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[ continuation from here, paragraph (N_1) ]

# questio(data,s1,s2,N,I) evaluates the answers to a questionnaire, where:
# data  are  results,  s1  numbers of possible answers to information questions,  s2
# numbers of possible answers to actual questions,  N  the particular question to be
# examined,  I  the answers to informative questions to be tested (0 if all are tested).
# questioP(data,s1,s2, OK,P, I) gives the scores, if the questions have an OK answer
# and are evaluated with a certain weight.
# An example of the use of questio (with little data, to understand the commands):

#  1: 1 females, 2 males                2: 1,2,3 class
#  5: answer (selected among 1,2)

D = c(
1,1, 2,3,1,
1,2, 1,4,2,
2,1, 3,1,1,
2,3, 4,2,2,
1,3, 3,2,1,
2,1, 3,1,2,
2,2, 1,2,2,
1,3, 2,2,1,
2,1, 3,4,2,
2,2, 1,2,2,
2,3, 2,2,1,
1,3, 1,4,2,
1,2, 1,4,2,
1,2, 1,3,2,
1,2, 1,1,2  )

BF=2; HF=2.3
# To resize the graphs.  The histograms are shown below.

info = c(2,3); cult = c(4,4,2)
questio(D,info,cult, 1,0)      # 1^ question
#  15  total number of answers
#  [the distance of the sides of the grid is   10 %]
#    Frequencies and percentage freq.:
#  8,    7
#  53.33,46.67
undery("F / M")

questio(D,info,cult, 2,0)      # 2^ question
#  15  total number of answers
#  [the distance of the sides of the grid is   5 %]
#    Frequencies and percentage freq.:
#  4,    6, 5
#  26.67,40,33.33
undery("1^ / 2^ / 3^")

questio(D,info,cult, 3,0)      # 3^ question
#  15  total number of answers
#  [the distance of the sides of the grid is   5 %]
#    Frequencies and percentage freq.:
#  7,    3, 4,    1
#  46.67,20,26.67,6.67

questio(D,info,cult, 5,0)      # 5^ question
#  15  total number of answers
#  [the distance of the sides of the grid is   10 %]
#    Frequencies and percentage freq.:
#  5,    10
#  33.33,66.67

questio(D,info,cult, 4,c(1,2)) # 4^ question among the female of 2^ class
#  4  total number of answers
#  [the distance of the sides of the grid is   10 %]
#    Frequencies and percentage freq.:
#  1, 0, 1, 2
#  25,0,25,50
undery("F / 2^")

#  An example of the use of questio and of questioP
#  Two classes performed a verification test (of 6 questions):
# 1: 1,2 class
#  One informative question and 6 actual questions of 4,4,4,2,6,3 possible answers
info = 2; cult = c(4,4,4,2,6,3)
#  The correct answers are OK and the scores of the various questions are P:
OK = c(4,2,3,1,5,3); P = c(10,8,8,7,10,5)
X = c(
1, 4,2,3,1,5,3,
1, 4,1,3,2,5,2,
2, 1,2,1,1,1,3,
2, 4,4,3,2,5,1,
1, 4,2,2,1,5,3,
2, 4,2,3,1,6,2,
2, 2,2,2,2,1,3,
1, 3,2,1,1,5,1,
1, 4,3,3,1,5,3,
2, 1,2,1,2,6,2,
2, 2,1,1,1,1,3,
1, 4,2,2,1,5,1,
2, 4,2,3,2,5,3,
2, 2,1,3,1,5,3,
2, 4,2,2,1,5,3,
1, 3,1,1,2,5,3,
1, 4,2,3,1,3,3,
1, 4,2,3,1,5,3,
1, 2,1,3,2,5,2,
2, 4,2,1,1,1,3,
2, 3,2,3,1,5,1,
1, 4,3,3,2,5,3,
1, 4,2,3,1,3,2 )

questio(X, info,cult, 2,0)
# 23  total number of answers
# Frequencies and percentage freq.:
# 2,  4,    3,    14
# 8.7,17.39,13.04,60.87
questio(X, info,cult, 5,0)
# 23  total number of answers
# Frequencies and percentage freq.:
# 15,   8
# 65.22,34.78
questio(X, info,cult, 5,1)
# 12  total number of answers
# Frequencies and percentage freq.:
# 8, 4
# 66.67,33.33
undery("F")

#  The scores (in hundredths) of the 23 students (see above for OK and P):
questioP(X,info,cult, OK,P, 0)
# 1 100
# 2 58
# 3 42
# 4 58
# 5 83
# 6 69
# 7 27
# 8 52
# 9 83
# 10 17
# 11 25
# 12 73
# 13 85
# 14 62
# 15 83
# 16 31
# 17 79
# 18 100
# 19 38
# 20 62
# 21 69
# 22 69
# 23 69
#  The scores of the females:
questioP(X,info,cult, OK,P, 1)
# 1 100
# 2 58
# 5 83
# 8 52
# 9 83
# 12 73
# 16 31
# 17 79
# 18 100
# 19 38
# 22 69
# 23 69

#  If I want to transform the scores I can use P_
S = P_; S
# 100.00000  58.33333  83.33333  52.08333  83.33333  72.91667  31.25000
# 79.16667  100.00000  37.50000  68.75000  68.75000
# The scores of the females in tenths and in quarters:
round(S/100*10)
# 10  6  8  5  8  7  3  8  10  4  7  7
round(S/100*4)
# 4 2 3 2 3 3 1 3 4 2 3 3

# If I want I can process other statistics:
Histo(P_, 0,100, 10)
#   Frequencies and percentage freq.:
# 0,  1,  2,  2,  1,    3,    6,   2,   4,   2
# 0,4.35,8.7,8.7,4.35,13.04,26.09,8.7,17.39,8.7
statistics(P_)
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#   16.67   46.88   68.75   62.41   81.25  100.00
#     The brown dots are 5^ and 95^ percentiles
#            The red dot is the mean

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