library(dplyr);

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(boot);
library(broom);
library(knitr);
library(ggplot2);
data <- read.csv("analysis/data.csv", comment.char ="#");
data[data=="true"] <- 1;
data[data=="false"] <- 0;
data <- transform(data, wasSuccessful = as.numeric(wasSuccessful)
                  ,HLO0 = HLO0 / 1000
                  ,HLO1 = HLO1 / 1000
                  ,HLO2 = HLO2 / 1000
                  ,HLO3 = HLO3 / 1000
                  ,HLO4 = HLO4 / 1000
                  ,HLO5 = HLO5 / 1000
                  ,HLO6 = HLO6 / 1000
                  ,HLO7 = HLO7 / 1000

                  )
boot_mean <- function(original_vector, resample_vector) {
    mean(original_vector[resample_vector])
}

nonnegative <- Vectorize(function(x) {x >= 0})

wiltest <- function(a, b) {
  return(wilcox.test(Filter(nonnegative, a), Filter(nonnegative, b), paired=FALSE))
}

questions<-list(
"I had to re-read instructions to understand what I needed to do",
"It was always clear to me what I was supposed to do.",
"Overall, the system gave me good instructions.",
 "The system gave me useful feedback about my progress.",
 "The system was really verbose and explained things that were already clear to me.",
 "The system's  instructions came too early.",
 "The system's  instructions came too late."
)

Evaluation of all games (i.e. building might not be finished)

note that timings are probably highly correlated with successfulness here!

data

feats <- list("timeToSuccess", "numMistakes", "Question0", "Question1", "Question2", "Question3", "Question4",  "Question5", "Question6")

architects <- list("BLOCK", "MEDIUM", "HIGHLEVEL")

bridgetableall<-data.frame(matrix(nrow=length(feats), ncol=3))
colnames(bridgetableall) <- architects
row.names(bridgetableall) <- feats
housetableall<-data.frame(matrix(nrow=length(feats), ncol=3))
colnames(housetableall) <- architects
row.names(housetableall) <- feats


for (scen in list("house", "bridge")) {
  cat('\n\n##' , scen , '\n\n')
  for (q in list("wasSuccessful", "numMistakes", "Question0", "Question1", "Question2", "Question3", "Question4",  "Question5", "Question6")) {
    cat('\n\n###', q, '\n')
    for (arch in list("BLOCK", "MEDIUM", "HIGHLEVEL")) {
      cat('\n####', arch, '\n\n')
      nam <- paste("succ",scen,arch, sep="")
      assign(nam, data %>% filter(scenario == scen) %>% filter(architect == paste("SimpleArchitect-",arch, sep="")), envir = .GlobalEnv)

      dset <- Filter(function(x) {x >= 0}, get(nam)[,c(q)])
      # hist(dset)
      cat('\n\n')
      mean_results <- boot(dset, boot_mean, R = 20000);
      print(boot.ci(mean_results, type="bca"))
      cat('\n\n')
      print(kable(tidy(summary(dset)), align="llllll" ))
      
      if (scen == "house") {
        housetableall[q, arch] <- mean(dset)[1]
      } else {
        bridgetableall[q, arch] <- mean(dset)[1]
      }
      
    }
  }
}

house

wasSuccessful

BLOCK

1 “All values of t are equal to 1 Cannot calculate confidence intervals” NULL

minimum	q1	median	mean	q3	maximum
1	1	1	1	1	1

MEDIUM

## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 0.6667, 1.0000 )
Calculations and Intervals on Original Scale Warning : BCa Intervals used Extreme Quantiles Some BCa intervals may be unstable

minimum	q1	median	mean	q3	maximum
0	1	1	0.952381	1	1

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 0.5384, 0.9444 )
Calculations and Intervals on Original Scale Some BCa intervals may be unstable

minimum	q1	median	mean	q3	maximum
0	1	1	0.8888889	1	1

numMistakes

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.79, 18.42 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	7	13	14.47368	20	30

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (16.95, 34.81 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	13	19	23.66667	28	83

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (24.61, 60.44 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
7	14.25	21	38.38889	53.75	126

Question0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.211, 4.316 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3	4	3.894737	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.810, 4.619 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	4	5	4.333333	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.222, 4.833 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
3	4.25	5	4.666667	5	5

Question1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.368, 3.211 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.842105	3	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.952, 2.667 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.333333	3	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.500, 2.389 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	1.944444	2	4

Question2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.368, 3.474 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.947368	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.190, 3.143 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.714286	4	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.5, 2.5 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	2	2.75	4

Question3

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.368, 4.421 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	4.052632	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.286, 4.333 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	3.952381	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.333, 4.444 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	4.055556	5	5

Question4

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.842, 2.526 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.210526	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.286, 3.238 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.809524	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.222, 3.444 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2.5	2.833333	4	5

Question5

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.421, 3.684 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	3.157895	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.381, 3.381 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.952381	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.000, 3.944 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3	4	3.611111	4	5

Question6

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.105, 1.684 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.368421	1.5	3

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.714, 2.333 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.047619	2	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.222, 1.889 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.555556	2	3

bridge

wasSuccessful

BLOCK

## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 0.6842, 1.0000 )
Calculations and Intervals on Original Scale Warning : BCa Intervals used Extreme Quantiles Some BCa intervals may be unstable

minimum	q1	median	mean	q3	maximum
0	1	1	0.9473684	1	1

MEDIUM

1 “All values of t are equal to 1 Cannot calculate confidence intervals” NULL

minimum	q1	median	mean	q3	maximum
1	1	1	1	1	1

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 0.6190, 0.9524 )
Calculations and Intervals on Original Scale Some BCa intervals may be unstable

minimum	q1	median	mean	q3	maximum
0	1	1	0.9047619	1	1

numMistakes

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (14.89, 42.88 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
0	6.5	18	23.89474	29	121

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.53, 34.68 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
0	5	10	18.57895	18.5	98

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (27.46, 55.95 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4	13	27	39.7619	65	117

Question0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.368, 4.842 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4	4	5	4.68421	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.474, 4.421 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3.5	4	4.052632	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.286, 4.810 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
3	4	5	4.666667	5	5

Question1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.842, 2.947 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.5	2	2.368421	3	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.737, 3.632 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	2.5	3	3.210526	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.524, 2.095 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	1.857143	2	3

Question2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.789, 2.632 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.5	2	2.210526	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.842, 3.789 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	2	4	3.368421	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.952, 2.667 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.333333	3	4

Question3

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.421, 4.211 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3	4	3.894737	4.5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.105, 4.263 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3	4	3.789474	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.476, 4.429 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3	4	4.047619	5	5

Question4

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.737, 2.526 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.5	2	2.157895	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.316, 3.316 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.842105	3.5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.857, 2.857 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	2.333333	3	5

Question5

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.053, 4.158 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	3.789474	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.000, 3.895 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3	4	3.526316	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.524, 3.571 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	3.095238	4	5

Question6

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.158, 1.684 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.421053	2	3

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.368, 2.158 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	1.736842	2	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.714, 2.286 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.047619	3	3

Question0: I had to re-read instructions to understand what I needed to do
Question1: It was always clear to me what I was supposed to do.
Question2: Overall, the system gave me good instructions.
Question3: The system gave me useful feedback about my progress.
Question4: The system was really verbose and explained things that were already clear to me.
Question5: The system’s instructions came too early.
Question6: The system’s instructions came too late.

Evaluation of successful games (i.e. building was finished)

# "HLO0", "HLO1", "HLO2",

feats <- list("timeToSuccess", "numMistakes", "HLO0", "HLO1", "HLO2", "Question0", "Question1", "Question2", "Question3", "Question4",  "Question5", "Question6")

architects <- list("BLOCK", "MEDIUM", "HIGHLEVEL")

bridgetable<-data.frame(matrix(nrow=length(feats), ncol=3))
colnames(bridgetable) <- architects
row.names(bridgetable) <- feats
housetable<-data.frame(matrix(nrow=length(feats), ncol=3))
colnames(housetable) <- architects
row.names(housetable) <- feats

housetable["timeToSuccess", "BLOCK"] <- 1

succGames <- data %>% filter(wasSuccessful == 1);
for (scen in list("house", "bridge")) {
  cat('\n\n##' , scen , '\n\n')
  for (q in feats) {
    cat('\n\n###', q, '\n')
    for (arch in architects) {
      cat('\n####', arch, '\n\n')
      nam <- paste("succ",scen,arch, sep="")
      assign(nam, succGames %>% filter(scenario == scen) %>% filter(architect == paste("SimpleArchitect-",arch, sep="")), envir = .GlobalEnv)

      dset <- Filter(function(x) {x >= 0}, get(nam)[,c(q)])

      # hist(dset)
      cat('\n\n')
      mean_results <- boot(dset, boot_mean, R = 20000);
      print(boot.ci(mean_results, type="bca"))
      cat('\n\n')
      print(kable(tidy(summary(dset)), align="llllll" ))
      if (scen == "house") {
        housetable[q, arch] <- mean(dset)[1]
      } else {
        bridgetable[q, arch] <- mean(dset)[1]
      }
    }
  }
}

house

timeToSuccess

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (152.6, 203.1 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
115	133.5	156	171.5789	191.5	332

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (195.4, 308.0 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
92	159.75	184	239.8	307	573

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (195.5, 304.2 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
120	154	195	244	310.25	461

numMistakes

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.74, 18.42 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	7	13	14.47368	20	30

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (16.50, 34.83 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	11.75	18	23.3	25	83

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (19.62, 51.25 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
7	13.25	21	29.5	26	120

HLO0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (46.10, 61.14 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
31.762	43.046	48.077	51.85579	53.4095	92.288

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (58.89, 77.87 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
31.058	51.0405	70.554	68.6423	83.092	109.01

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (40.17, 68.30 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
12.493	28.08125	46.0955	52.85106	71.4425	106.373

HLO1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (18.00, 34.65 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
9.448	14.468	17.767	23.22337	22.9275	78.25

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (11.65, 24.26 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
6.808	9.27775	10.9245	15.23425	14.517	59.804

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 8.09, 15.96 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4.684	6.5805	8.7005	10.30369	10.14575	34.4

HLO2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.49, 16.02 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
5.653	8.302	11.75	12.71684	14.739	29.668

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 6.459, 11.789 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2.856	4.645	6.457	8.612632	11.07	23.296

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 5.355, 10.256 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2.995	4.60725	5.5575	6.728688	7.1595	20.752

Question0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.211, 4.316 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3	4	3.894737	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.85, 4.65 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	4	5	4.4	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.125, 4.812 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
3	4	5	4.625	5	5

Question1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.368, 3.211 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.842105	3	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.95, 2.70 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.35	3	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.500, 2.438 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	1.9375	2	4

Question2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.368, 3.474 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.947368	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.1, 3.1 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.65	4	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.500, 2.562 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	2	2.25	4

Question3

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.368, 4.421 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	4.052632	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.2, 4.3 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3.75	4	3.9	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.188, 4.438 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3.75	4	4	5	5

Question4

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.842, 2.526 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.210526	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.35, 3.30 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.9	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.250, 3.562 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2.5	2.9375	4	5

Question5

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.421, 3.737 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	3.157895	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.40, 3.45 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	3	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.062, 4.062 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3.75	4	3.75	4	5

Question6

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.105, 1.684 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.368421	1.5	3

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.65, 2.30 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.75	2	2	2	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.188, 1.938 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.5625	2	3

bridge

timeToSuccess

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (154.7, 224.2 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
110	140	162	177	181.75	403

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (142.7, 226.5 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
84	110	156	172.5263	187.5	449

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (217.8, 343.8 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
76	165	267	275.4737	358	564

numMistakes

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (12.22, 28.50 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
0	6.25	14.5	18.5	26.5	67

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.63, 34.84 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
0	5	10	18.57895	18.5	98

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (24.21, 55.37 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4	11	26	36.89474	45.5	117

HLO0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 71.78, 100.49 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
43.523	62.70775	73.484	84.11461	105.2322	151.517

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 78.3, 164.1 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
34.858	61.8525	77.087	103.9274	104.8195	390.203

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 91.5, 191.5 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
27.914	49.0325	87.116	128.8877	181.817	446.516

HLO1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (40.50, 68.56 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
21.349	32.739	43.7105	49.37467	51.9295	140.503

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (36.96, 53.26 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
21.597	31.6015	39.588	44.34684	60.398	77.147

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 92.6, 189.7 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
30.751	64.0975	93.991	129.8736	136.484	393.192

HLO2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (24.55, 79.60 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
13.405	18.2395	21.7575	38.1605	29.312	215.785

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.73, 47.09 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
5.644	8.4235	10.59	18.44716	13.0765	143.999

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 9.58, 21.52 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4.943	7.108	9.24	13.30976	15.747	49.646

Question0

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.333, 4.833 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
4	4	5	4.666667	5	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.474, 4.421 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3.5	4	4.052632	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.263, 4.789 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
3	4	5	4.631579	5	5

Question1

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.833, 3.000 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.25	2	2.388889	3	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.737, 3.632 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	2.5	3	3.210526	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.579, 2.158 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.5	2	1.894737	2	3

Question2

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.833, 2.667 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.277778	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.842, 3.737 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	2	4	3.368421	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.947, 2.737 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	2	2.368421	3	4

Question3

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.444, 4.278 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3.25	4	3.944444	4.75	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.053, 4.263 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	3	4	3.789474	5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.421, 4.474 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3	5	4.052632	5	5

Question4

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.722, 2.611 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.25	2	2.166667	3	4

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.316, 3.316 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	2.842105	3.5	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.737, 2.632 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	2.210526	3	4

Question5

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.056, 4.167 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	4	4	3.777778	4	5

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 3.000, 3.895 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
2	3	4	3.526316	4	5

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.526, 3.579 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	2	3	3.105263	4	5

Question6

BLOCK

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.167, 1.722 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	1	1.444444	2	3

MEDIUM

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.368, 2.105 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1	2	1.736842	2	4

HIGHLEVEL

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 1.684, 2.316 )
Calculations and Intervals on Original Scale

minimum	q1	median	mean	q3	maximum
1	1.5	2	2.052632	3	3

Question0: I had to re-read instructions to understand what I needed to do
Question1: It was always clear to me what I was supposed to do.
Question2: Overall, the system gave me good instructions.
Question3: The system gave me useful feedback about my progress.
Question4: The system was really verbose and explained things that were already clear to me.
Question5: The system’s instructions came too early.
Question6: The system’s instructions came too late.

compact tables

significance tests (because reviewers)

bridge

low level faster than high level:

m1<-wiltest(succbridgeBLOCK$timeToSuccess, succbridgeHIGHLEVEL$timeToSuccess)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 101.5, p-value = 0.03601
## alternative hypothesis: true location shift is not equal to 0

medium faster than high level:

m1<-wiltest(succbridgeMEDIUM$timeToSuccess, succbridgeHIGHLEVEL$timeToSuccess)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 103, p-value = 0.02457
## alternative hypothesis: true location shift is not equal to 0

high level and block level seem to be similar-ish in “gave good instructions”

m1<-wiltest(succbridgeBLOCK$Question2, succbridgeHIGHLEVEL$Question2)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 163.5, p-value = 0.8222
## alternative hypothesis: true location shift is not equal to 0

medium preferred to block level:

m1<-wiltest(succbridgeMEDIUM$Question2, succbridgeBLOCK$Question2)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 261, p-value = 0.00464
## alternative hypothesis: true location shift is not equal to 0

medium preferred to high level:

m1<-wiltest(succbridgeMEDIUM$Question2, succbridgeHIGHLEVEL$Question2)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 268.5, p-value = 0.007185
## alternative hypothesis: true location shift is not equal to 0

Always clear what to do block vs medium:

m1<-wiltest(succbridgeBLOCK$Question1, succbridgeMEDIUM$Question1)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

print(m1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 99.5, p-value = 0.0262
## alternative hypothesis: true location shift is not equal to 0

succbridgeMEDIUM$numBlocksDestroyed

##  [1]   0 107   4   1  11   5   2  67  15   7  11  15  12  44  22   9  15   3   4

Bridge: railing high level slower than medium:

m1<-wiltest(succbridgeHIGHLEVEL$HLO1, succbridgeMEDIUM$HLO1)
print(m1)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 308, p-value = 9.132e-05
## alternative hypothesis: true location shift is not equal to 0

Bridge: railing 2 high level faster than block level:

m1<-wiltest(succbridgeBLOCK$HLO2[], succbridgeHIGHLEVEL$HLO2)
print(m1)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 270, p-value = 3.895e-05
## alternative hypothesis: true location shift is not equal to 0

high level less successful finish than medium level slightly non-significant:

fishertable <- data %>% filter(scenario=="bridge") %>% filter(architect != "SimpleArchitect-BLOCK"  ) %>% group_by(architect) %>% summarise(
  t=sum(wasSuccessful),
  f=n()-sum(wasSuccessful)
)

fisher.test(data.matrix(fishertable))

house HLO speeds

house_hlo_speed_table <--data.frame(matrix(nrow=8, ncol=3))
colnames(house_hlo_speed_table) <- architects
# row.names(house_hlo_speed_table) <- feats

for (col in seq(0,7)) {
  cat("\n\n## ")
  cat(col)
  cat("\n\n")
  for (arch in list("BLOCK", "MEDIUM", "HIGHLEVEL")) {
      nam <- paste("succhouse",arch, sep="")
      cname <- paste("HLO",col, sep="")
      cat("\n### ")
      cat(arch)
      cat("\n\n")
      dset <- Filter(nonnegative, get(nam)[,c(cname)])
      mean_results <- boot(dset, boot_mean, R = 20000);
      print(summary(dset))
      cat("\n\n")
      house_hlo_speed_table [col+1, arch] <- mean(dset)[1]

    print(boot.ci(mean_results, type="bca"))
  }
}

0

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 31.76 43.05 48.08 51.86 53.41 92.29

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (46.33, 61.41 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 31.06 51.04 70.55 68.64 83.09 109.01

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (59.07, 78.11 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 12.49 28.08 46.10 52.85 71.44 106.37

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (40.07, 68.21 )
Calculations and Intervals on Original Scale

1

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 9.448 14.468 17.767 23.223 22.927 78.250

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (17.90, 34.16 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 6.808 9.278 10.925 15.234 14.517 59.804

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (11.68, 24.12 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 4.684 6.580 8.700 10.304 10.146 34.400

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 8.09, 15.99 )
Calculations and Intervals on Original Scale

2

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 5.653 8.302 11.750 12.717 14.739 29.668

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (10.50, 16.09 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 2.856 4.645 6.457 8.613 11.070 23.296

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 6.482, 11.880 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 2.995 4.607 5.558 6.729 7.160 20.752

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 5.356, 10.091 )
Calculations and Intervals on Original Scale

3

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 3.791 7.294 10.099 9.407 10.783 16.870

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 8.135, 10.748 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 3.447 3.845 4.801 9.535 8.789 45.181

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 5.972, 17.232 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 1.855 3.862 4.333 5.772 7.768 12.901

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.448, 7.607 )
Calculations and Intervals on Original Scale

4

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 9.559 11.131 13.411 17.148 19.572 45.087

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (14.02, 22.65 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 16.48 21.60 33.04 42.73 40.15 174.19

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (32.25, 69.42 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 0.354 53.403 64.823 69.630 76.983 180.041

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (52.61, 96.08 )
Calculations and Intervals on Original Scale

5

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 7.259 11.620 14.240 35.538 41.601 146.846

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% (21.34, 62.05 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 8.144 14.829 38.612 66.921 64.100 318.298

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 38.20, 123.53 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 8.88 25.24 42.45 85.18 114.40 323.35

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 48.71, 151.35 )
Calculations and Intervals on Original Scale

6

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 3.128 3.900 5.150 8.985 10.423 34.393

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 6.235, 14.552 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 2.352 4.077 5.296 8.566 8.166 50.948

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 5.788, 17.441 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 1.009 3.002 3.833 7.874 5.851 44.995

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.112, 18.004 )
Calculations and Intervals on Original Scale

7

BLOCK

Min. 1st Qu. Median Mean 3rd Qu. Max. 2.152 3.621 4.444 5.737 5.599 23.378

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.281, 10.026 )
Calculations and Intervals on Original Scale

MEDIUM

Min. 1st Qu. Median Mean 3rd Qu. Max. 2.347 3.431 3.797 13.942 4.787 106.980

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 4.89, 44.93 )
Calculations and Intervals on Original Scale

HIGHLEVEL

Min. 1st Qu. Median Mean 3rd Qu. Max. 1.749 2.329 2.688 3.831 5.155 7.200

BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 20000 bootstrap replicates

CALL : boot.ci(boot.out = mean_results, type = “bca”)

Intervals : Level BCa
95% ( 2.846, 5.201 )
Calculations and Intervals on Original Scale

block preferred over highlevel “gave good instructions”

wiltest(succhouseBLOCK$Question2, succhouseHIGHLEVEL$Question2)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 217, p-value = 0.02705
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$timeToSuccess, succhouseHIGHLEVEL$timeToSuccess)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 97.5, p-value = 0.07372
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$timeToSuccess, succhouseMEDIUM$timeToSuccess)

## Warning in wilcox.test.default(Filter(nonnegative, a), Filter(nonnegative, :
## cannot compute exact p-value with ties

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 122.5, p-value = 0.05974
## alternative hypothesis: true location shift is not equal to 0

# significance tests for HLOs
wiltest(succhouseBLOCK$HLO0, succhouseHIGHLEVEL$HLO0)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 162, p-value = 0.7561
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO1, succhouseHIGHLEVEL$HLO1)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 273, p-value = 1.63e-05
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO2, succhouseHIGHLEVEL$HLO2)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 265, p-value = 7.427e-05
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO3, succhouseHIGHLEVEL$HLO3)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 240, p-value = 0.002906
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO4, succhouseHIGHLEVEL$HLO4)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 26, p-value = 5.603e-06
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO5, succhouseHIGHLEVEL$HLO5)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 100, p-value = 0.08826
## alternative hypothesis: true location shift is not equal to 0

wiltest(succhouseBLOCK$HLO6, succhouseHIGHLEVEL$HLO6)

## 
##  Wilcoxon rank sum exact test
## 
## data:  Filter(nonnegative, a) and Filter(nonnegative, b)
## W = 191, p-value = 0.0963
## alternative hypothesis: true location shift is not equal to 0

pretty tables

all games

means for bridge (all)

kable(bridgetableall, digits=2)

means for house (all)

kable(housetableall, digits=2)

successful games

means for bridge (successful only)

kable(bridgetable, digits=2)

	BLOCK	MEDIUM	HIGHLEVEL
timeToSuccess	177.00	172.53	275.47
numMistakes	18.50	18.58	36.89
HLO0	84.11	103.93	128.89
HLO1	49.37	44.35	129.87
HLO2	38.16	18.45	13.31
Question0	4.67	4.05	4.63
Question1	2.39	3.21	1.89
Question2	2.28	3.37	2.37
Question3	3.94	3.79	4.05
Question4	2.17	2.84	2.21
Question5	3.78	3.53	3.11
Question6	1.44	1.74	2.05

means for house (successful only)

kable(housetable, digits=2)

	BLOCK	MEDIUM	HIGHLEVEL
timeToSuccess	171.58	239.80	244.00
numMistakes	14.47	23.30	29.50
HLO0	51.86	68.64	52.85
HLO1	23.22	15.23	10.30
HLO2	12.72	8.61	6.73
Question0	3.89	4.40	4.62
Question1	2.84	2.35	1.94
Question2	2.95	2.65	2.00
Question3	4.05	3.90	4.00
Question4	2.21	2.90	2.94
Question5	3.16	3.00	3.75
Question6	1.37	2.00	1.56

Build speeds house (4x wall then rows) (successfull and in-order built only)

kable(house_hlo_speed_table, digits = 2)

BLOCK	MEDIUM	HIGHLEVEL
51.86	68.64	52.85
23.22	15.23	10.30
12.72	8.61	6.73
9.41	9.53	5.77
17.15	42.73	69.63
35.54	66.92	85.18
8.98	8.57	7.87
5.74	13.94	3.83

R Notebook

Evaluation of all games (i.e. building might not be finished)

house

wasSuccessful

BLOCK

MEDIUM

HIGHLEVEL

numMistakes

BLOCK

MEDIUM

HIGHLEVEL

Question0

BLOCK

MEDIUM

HIGHLEVEL

Question1

BLOCK

MEDIUM

HIGHLEVEL

Question2

BLOCK

MEDIUM

HIGHLEVEL

Question3

BLOCK

MEDIUM

HIGHLEVEL

Question4

BLOCK

MEDIUM

HIGHLEVEL

Question5

BLOCK

MEDIUM

HIGHLEVEL

Question6

BLOCK

MEDIUM

HIGHLEVEL

bridge

wasSuccessful

BLOCK

MEDIUM

HIGHLEVEL

numMistakes

BLOCK

MEDIUM

HIGHLEVEL

Question0

BLOCK

MEDIUM

HIGHLEVEL

Question1

BLOCK

MEDIUM

HIGHLEVEL

Question2

BLOCK

MEDIUM

HIGHLEVEL

Question3

BLOCK

MEDIUM

HIGHLEVEL

Question4

BLOCK

MEDIUM

HIGHLEVEL

Question5

BLOCK

MEDIUM

HIGHLEVEL

Question6

BLOCK

MEDIUM

HIGHLEVEL

Evaluation of successful games (i.e. building was finished)

house

timeToSuccess

BLOCK