquare | Chi-squared | p = | 0.1738 | ||||||
Diastereomer | 80 | 61 | 19 | 28 | 21 | 7 | 42 | 24 | 18 |
DiastereomerPercent | % | 76 | 24 | % | 75 | 25 | % | 57 | 43 |
DiasteChiSquare | Chi-squared | p = | 0.0757 | ||||||
EnantiomerRing | 36 | 24 | 12 | 54 | 40 | 14 | 60 | 34 | 26 |
EnantiomerRingPercert | % | 67 | 33 | % | 74 | 26 | % | 57 | 43 |
EnantiomerChiSquare | Chi-squared | p = | 0.1466 | ||||||
WedgeDash | 94 | 64 | 30 | 13 | 11 | 2 | 43 | 27 | 16 |
WedgeDashPercent | % | 68 | 32 | % | 85 | 15 | % | 63 | 37 |
WedgeDashChiSquare | Chi-squared | p = | 0.3352 | ||||||
DiastereomerMeso | 49 | 35 | 14 | 50 | 40 | 10 | 51 | 36 | 15 |
DiastereomerMesoPercent | % | 71 | 29 | % | 80 | 20 | % | 71 | 29 |
DiastereomerChiSquare | Chi-squared | p = | 0.4935 |
plotBarAndCorr(students,"newman","newmanQ","Using Models","N of students","Newman projection question")
The Chi-square analysis gives a p= 0.11676
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"diaste","diasteQ","Using Models","N of students","Diastereomer question")
The Chi-square analysis gives a p= 0.05442
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"enanto","enantoQ","Using Models","N of students","Enantiomer ring question")
The Chi-square analysis gives a p= 0.07037
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"didash","didashQ","Using Models","N of students","Wedge Dash question")
The Chi-square analysis gives a p= 0.34409
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"dimeso","dimesoQ","Using Models","N of students","Diastereomer Meso question")
The Chi-square analysis gives a p= 0.16913
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
import pandas as pd
import plotly.express as px
= pd.read_csv("~/Papers/22_Ochem_Models_across_the_Curriculum/Analysis/out.csv")
students = students[["didash","diaste","newman","dimeso","enanto","Exam..1"]]
modelUse = modelUse.sort_values(by=['didash','diaste','newman','dimeso','enanto'], axis=0, ascending=False)
sortedModelUse = px.parallel_categories(sortedModelUse,
fig ="Exam..1",
color#color_continuous_scale='Bluered_r',
={
labels"didash":"Wedge/Dash",
"diaste":"Diastereomers",
"newman":"Newman",
"dimeso":"Diastereomer Meso",
"enanto":"Enantiomer Ring",
}) fig.show()
= students[["didash2","diaste2","newman2","dimeso2","enanto2","Exam..1"]]
modelUse = modelUse.sort_values(by=['didash2','diaste2','newman2','dimeso2','enanto2'], axis=0, ascending=False)
sortedModelUse = px.parallel_categories(sortedModelUse,
fig ="Exam..1",
color#color_continuous_scale='Bluered_r',
={
labels"didash2":"Wedge/Dash",
"diaste2":"Diastereomers",
"newman2":"Newman",
"dimeso2":"Diastereomer Meso",
"enanto2":"Enantiomer Ring",
}) fig.show()
= students[["didashComb","diasteComb","newmanComb","dimesoComb","enantoComb","Exam..1"]]
modelUse = modelUse.sort_values(by=['didashComb','diasteComb','newmanComb','dimesoComb','enantoComb'], axis=0, ascending=False)
sortedModelUse = px.parallel_categories(sortedModelUse,
fig ="Exam..1",
color#color_continuous_scale='Bluered_r',
={
labels"didashComb":"Wedge/Dash",
"diasteComb":"Diastereomers",
"newmanComb":"Newman",
"dimesoComb":"Diastereomer Meso",
"enantoComb":"Enantiomer Ring",
}) fig.show()
#maybe this can be done easier with parallel categories https://plotly.com/python/parallel-categories-diagram/
= c()
mysource for (i in 0:11){
for (j in 0:2){
= append(source,i)
mysource
}
}= c(3,4,5,3,4,5,3,4,5,
mytarget 6,7,8,6,7,8,6,7,8,
9,10,11,9,10,11,9,10,11,
12,13,14,12,13,14,12,13,14)
= rep(1,each=45)
myvalue #order from most used to least used
= c("didash","newman","diaste","dimeso","enanto")
exercises
= c()
myvalue for (i in 2:length(exercises)){
print(exercises[i])
}
library(plotly)
<- plot_ly(
fig type = "sankey",
orientation = "h",
node = list(
label = c("Using", "Not Using", "Not Needed",
"Using", "Not Using", "Not Needed",
"Using", "Not Using", "Not Needed",
"Using", "Not Using", "Not Needed",
"Using", "Not Using", "Not Needed"
),color = rep("blue",each=15),
pad = 15,
thickness = 20,
line = list(
color = "black",
width = 0.5
)
),
link = list(
source = mysource,
target = mytarget,
value = myvalue
)
)<- fig %>% layout(
fig title = "Students Model Use Through Exercises",
font = list(
size = 10
)
)
fig
The relevant data that we are going to test against course performance for each exercise is:
Very few students reply “no” to the question “have you found the models helpful”, so the statistics would be skewed. It may be better to just analyze whether they used them or not.
The p-values shown in the boxplot are not an ANOVA analysis. Rather it is a t-test of that group against the rest of students.
= addAnswerColumn(newman)
newman plotAndTable(newman,"answers","Exam..1","Exam 1: Was the question correct? Did they use the models?","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 24 | 33.26 | 8.79 | 36.38 | 19.75 | 44.75 | 25.00 |
X12 | No&Yes | 21 | 35.15 | 6.51 | 35.50 | 24.00 | 44.25 | 20.25 |
X13 | Yes&No | 46 | 41.70 | 5.35 | 41.88 | 26.50 | 49.50 | 23.00 |
X14 | Yes&Yes | 59 | 41.71 | 7.23 | 43.75 | 22.75 | 49.75 | 27.00 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.7943966 |
Yes&No-No&No | 0.0000178 |
Yes&Yes-No&No | 0.0000073 |
Yes&No-No&Yes | 0.0024101 |
Yes&Yes-No&Yes | 0.0014855 |
Yes&Yes-Yes&No | 0.9999998 |
plotAndTable(newman,"answers","Exam..2","Exam 2: Was the question correct? Did they use the models?","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 23 | 36.76 | 6.52 | 37.40 | 23.30 | 46.0 | 22.70 |
X12 | No&Yes | 21 | 36.32 | 7.42 | 35.75 | 20.40 | 47.5 | 27.10 |
X13 | Yes&No | 45 | 40.98 | 6.04 | 41.15 | 19.75 | 49.5 | 29.75 |
X14 | Yes&Yes | 59 | 41.92 | 5.66 | 43.75 | 22.50 | 50.0 | 27.50 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.9954546 |
Yes&No-No&No | 0.0422499 |
Yes&Yes-No&No | 0.0048311 |
Yes&No-No&Yes | 0.0254227 |
Yes&Yes-No&Yes | 0.0027572 |
Yes&Yes-Yes&No | 0.8683489 |
plotAndTable(newman,"answers","Exam..3","Exam 3: Was the question correct? Did they use the models?","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 24 | 31.95 | 11.25 | 35.17 | 7.50 | 45.50 | 38.00 |
X12 | No&Yes | 20 | 28.43 | 10.21 | 28.50 | 11.50 | 47.05 | 35.55 |
X13 | Yes&No | 46 | 37.68 | 9.13 | 39.15 | 0.00 | 49.50 | 49.50 |
X14 | Yes&Yes | 58 | 39.81 | 8.31 | 41.35 | 19.75 | 50.00 | 30.25 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.5997337 |
Yes&No-No&No | 0.0746253 |
Yes&Yes-No&No | 0.0038671 |
Yes&No-No&Yes | 0.0017343 |
Yes&Yes-No&Yes | 0.0000361 |
Yes&Yes-Yes&No | 0.6592264 |
plotAndTable(newman,"answers","Final.Exam","Final Exam: Was the question correct? Did they use the models?","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 24 | 59.95 | 15.85 | 62.25 | 36.00 | 90.00 | 54.00 |
X12 | No&Yes | 21 | 54.89 | 16.59 | 54.25 | 29.25 | 82.75 | 53.50 |
X13 | Yes&No | 46 | 70.46 | 17.72 | 70.88 | 0.00 | 92.75 | 92.75 |
X14 | Yes&Yes | 59 | 74.71 | 16.29 | 77.00 | 34.50 | 99.00 | 64.50 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.7422885 |
Yes&No-No&No | 0.0648681 |
Yes&Yes-No&No | 0.0020852 |
Yes&No-No&Yes | 0.0030441 |
Yes&Yes-No&Yes | 0.0000410 |
Yes&Yes-Yes&No | 0.5695576 |
plotAndTable(newman,"answers","Quizzes.Final.Score","Quizzes: Was the question correct? Did they use the models?","Quizzes Average Score")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 24 | 76.31 | 14.80 | 80.56 | 53.44 | 99.44 | 46.00 |
X12 | No&Yes | 21 | 71.67 | 16.07 | 72.78 | 46.39 | 97.78 | 51.39 |
X13 | Yes&No | 46 | 81.04 | 16.17 | 82.37 | 30.00 | 103.33 | 73.33 |
X14 | Yes&Yes | 59 | 87.54 | 13.77 | 90.63 | 51.39 | 105.56 | 54.17 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.7300690 |
Yes&No-No&No | 0.5963749 |
Yes&Yes-No&No | 0.0128058 |
Yes&No-No&Yes | 0.0880143 |
Yes&Yes-No&Yes | 0.0003173 |
Yes&Yes-Yes&No | 0.1281489 |
= addAnswerColumn(diaste)
diaste plotAndTable(diaste,"answers","Exam..1","Exam 1: Was the question correct? Did they use the models?","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 34.61 | 7.22 | 36.50 | 21.50 | 47.50 | 26.00 |
X12 | No&Yes | 19 | 35.36 | 6.98 | 35.50 | 19.75 | 48.00 | 28.25 |
X13 | Yes&No | 44 | 40.47 | 7.27 | 41.38 | 19.75 | 49.75 | 30.00 |
X14 | Yes&Yes | 61 | 42.02 | 7.01 | 44.00 | 22.75 | 49.75 | 27.00 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.9853766 |
Yes&No-No&No | 0.0059834 |
Yes&Yes-No&No | 0.0001015 |
Yes&No-No&Yes | 0.0476465 |
Yes&Yes-No&Yes | 0.0027828 |
Yes&Yes-Yes&No | 0.6919621 |
plotAndTable(diaste,"answers","Exam..2","Exam 2: Was the question correct? Did they use the models?","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 24 | 37.10 | 6.50 | 37.10 | 19.75 | 49.0 | 29.25 |
X12 | No&Yes | 19 | 38.42 | 6.00 | 36.70 | 23.30 | 47.5 | 24.20 |
X13 | Yes&No | 44 | 39.88 | 6.45 | 40.58 | 20.40 | 49.5 | 29.10 |
X14 | Yes&Yes | 61 | 41.82 | 6.35 | 44.25 | 22.50 | 50.0 | 27.50 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.9063228 |
Yes&No-No&No | 0.3161622 |
Yes&Yes-No&No | 0.0130503 |
Yes&No-No&Yes | 0.8370842 |
Yes&Yes-No&Yes | 0.1794463 |
Yes&Yes-Yes&No | 0.4137197 |
plotAndTable(diaste,"answers","Exam..3","Exam 3: Was the question correct? Did they use the models?","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 29.88 | 12.93 | 33.65 | 0.00 | 50.0 | 50.00 |
X12 | No&Yes | 18 | 32.94 | 8.02 | 31.32 | 17.25 | 45.5 | 28.25 |
X13 | Yes&No | 44 | 37.47 | 8.65 | 38.52 | 15.75 | 50.0 | 34.25 |
X14 | Yes&Yes | 60 | 39.32 | 8.91 | 41.02 | 14.90 | 49.8 | 34.90 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.7237166 |
Yes&No-No&No | 0.0088721 |
Yes&Yes-No&No | 0.0002696 |
Yes&No-No&Yes | 0.3321327 |
Yes&Yes-No&Yes | 0.0676680 |
Yes&Yes-Yes&No | 0.7658246 |
plotAndTable(diaste,"answers","Final.Exam","Final Exam: Was the question correct? Did they use the models?","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 57.58 | 19.79 | 57.75 | 0.00 | 99 | 99.00 |
X12 | No&Yes | 19 | 61.64 | 14.45 | 56.75 | 41.50 | 90 | 48.50 |
X13 | Yes&No | 44 | 69.45 | 15.02 | 68.50 | 37.00 | 94 | 57.00 |
X14 | Yes&Yes | 61 | 74.04 | 18.14 | 77.00 | 29.25 | 96 | 66.75 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.8610101 |
Yes&No-No&No | 0.0295541 |
Yes&Yes-No&No | 0.0004023 |
Yes&No-No&Yes | 0.3504037 |
Yes&Yes-No&Yes | 0.0337593 |
Yes&Yes-Yes&No | 0.5322270 |
plotAndTable(diaste,"answers","Quizzes.Final.Score","Quizzes: Was the question correct? Did they use the models?","Quizzes Average Score")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 72.15 | 17.57 | 71.78 | 30.00 | 105.56 | 75.56 |
X12 | No&Yes | 19 | 77.67 | 13.12 | 77.19 | 56.11 | 100.00 | 43.89 |
X13 | Yes&No | 44 | 81.57 | 15.63 | 85.62 | 43.13 | 104.44 | 61.31 |
X14 | Yes&Yes | 61 | 86.70 | 14.30 | 90.56 | 46.94 | 104.44 | 57.50 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.6245966 |
Yes&No-No&No | 0.0624727 |
Yes&Yes-No&No | 0.0004001 |
Yes&No-No&Yes | 0.7849585 |
Yes&Yes-No&Yes | 0.1105684 |
Yes&Yes-Yes&No | 0.3217175 |
= addAnswerColumn(enanto)
enanto plotAndTable(enanto,"answers","Exam..1","Exam 1: Was the question correct? Did they use the models?","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 42 | 33.29 | 7.08 | 34.62 | 19.75 | 43.50 | 23.75 |
X12 | No&Yes | 12 | 38.46 | 5.27 | 39.50 | 30.75 | 46.50 | 15.75 |
X13 | Yes&No | 72 | 41.71 | 6.41 | 42.38 | 20.75 | 49.75 | 29.00 |
X14 | Yes&Yes | 24 | 43.85 | 6.77 | 46.88 | 25.50 | 49.75 | 24.25 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.0817935 |
Yes&No-No&No | 0.0000000 |
Yes&Yes-No&No | 0.0000000 |
Yes&No-No&Yes | 0.3909838 |
Yes&Yes-No&Yes | 0.0989274 |
Yes&Yes-Yes&No | 0.5140672 |
plotAndTable(enanto,"answers","Exam..2","Exam 2: Was the question correct? Did they use the models?","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 41 | 36.35 | 6.25 | 35.95 | 19.75 | 46.15 | 26.40 |
X12 | No&Yes | 12 | 39.68 | 6.16 | 39.92 | 25.50 | 47.75 | 22.25 |
X13 | Yes&No | 71 | 41.31 | 5.90 | 42.50 | 22.50 | 50.00 | 27.50 |
X14 | Yes&Yes | 24 | 42.76 | 6.66 | 45.00 | 20.40 | 49.50 | 29.10 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.3533042 |
Yes&No-No&No | 0.0003666 |
Yes&Yes-No&No | 0.0004661 |
Yes&No-No&Yes | 0.8289157 |
Yes&Yes-No&Yes | 0.4911216 |
Yes&Yes-Yes&No | 0.7532891 |
plotAndTable(enanto,"answers","Exam..3","Exam 3: Was the question correct? Did they use the models?","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 42 | 30.18 | 11.66 | 32.05 | 0.00 | 49.0 | 49.00 |
X12 | No&Yes | 11 | 36.21 | 9.08 | 38.25 | 14.90 | 49.0 | 34.10 |
X13 | Yes&No | 71 | 38.60 | 7.81 | 39.75 | 17.25 | 50.0 | 32.75 |
X14 | Yes&Yes | 24 | 40.48 | 9.41 | 43.77 | 16.00 | 49.8 | 33.80 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.2334518 |
Yes&No-No&No | 0.0000526 |
Yes&Yes-No&No | 0.0001913 |
Yes&No-No&Yes | 0.8619775 |
Yes&Yes-No&Yes | 0.5976589 |
Yes&Yes-Yes&No | 0.8307620 |
plotAndTable(enanto,"answers","Final.Exam","Final Exam: Was the question correct? Did they use the models?","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 42 | 56.25 | 18.85 | 54.62 | 0.00 | 90.0 | 90.00 |
X12 | No&Yes | 12 | 64.90 | 17.76 | 70.88 | 33.75 | 88.0 | 54.25 |
X13 | Yes&No | 72 | 73.09 | 14.00 | 71.50 | 34.50 | 99.0 | 64.50 |
X14 | Yes&Yes | 24 | 76.51 | 17.98 | 83.12 | 35.75 | 95.9 | 60.15 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.3772369 |
Yes&No-No&No | 0.0000027 |
Yes&Yes-No&No | 0.0000211 |
Yes&No-No&Yes | 0.3813311 |
Yes&Yes-No&Yes | 0.1929542 |
Yes&Yes-Yes&No | 0.8145588 |
plotAndTable(enanto,"answers","Quizzes.Final.Score","Quizzes: Was the question correct? Did they use the models?","Quizzes Average Score")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 42 | 70.18 | 16.66 | 69.31 | 30.00 | 97.78 | 67.78 |
X12 | No&Yes | 12 | 82.65 | 11.54 | 84.62 | 63.75 | 99.44 | 35.69 |
X13 | Yes&No | 72 | 85.03 | 13.07 | 86.46 | 54.44 | 105.56 | 51.12 |
X14 | Yes&Yes | 24 | 90.32 | 14.23 | 96.25 | 46.67 | 104.44 | 57.77 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.0410941 |
Yes&No-No&No | 0.0000018 |
Yes&Yes-No&No | 0.0000009 |
Yes&No-No&Yes | 0.9500051 |
Yes&Yes-No&Yes | 0.4261788 |
Yes&Yes-Yes&No | 0.3958202 |
= addAnswerColumn(didash)
didash plotAndTable(didash,"answers","Exam..1","Exam 1: Was the question correct? Did they use the models?","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 19 | 33.29 | 7.80 | 36.75 | 19.75 | 44.00 | 24.25 |
X12 | No&Yes | 30 | 34.94 | 6.64 | 34.75 | 22.75 | 47.25 | 24.50 |
X13 | Yes&No | 37 | 40.18 | 7.13 | 40.75 | 20.75 | 49.75 | 29.00 |
X14 | Yes&Yes | 64 | 42.93 | 6.23 | 44.12 | 19.75 | 49.75 | 30.00 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.8377591 |
Yes&No-No&No | 0.0022821 |
Yes&Yes-No&No | 0.0000011 |
Yes&No-No&Yes | 0.0102264 |
Yes&Yes-No&Yes | 0.0000020 |
Yes&Yes-Yes&No | 0.2026792 |
plotAndTable(didash,"answers","Exam..2","Exam 2: Was the question correct? Did they use the models?","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 19 | 35.08 | 6.24 | 37.15 | 23.30 | 45.0 | 21.70 |
X12 | No&Yes | 30 | 38.02 | 7.62 | 36.27 | 20.40 | 50.0 | 29.60 |
X13 | Yes&No | 35 | 40.15 | 6.27 | 40.65 | 19.75 | 49.5 | 29.75 |
X14 | Yes&Yes | 64 | 42.40 | 5.07 | 43.95 | 23.75 | 49.5 | 25.75 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.3559186 |
Yes&No-No&No | 0.0205889 |
Yes&Yes-No&No | 0.0000533 |
Yes&No-No&Yes | 0.4947170 |
Yes&Yes-No&Yes | 0.0076862 |
Yes&Yes-Yes&No | 0.2998958 |
plotAndTable(didash,"answers","Exam..3","Exam 3: Was the question correct? Did they use the models?","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 19 | 28.01 | 11.14 | 30.30 | 7.5 | 40.3 | 32.8 |
X12 | No&Yes | 29 | 31.67 | 9.83 | 29.90 | 16.0 | 48.0 | 32.0 |
X13 | Yes&No | 37 | 37.26 | 10.02 | 38.50 | 0.0 | 50.0 | 50.0 |
X14 | Yes&Yes | 63 | 40.45 | 7.47 | 41.65 | 21.3 | 50.0 | 28.7 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.5272977 |
Yes&No-No&No | 0.0025590 |
Yes&Yes-No&No | 0.0000039 |
Yes&No-No&Yes | 0.0699516 |
Yes&Yes-No&Yes | 0.0001966 |
Yes&Yes-Yes&No | 0.3343617 |
plotAndTable(didash,"answers","Final.Exam","Final Exam: Was the question correct? Did they use the models?","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 19 | 56.90 | 12.79 | 60.75 | 36.00 | 80.75 | 44.75 |
X12 | No&Yes | 30 | 60.59 | 19.63 | 57.88 | 29.25 | 96.00 | 66.75 |
X13 | Yes&No | 37 | 67.63 | 19.14 | 68.50 | 0.00 | 95.50 | 95.50 |
X14 | Yes&Yes | 64 | 75.61 | 14.85 | 77.88 | 39.50 | 99.00 | 59.50 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.8767616 |
Yes&No-No&No | 0.1116913 |
Yes&Yes-No&No | 0.0002093 |
Yes&No-No&Yes | 0.3248693 |
Yes&Yes-No&Yes | 0.0004945 |
Yes&Yes-Yes&No | 0.1025616 |
plotAndTable(didash,"answers","Quizzes.Final.Score","Quizzes: Was the question correct? Did they use the models?","Quizzes Average Score")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 19 | 68.84 | 12.57 | 66.39 | 46.39 | 88.61 | 42.22 |
X12 | No&Yes | 30 | 74.68 | 16.70 | 74.59 | 46.67 | 102.22 | 55.55 |
X13 | Yes&No | 37 | 82.90 | 15.16 | 86.25 | 30.00 | 104.44 | 74.44 |
X14 | Yes&Yes | 64 | 87.72 | 13.57 | 90.60 | 43.13 | 105.56 | 62.43 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.5209217 |
Yes&No-No&No | 0.0043566 |
Yes&Yes-No&No | 0.0000108 |
Yes&No-No&Yes | 0.1017889 |
Yes&Yes-No&Yes | 0.0004638 |
Yes&Yes-Yes&No | 0.3784543 |
= addAnswerColumn(dimeso)
dimeso plotAndTable(dimeso,"answers","Exam..1","Exam 1: Was the question correct? Did they use the models?","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 34.24 | 7.58 | 35.50 | 19.75 | 45.00 | 25.25 |
X12 | No&Yes | 14 | 31.82 | 7.37 | 33.62 | 19.75 | 47.50 | 27.75 |
X13 | Yes&No | 75 | 41.07 | 6.16 | 41.25 | 22.75 | 49.50 | 26.75 |
X14 | Yes&Yes | 35 | 42.83 | 7.09 | 45.50 | 20.75 | 49.75 | 29.00 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.7020425 |
Yes&No-No&No | 0.0001010 |
Yes&Yes-No&No | 0.0000142 |
Yes&No-No&Yes | 0.0000344 |
Yes&Yes-No&Yes | 0.0000048 |
Yes&Yes-Yes&No | 0.5837784 |
plotAndTable(dimeso,"answers","Exam..2","Exam 2: Was the question correct? Did they use the models?","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 25 | 37.51 | 6.13 | 37.75 | 24.50 | 50.0 | 25.50 |
X12 | No&Yes | 14 | 34.74 | 7.80 | 34.62 | 20.40 | 49.5 | 29.10 |
X13 | Yes&No | 74 | 40.80 | 6.01 | 41.10 | 19.75 | 49.5 | 29.75 |
X14 | Yes&Yes | 35 | 42.36 | 5.87 | 44.25 | 23.30 | 49.0 | 25.70 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.5369279 |
Yes&No-No&No | 0.1019028 |
Yes&Yes-No&No | 0.0167917 |
Yes&No-No&Yes | 0.0053259 |
Yes&Yes-No&Yes | 0.0008415 |
Yes&Yes-Yes&No | 0.6108740 |
plotAndTable(dimeso,"answers","Exam..3","Exam 3: Was the question correct? Did they use the models?","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 33.81 | 9.43 | 37.50 | 8.75 | 45.5 | 36.75 |
X12 | No&Yes | 14 | 25.78 | 11.45 | 27.15 | 7.50 | 46.8 | 39.30 |
X13 | Yes&No | 73 | 37.71 | 9.25 | 38.55 | 0.00 | 49.8 | 49.80 |
X14 | Yes&Yes | 35 | 39.57 | 8.98 | 41.00 | 17.25 | 50.0 | 32.75 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.0544558 |
Yes&No-No&No | 0.2728122 |
Yes&Yes-No&No | 0.0902442 |
Yes&No-No&Yes | 0.0001605 |
Yes&Yes-No&Yes | 0.0000498 |
Yes&Yes-Yes&No | 0.7737299 |
plotAndTable(dimeso,"answers","Final.Exam","Final Exam: Was the question correct? Did they use the models?","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 61.28 | 15.92 | 63.50 | 29.25 | 96.00 | 66.75 |
X12 | No&Yes | 14 | 54.45 | 18.08 | 52.62 | 29.25 | 93.25 | 64.00 |
X13 | Yes&No | 75 | 70.30 | 17.15 | 71.75 | 0.00 | 95.50 | 95.50 |
X14 | Yes&Yes | 35 | 74.63 | 17.88 | 77.75 | 35.75 | 99.00 | 63.25 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.6287530 |
Yes&No-No&No | 0.1021404 |
Yes&Yes-No&No | 0.0167406 |
Yes&No-No&Yes | 0.0101008 |
Yes&Yes-No&Yes | 0.0016576 |
Yes&Yes-Yes&No | 0.6086441 |
plotAndTable(dimeso,"answers","Quizzes.Final.Score","Quizzes: Was the question correct? Did they use the models?","Quizzes Average Score")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | No&No | 26 | 73.73 | 14.23 | 73.47 | 46.94 | 102.22 | 55.28 |
X12 | No&Yes | 14 | 66.33 | 15.93 | 64.69 | 46.39 | 101.11 | 54.72 |
X13 | Yes&No | 75 | 83.78 | 14.99 | 85.56 | 30.00 | 103.33 | 73.33 |
X14 | Yes&Yes | 35 | 88.59 | 13.13 | 90.63 | 55.31 | 105.56 | 50.25 |
Testing statistical significance: p-values | |
---|---|
No&Yes-No&No | 0.4186218 |
Yes&No-No&No | 0.0148549 |
Yes&Yes-No&No | 0.0006988 |
Yes&No-No&Yes | 0.0003605 |
Yes&Yes-No&Yes | 0.0000191 |
Yes&Yes-Yes&No | 0.3723377 |
Analyzing those who already transitioned and don’t need the models: How many are not using the models because they are visualizing it in their head or using the R/S.
How many students don’t need the models at all or for some specific question. What question?
#merging by ID the students array which has pretty much everything
= read.csv("~/Papers/22_Ochem_Models_across_the_Curriculum/Analysis/modelusage_mergedwithdemographics_cleanforexternal.csv",header = TRUE)
demoData = demoData[which(demoData$Question == "Newman Projection (1.1)"),]
demoData = demoData[,c("ID","Sex","Ethnicity","SOC","First.Generation","Underrepresented","Home.state","HS.GPA","LLC")]
trimDemoData = merge(students,trimDemoData,by.x = "ID",by.y = "ID") students
plotAndTable(students,"Sex","Exam..1","Exam 1: Sex","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | F | 112 | 39.08 | 8.05 | 40.50 | 19.75 | 49.75 | 30.00 |
X12 | M | 24 | 40.73 | 7.02 | 40.62 | 22.75 | 49.50 | 26.75 |
Testing statistical significance: p-values |
---|
0.3540949 |
plotAndTable(students,"Sex","Exam..2","Exam 2: Sex","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | F | 110 | 39.94 | 6.68 | 40.52 | 19.75 | 50.0 | 30.25 |
X12 | M | 24 | 39.44 | 6.75 | 39.90 | 22.50 | 49.5 | 27.00 |
Testing statistical significance: p-values |
---|
0.743875 |
plotAndTable(students,"Sex","Exam..3","Exam 3: Sex","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | F | 110 | 35.66 | 10.76 | 37.88 | 0.0 | 50 | 50.0 |
X12 | M | 24 | 38.55 | 8.09 | 39.80 | 19.8 | 50 | 30.2 |
Testing statistical significance: p-values |
---|
0.2168484 |
plotAndTable(students,"Sex","Final.Exam","Final Exam: Sex","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | F | 112 | 67.07 | 19.04 | 68.50 | 0.0 | 96 | 96.0 |
X12 | M | 24 | 71.83 | 16.34 | 68.38 | 34.5 | 99 | 64.5 |
Testing statistical significance: p-values |
---|
0.2580761 |
plotAndTable(students,"Sex","HS.GPA","HS GPA: Sex","HS GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | F | 103 | 3.66 | 0.36 | 3.70 | 2.72 | 4.46 | 1.74 |
X12 | M | 19 | 3.56 | 0.40 | 3.78 | 2.77 | 3.95 | 1.18 |
Testing statistical significance: p-values |
---|
0.2704221 |
plotAndTable(students,"Ethnicity","Exam..1","Exam 1: Ethnicity","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Am. Indian | 2 | 39.75 | 2.12 | 39.75 | 38.25 | 41.25 | 3.00 |
X12 | Asian | 29 | 38.90 | 7.76 | 39.25 | 23.75 | 49.75 | 26.00 |
X13 | Black | 21 | 39.57 | 8.74 | 43.25 | 19.75 | 49.50 | 29.75 |
X14 | Hispanic | 10 | 35.33 | 6.94 | 37.75 | 24.50 | 45.00 | 20.50 |
X15 | NS | 5 | 40.80 | 5.53 | 39.25 | 35.25 | 49.00 | 13.75 |
X16 | White | 69 | 39.98 | 8.04 | 41.50 | 19.75 | 49.75 | 30.00 |
Testing statistical significance: p-values | |
---|---|
Asian-Am. Indian | 0.9999902 |
Black-Am. Indian | 1.0000000 |
Hispanic-Am. Indian | 0.9791369 |
NS-Am. Indian | 0.9999859 |
White-Am. Indian | 1.0000000 |
Black-Asian | 0.9996832 |
Hispanic-Asian | 0.8222409 |
NS-Asian | 0.9962504 |
White-Asian | 0.9895042 |
Hispanic-Black | 0.7306292 |
NS-Black | 0.9996011 |
White-Black | 0.9999457 |
NS-Hispanic | 0.8056449 |
White-Hispanic | 0.5109012 |
White-NS | 0.9999231 |
plotAndTable(students,"Ethnicity","Exam..2","Exam 2: Ethnicity","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Am. Indian | 2 | 39.47 | 4.70 | 39.47 | 36.15 | 42.8 | 6.65 |
X12 | Asian | 29 | 39.51 | 6.82 | 40.60 | 20.40 | 49.0 | 28.60 |
X13 | Black | 21 | 41.49 | 5.43 | 43.75 | 29.15 | 48.0 | 18.85 |
X14 | Hispanic | 10 | 36.00 | 6.69 | 36.58 | 25.50 | 44.9 | 19.40 |
X15 | NS | 5 | 41.38 | 6.49 | 38.65 | 35.80 | 50.0 | 14.20 |
X16 | White | 67 | 39.95 | 6.98 | 40.25 | 19.75 | 49.5 | 29.75 |
Testing statistical significance: p-values | |
---|---|
Asian-Am. Indian | 1.0000000 |
Black-Am. Indian | 0.9985074 |
Hispanic-Am. Indian | 0.9847733 |
NS-Am. Indian | 0.9993764 |
White-Am. Indian | 0.9999986 |
Black-Asian | 0.9036295 |
Hispanic-Asian | 0.7085177 |
NS-Asian | 0.9921739 |
White-Asian | 0.9996706 |
Hispanic-Black | 0.2734223 |
NS-Black | 1.0000000 |
White-Black | 0.9394133 |
NS-Hispanic | 0.6834781 |
White-Hispanic | 0.5058310 |
White-NS | 0.9973010 |
plotAndTable(students,"Ethnicity","Exam..3","Exam 3: Ethnicity","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Am. Indian | 2 | 38.38 | 4.07 | 38.38 | 35.50 | 41.25 | 5.75 |
X12 | Asian | 29 | 35.65 | 10.49 | 35.60 | 7.50 | 50.00 | 42.50 |
X13 | Black | 20 | 38.33 | 8.59 | 38.50 | 20.05 | 49.50 | 29.45 |
X14 | Hispanic | 10 | 28.84 | 11.07 | 30.55 | 11.50 | 41.50 | 30.00 |
X15 | NS | 5 | 34.96 | 9.26 | 31.50 | 24.75 | 45.00 | 20.25 |
X16 | White | 68 | 36.88 | 10.69 | 40.00 | 0.00 | 50.00 | 50.00 |
Testing statistical significance: p-values | |
---|---|
Asian-Am. Indian | 0.9991681 |
Black-Am. Indian | 1.0000000 |
Hispanic-Am. Indian | 0.8389091 |
NS-Am. Indian | 0.9987172 |
White-Am. Indian | 0.9999526 |
Black-Asian | 0.9471217 |
Hispanic-Asian | 0.4689982 |
NS-Asian | 0.9999930 |
White-Asian | 0.9943988 |
Hispanic-Black | 0.1723634 |
NS-Black | 0.9865252 |
White-Black | 0.9938049 |
NS-Hispanic | 0.8869511 |
White-Hispanic | 0.2003628 |
White-NS | 0.9986176 |
plotAndTable(students,"Ethnicity","Final.Exam","Final Exam: Ethnicity","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Am. Indian | 2 | 63.25 | 2.47 | 63.25 | 61.50 | 65.00 | 3.5 |
X12 | Asian | 29 | 69.64 | 15.63 | 65.60 | 38.50 | 94.00 | 55.5 |
X13 | Black | 21 | 69.76 | 18.59 | 75.00 | 29.25 | 92.75 | 63.5 |
X14 | Hispanic | 10 | 56.35 | 17.11 | 58.25 | 29.25 | 80.75 | 51.5 |
X15 | NS | 5 | 68.60 | 21.28 | 59.75 | 46.50 | 96.00 | 49.5 |
X16 | White | 69 | 68.39 | 19.95 | 70.00 | 0.00 | 99.00 | 99.0 |
Testing statistical significance: p-values | |
---|---|
Asian-Am. Indian | 0.9971308 |
Black-Am. Indian | 0.9970403 |
Hispanic-Am. Indian | 0.9968637 |
NS-Am. Indian | 0.9993634 |
White-Am. Indian | 0.9988986 |
Black-Asian | 1.0000000 |
Hispanic-Asian | 0.3813761 |
NS-Asian | 0.9999971 |
White-Asian | 0.9996445 |
Hispanic-Black | 0.4247844 |
NS-Black | 0.9999956 |
White-Black | 0.9996888 |
NS-Hispanic | 0.8368208 |
White-Hispanic | 0.4030487 |
White-NS | 1.0000000 |
plotAndTable(students,"Ethnicity","HS.GPA","HS GPA: Ethnicity","HS GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Am. Indian | 1 | 3.94 | NA | 3.94 | 3.94 | 3.94 | 0.00 |
X12 | Asian | 28 | 3.76 | 0.25 | 3.84 | 3.10 | 4.17 | 1.06 |
X13 | Black | 21 | 3.45 | 0.42 | 3.55 | 2.74 | 4.05 | 1.31 |
X14 | Hispanic | 9 | 3.42 | 0.52 | 3.37 | 2.83 | 4.22 | 1.39 |
X15 | NS | 3 | 3.84 | 0.29 | 3.86 | 3.53 | 4.12 | 0.59 |
X16 | White | 60 | 3.68 | 0.35 | 3.74 | 2.72 | 4.46 | 1.74 |
Testing statistical significance: p-values | |
---|---|
Asian-Am. Indian | 0.9960981 |
Black-Am. Indian | 0.7463281 |
Hispanic-Am. Indian | 0.7270919 |
NS-Am. Indian | 0.9998282 |
White-Am. Indian | 0.9786182 |
Black-Asian | 0.0297331 |
Hispanic-Asian | 0.1257678 |
NS-Asian | 0.9993950 |
White-Asian | 0.9269927 |
Hispanic-Black | 0.9999643 |
NS-Black | 0.4844470 |
White-Black | 0.0959457 |
NS-Hispanic | 0.4959608 |
White-Hispanic | 0.3023017 |
White-NS | 0.9788118 |
plotAndTable(students,"First.Generation","Exam..1","Exam 1: First Generation","Exam 1")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | N | 76 | 40.83 | 7.05 | 41.38 | 20.75 | 49.75 | 29 |
X12 | Y | 60 | 37.52 | 8.52 | 39.75 | 19.75 | 49.75 | 30 |
Testing statistical significance: p-values |
---|
0.0143914 |
plotAndTable(students,"First.Generation","Exam..2","Exam 2: First Generation","Exam 2")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | N | 74 | 40.84 | 6.53 | 42.20 | 22.50 | 50.0 | 27.50 |
X12 | Y | 60 | 38.62 | 6.69 | 38.52 | 19.75 | 49.5 | 29.75 |
Testing statistical significance: p-values |
---|
0.0542465 |
plotAndTable(students,"First.Generation","Exam..3","Exam 3: First Generation","Exam 3")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | N | 75 | 37.48 | 9.96 | 40.00 | 8.75 | 50 | 41.25 |
X12 | Y | 59 | 34.53 | 10.71 | 35.55 | 0.00 | 50 | 50.00 |
Testing statistical significance: p-values |
---|
0.1026239 |
plotAndTable(students,"First.Generation","Final.Exam","Final Exam: First Generation","Final Exam")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | N | 76 | 70.82 | 18.24 | 73.12 | 29.25 | 99 | 69.75 |
X12 | Y | 60 | 64.23 | 18.60 | 65.25 | 0.00 | 94 | 94.00 |
Testing statistical significance: p-values |
---|
0.0397817 |
plotAndTable(students,"First.Generation","HS.GPA","HS GPA: First Generation","HS GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | N | 64 | 3.67 | 0.38 | 3.75 | 2.74 | 4.46 | 1.72 |
X12 | Y | 58 | 3.62 | 0.36 | 3.69 | 2.72 | 4.22 | 1.50 |
Testing statistical significance: p-values |
---|
0.4748857 |
We can plot the final score
plot(students$Final.Score,students$HS.GPA)
= cor(students[,c("Exam..1","Exam..2","Exam..3","Final.Exam","Quizzes.Final.Score","Final.Score","HS.GPA")],use = "pairwise.complete.obs")
mycor
<-round(mycor,3)
upperupper.tri(mycor)]<-""
upper[#upper<-as.data.frame(upper)
#upper
#library(xtable)
#print(xtable(upper, type="html"))
::kable(upper , caption = "Correlation between the numerical data") knitr
Exam..1 | Exam..2 | Exam..3 | Final.Exam | Quizzes.Final.Score | Final.Score | HS.GPA | |
---|---|---|---|---|---|---|---|
Exam..1 | 1 | ||||||
Exam..2 | 0.725 | 1 | |||||
Exam..3 | 0.772 | 0.791 | 1 | ||||
Final.Exam | 0.813 | 0.851 | 0.883 | 1 | |||
Quizzes.Final.Score | 0.755 | 0.778 | 0.81 | 0.83 | 1 | ||
Final.Score | 0.794 | 0.854 | 0.895 | 0.909 | 0.897 | 1 | |
HS.GPA | 0.312 | 0.372 | 0.282 | 0.366 | 0.391 | 0.405 | 1 |
plotBarAndCorr(students,"Sex","newman","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.20455
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"Sex","newmanComb","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.04451
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","newman","Model use","N of students","Model use by Student of Color (SOC)")
The Chi-square analysis gives a p= 0.14023
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","newmanComb","Model use","N of students","Model use by Student of Color")
The Chi-square analysis gives a p= 0.04874
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","newman","Model use","N of students","Model use by First Generation")
The Chi-square analysis gives a p= 0.50607
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","newmanComb","Model use","N of students","Model use First Generation")
The Chi-square analysis gives a p= 0.10626
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotAndTable(students,"newman","HS.GPA","Using/Not Using/Not Needed","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | NotNeed | 20 | 3.78 | 0.26 | 3.80 | 3.19 | 4.22 | 1.03 |
X12 | NotUsing | 38 | 3.48 | 0.36 | 3.47 | 2.74 | 4.17 | 1.43 |
X13 | Using | 64 | 3.71 | 0.37 | 3.82 | 2.72 | 4.46 | 1.74 |
Testing statistical significance: p-values | |
---|---|
NotUsing-NotNeed | 0.0072797 |
Using-NotNeed | 0.7495826 |
Using-NotUsing | 0.0043354 |
plotAndTable(students,"newmanComb","HS.GPA","Using/Not Using - Correct/Incorrect","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Notusing Correct | 39 | 3.56 | 0.37 | 3.60 | 2.74 | 4.22 | 1.48 |
X12 | Notusing Incorrect | 19 | 3.63 | 0.33 | 3.70 | 2.83 | 4.17 | 1.33 |
X13 | Using Correct | 45 | 3.75 | 0.37 | 3.86 | 2.72 | 4.46 | 1.74 |
X14 | Using Incorrect | 19 | 3.61 | 0.37 | 3.71 | 2.82 | 4.00 | 1.19 |
Testing statistical significance: p-values | |
---|---|
Notusing Incorrect-Notusing Correct | 0.8933020 |
Using Correct-Notusing Correct | 0.0657781 |
Using Incorrect-Notusing Correct | 0.9603225 |
Using Correct-Notusing Incorrect | 0.5834926 |
Using Incorrect-Notusing Incorrect | 0.9977177 |
Using Incorrect-Using Correct | 0.4463924 |
plotBarAndCorr(students,"Sex","diaste","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.02205
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"Sex","diasteComb","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.0152
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","diaste","Model use","N of students","Model use by Student of Color (SOC)")
The Chi-square analysis gives a p= 0.08004
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","diasteComb","Model use","N of students","Model use by Student of Color")
The Chi-square analysis gives a p= 0.29166
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","diaste","Model use","N of students","Model use by First Generation")
The Chi-square analysis gives a p= 0.42854
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","diasteComb","Model use","N of students","Model use First Generation")
The Chi-square analysis gives a p= 0.46868
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotAndTable(students,"diaste","HS.GPA","Using/Not Using/Not Needed","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | NotNeed | 23 | 3.71 | 0.30 | 3.82 | 2.92 | 4.17 | 1.25 |
X12 | NotUsing | 37 | 3.43 | 0.36 | 3.44 | 2.74 | 4.25 | 1.51 |
X13 | Using | 62 | 3.75 | 0.35 | 3.86 | 2.72 | 4.46 | 1.74 |
Testing statistical significance: p-values | |
---|---|
NotUsing-NotNeed | 0.0058891 |
Using-NotNeed | 0.8811543 |
Using-NotUsing | 0.0000336 |
plotAndTable(students,"diasteComb","HS.GPA","Using/Not Using - Correct/Incorrect","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Notusing Correct | 38 | 3.49 | 0.38 | 3.53 | 2.74 | 4.17 | 1.43 |
X12 | Notusing Incorrect | 22 | 3.61 | 0.32 | 3.69 | 2.83 | 4.25 | 1.42 |
X13 | Using Correct | 48 | 3.78 | 0.36 | 3.90 | 2.72 | 4.46 | 1.74 |
X14 | Using Incorrect | 14 | 3.67 | 0.31 | 3.68 | 3.19 | 4.22 | 1.04 |
Testing statistical significance: p-values | |
---|---|
Notusing Incorrect-Notusing Correct | 0.5995632 |
Using Correct-Notusing Correct | 0.0019687 |
Using Incorrect-Notusing Correct | 0.3695345 |
Using Correct-Notusing Incorrect | 0.2741740 |
Using Incorrect-Notusing Incorrect | 0.9568384 |
Using Incorrect-Using Correct | 0.7719006 |
plotBarAndCorr(students,"Sex","enanto","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.59795
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"Sex","enantoComb","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.02156
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","enanto","Model use","N of students","Model use by Student of Color (SOC)")
The Chi-square analysis gives a p= 0.46662
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","enantoComb","Model use","N of students","Model use by Student of Color")
The Chi-square analysis gives a p= 0.04216
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","enanto","Model use","N of students","Model use by First Generation")
The Chi-square analysis gives a p= 0.04054
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","enantoComb","Model use","N of students","Model use First Generation")
The Chi-square analysis gives a p= 0.09056
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotAndTable(students,"enanto","HS.GPA","Using/Not Using/Not Needed","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | NotNeed | 45 | 3.80 | 0.31 | 3.87 | 2.83 | 4.28 | 1.45 |
X12 | NotUsing | 52 | 3.45 | 0.35 | 3.50 | 2.72 | 3.98 | 1.26 |
X13 | Using | 25 | 3.77 | 0.33 | 3.88 | 3.08 | 4.46 | 1.38 |
Testing statistical significance: p-values | |
---|---|
NotUsing-NotNeed | 0.0000032 |
Using-NotNeed | 0.9452032 |
Using-NotUsing | 0.0003567 |
plotAndTable(students,"enantoComb","HS.GPA","Using/Not Using - Correct/Incorrect","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Notusing Correct | 59 | 3.63 | 0.38 | 3.74 | 2.74 | 4.28 | 1.54 |
X12 | Notusing Incorrect | 38 | 3.58 | 0.37 | 3.66 | 2.72 | 4.25 | 1.53 |
X13 | Using Correct | 16 | 3.90 | 0.25 | 3.94 | 3.31 | 4.46 | 1.15 |
X14 | Using Incorrect | 9 | 3.55 | 0.35 | 3.53 | 3.08 | 4.00 | 0.92 |
Testing statistical significance: p-values | |
---|---|
Notusing Incorrect-Notusing Correct | 0.9115999 |
Using Correct-Notusing Correct | 0.0480009 |
Using Incorrect-Notusing Correct | 0.9183449 |
Using Correct-Notusing Incorrect | 0.0202872 |
Using Incorrect-Notusing Incorrect | 0.9945846 |
Using Incorrect-Using Correct | 0.0982578 |
plotBarAndCorr(students,"Sex","didash","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.08109
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"Sex","didashComb","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.05317
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","didash","Model use","N of students","Model use by Student of Color (SOC)")
The Chi-square analysis gives a p= 0.62383
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","didashComb","Model use","N of students","Model use by Student of Color")
The Chi-square analysis gives a p= 0.89281
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","didash","Model use","N of students","Model use by First Generation")
The Chi-square analysis gives a p= 0.44463
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","didashComb","Model use","N of students","Model use First Generation")
The Chi-square analysis gives a p= 0.73594
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotAndTable(students,"didash","HS.GPA","Using/Not Using/Not Needed","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | NotNeed | 10 | 3.72 | 0.33 | 3.86 | 2.92 | 4.00 | 1.08 |
X12 | NotUsing | 36 | 3.46 | 0.34 | 3.47 | 2.74 | 4.17 | 1.43 |
X13 | Using | 76 | 3.73 | 0.36 | 3.82 | 2.72 | 4.46 | 1.74 |
Testing statistical significance: p-values | |
---|---|
NotUsing-NotNeed | 0.1128374 |
Using-NotNeed | 0.9949252 |
Using-NotUsing | 0.0008828 |
plotAndTable(students,"didashComb","HS.GPA","Using/Not Using - Correct/Incorrect","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Notusing Correct | 31 | 3.52 | 0.36 | 3.55 | 2.74 | 4.00 | 1.26 |
X12 | Notusing Incorrect | 15 | 3.50 | 0.33 | 3.49 | 2.92 | 4.17 | 1.25 |
X13 | Using Correct | 52 | 3.76 | 0.33 | 3.86 | 2.72 | 4.46 | 1.74 |
X14 | Using Incorrect | 24 | 3.65 | 0.42 | 3.74 | 2.80 | 4.25 | 1.45 |
Testing statistical significance: p-values | |
---|---|
Notusing Incorrect-Notusing Correct | 0.9973854 |
Using Correct-Notusing Correct | 0.0197914 |
Using Incorrect-Notusing Correct | 0.5753114 |
Using Correct-Notusing Incorrect | 0.0663154 |
Using Incorrect-Notusing Incorrect | 0.5990924 |
Using Incorrect-Using Correct | 0.5658395 |
plotBarAndCorr(students,"Sex","dimeso","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.81715
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"Sex","dimesoComb","Model use","N of students","Model use by sex")
The Chi-square analysis gives a p= 0.04783
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","dimeso","Model use","N of students","Model use by Student of Color (SOC)")
The Chi-square analysis gives a p= 0.76848
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"SOC","dimesoComb","Model use","N of students","Model use by Student of Color")
The Chi-square analysis gives a p= 0.69718
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","dimeso","Model use","N of students","Model use by First Generation")
The Chi-square analysis gives a p= 0.36419
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotBarAndCorr(students,"First.Generation","dimesoComb","Model use","N of students","Model use First Generation")
The Chi-square analysis gives a p= 0.07435
Residuals analysis:
A negative residual implies that the measured value is lower than expected and a positive value higher than expected
plotAndTable(students,"dimeso","HS.GPA","Using/Not Using/Not Needed","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | NotNeed | 37 | 3.73 | 0.30 | 3.77 | 2.92 | 4.25 | 1.33 |
X12 | NotUsing | 48 | 3.46 | 0.36 | 3.48 | 2.72 | 4.05 | 1.33 |
X13 | Using | 37 | 3.80 | 0.34 | 3.93 | 2.83 | 4.46 | 1.63 |
Testing statistical significance: p-values | |
---|---|
NotUsing-NotNeed | 0.0009286 |
Using-NotNeed | 0.6585847 |
Using-NotUsing | 0.0000274 |
plotAndTable(students,"dimesoComb","HS.GPA","Using/Not Using - Correct/Incorrect","Highschool GPA")
group1 | n | mean | sd | median | min | max | range | |
---|---|---|---|---|---|---|---|---|
X11 | Notusing Correct | 61 | 3.63 | 0.33 | 3.69 | 2.74 | 4.22 | 1.48 |
X12 | Notusing Incorrect | 24 | 3.46 | 0.41 | 3.50 | 2.72 | 4.25 | 1.53 |
X13 | Using Correct | 26 | 3.88 | 0.27 | 3.95 | 3.08 | 4.46 | 1.38 |
X14 | Using Incorrect | 11 | 3.62 | 0.42 | 3.78 | 2.83 | 4.28 | 1.45 |
Testing statistical significance: p-values | |
---|---|
Notusing Incorrect-Notusing Correct | 0.1970409 |
Using Correct-Notusing Correct | 0.0126614 |
Using Incorrect-Notusing Correct | 0.9999967 |
Using Correct-Notusing Incorrect | 0.0002371 |
Using Incorrect-Notusing Incorrect | 0.5652983 |
Using Incorrect-Using Correct | 0.1795673 |