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