#The exam consisted of 15 questions with which you could score a maximum of 55 points (5 points bonus included). The required number of questions answered correctly was lowered from 8 to 7 questions. If you answered 6 questions or less correctly, you did not pass the exam. There is no negotiation about the number of points achieved in the exam. I encourage you to retake the exam if you are not satisfied with the score you received. The exact date for the retake will be given to you in the student office.

mydata <- read.table("./Results_20022023.csv", header=TRUE, sep=";", dec=",")
library(psych)
psych::describe(mydata$Questions) #Number of correctly answered questions (15 max)
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 39  9.1 2.82     10    9.15 2.97   3  15    12 -0.25    -0.74 0.45
library(ggplot2)
ggplot(mydata, aes(x = Questions)) +
  geom_bar() +
  scale_x_continuous(breaks = (seq(0, 15, 1))) +
  xlab("Number of correctly answered questions (max 15)") +
  ylab("Frequency") +
  stat_function(fun = function(x) dnorm(x, 
                                        mean = mean(mydata$Questions), 
                                        sd = sd(mydata$Questions)) * 39 * 1,
                                        color = "darkred", linewidth = 1) +
  geom_vline(xintercept = 6.5, col = "red", linewidth = 1)

Explanation of the NLB point structure (max. 100 points, but if you have received additional points in the peer evaluation, you can receive more than 100 points)

\(NLB=0.20\times Presentation + 0.40\times Marketing + 0.40\times Statistics\)

If at least half of the team members rate a member differently and:

………………………………………………….

Regarding the question with Bonferroni correction: You got a point either if you answered that Answer with Bonferroni is wrong or if you answered All statements are correct.

Since I am very pleased with the effort you have put into your R homework, the number of points achieved on HW is multiplied by a factor of 1.15. Anyone who passes the exam may retake the exam once, and the higher grade will be used as the final grade.

mydata$Final <- mydata$Points + 1.15 * mydata$HW + 0.3 * mydata$NLB
library(psych)
psych::describe(mydata$Final) 
##    vars  n  mean    sd median trimmed   mad  min   max range  skew kurtosis   se
## X1    1 39 81.85 13.04  84.38   82.28 11.49 52.5 106.8  54.3 -0.41    -0.51 2.09
library(ggplot2)
ggplot(mydata, aes(x = Final)) +
  geom_histogram(binwidth = 5, colour = "Black", fill = "Pink" ) +
  scale_x_continuous(limits = c(50, 110), breaks = (seq(50, 110, 5))) +
  xlab("Number of final points") +
  ylab("Frequency")

library(dplyr)
mydata <- mydata %>%
             mutate(Grade = case_when(Passed == "NO" ~ 5,
                                      Final <= 50 & Passed == "YES" ~ 5,
                                      Final > 50 & Final <= 60 & Passed == "YES" ~ 6,
                                      Final > 60 & Final <= 70 & Passed == "YES" ~ 7,
                                      Final > 70 & Final <= 80 & Passed == "YES" ~ 8,
                                      Final > 80 & Final <= 90 & Passed == "YES" ~ 9,
                                      Final > 90 & Passed == "YES" ~ 10))
print(mydata[c(4, 7, 6, 8, 9, 10, 11, 12)])
##    StudentID Questions Passed Points   HW   NLB   Final Grade
## 1   19234707        12    YES     44 17.0  97.8  92.890    10
## 2   19228871         3     NO     11 13.5  86.6  52.505     5
## 3   19225294         8    YES     29 18.5  96.4  79.195     8
## 4   19609772         7    YES     26 15.0  96.0  72.050     8
## 5   19609788        10    YES     37 19.5 106.6  91.405    10
## 6   19609793         6     NO     22 13.5  94.0  65.725     5
## 7   19609809        13    YES     48 20.0 106.6 102.980    10
## 8   19609814         5     NO     18 18.0  96.6  67.680     5
## 9   19609835        10    YES     37 18.5  87.0  84.375     9
## 10  19228866        12    YES     44 17.5  96.4  93.045    10
## 11  19205592        12    YES     44 17.0 107.8  95.890    10
## 12  19596905         5     NO     18  8.0  96.0  56.000     5
## 13  19609840         5     NO     18 16.5  87.0  63.075     5
## 14  19227621        10    YES     37 18.5  94.0  86.475     9
## 15  19609856         9    YES     33 17.5 107.0  85.225     9
## 16  19228651         7    YES     26 19.5  94.0  76.625     8
## 17  19609861         9    YES     33 17.5  87.8  79.465     8
## 18  19609877        11    YES     40 16.0  94.0  86.600     9
## 19  19231931         7    YES     26 14.5  96.6  71.655     8
## 20  19609882        11    YES     40 18.5  97.0  90.375    10
## 21  19613031        13    YES     48 19.5  96.0  99.225    10
## 22  19609898         9    YES     33 17.0  91.0  79.850     8
## 23  19609903        12    YES     44 18.0  91.0  92.000    10
## 24  19204410        11    YES     40 19.5  94.0  90.625    10
## 25  19239071         8    YES     29 19.0 107.0  82.950     9
## 26  19238738        12    YES     44 19.5 106.6  98.405    10
## 27  19609919        10    YES     37 19.0  96.4  87.770     9
## 28  19609924         8    YES     29 17.0  96.4  77.470     8
## 29  19226764         7    YES     26 15.0  96.0  72.050     8
## 30  19232495        10    YES     37 19.0  94.0  87.050     9
## 31  19225231        10    YES     37 18.5  91.0  85.575     9
## 32  19229091        10    YES     37 15.5  91.0  82.125     9
## 33  19261458        15    YES     55 20.0  96.0 106.800    10
## 34  19208153        11    YES     40 14.0  97.8  85.440     9
## 35  19204405         5     NO     18 12.5  91.0  59.675     5
## 36  19230498         4     NO     15 14.0  96.0  59.900     5
## 37  19204253         9    YES     33 19.5  96.4  84.345     9
## 38  19207275         7    YES     26 17.5  94.0  74.325     8
## 39  19207081        12    YES     44 17.5  97.8  93.465    10