def hyperGeometricDistributionProblem():
try:
nk = random.randint(32, 64)
nx = random.randint(8, 16)
k = random.randint(8, 16)
x = random.randint(2, 4)
x2 = random.randint(2, 4)
sol1 = round(
coursesFunctionsBll.binomialCoefficient(k, x) *
(coursesFunctionsBll.binomialCoefficient(nk - k, nx - x) * 100 /
coursesFunctionsBll.binomialCoefficient(nk, nx)), 4)
sol2 = 0
i = 0
while i < x2:
sol2 += coursesFunctionsBll.binomialCoefficient(k, i) * (
coursesFunctionsBll.binomialCoefficient(nk - k, nx - i) * 100 /
coursesFunctionsBll.binomialCoefficient(nk, nx))
i += 1
sol2 = round(sol2, 4)
solution = r"a) " + str(sol1) + r"\%, b) " + str(sol2) + r"\%"
question = r"A glasses factory for this month make " + str(
nk
) + r" glasses, the quality engineer finds that " + str(
k
) + r" glasses are broken. for a group of " + str(
nx
) + r" glasses randomly chosen a) which is the probability that " + str(
x) + r" glasses are broken b) less than " + str(
x2) + r" glasses are broken: "
alternatives = coursesFunctionsBll.arithmeticPercentageOptions(
[sol1, sol2], 5, 1)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"a) " + str(alternatives[ta][0]) +
r"\%, b) " + str(alternatives[ta][1]) +
r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def pascalDistributionProblem():
try:
x = random.randint(1, 5) #success
r = random.randint(6, 10) #fails
p = (random.randint(3, 4) + (random.randint(0, 1) * 3)) / 10
q = 1 - p
sol1 = round(
math.factorial(x + r - 1) * (q**(x)) * (p**(r)) * 100 /
(math.factorial(r - 1) * math.factorial(x)), 4)
x2 = random.randint(1, 5)
r2 = random.randint(6, 10)
sol2 = 0
i = 0
while i <= (x2):
sol2 += round(
math.factorial(i + r2 - 1) * (q**(i)) * (p**(r2)) * 100 /
(math.factorial(r2 - 1) * math.factorial(i)), 4)
i += 1
sol2 = round(100 - sol2, 4)
solution = r"a) " + str(sol1) + r"\%, b) " + str(sol2) + r"\%"
question = r"The probability of a soccer player to fail a score opportunity is " + str(
round(p * 100)
) + r"\%. a) which is the probability that the player achieve his " + str(
r) + r"th fail in the " + str(
x + r) + r"th opportunity b) for achieve his " + str(
r2) + r"th fail he needs more than " + str(
x2 + r2) + r" opportunities:"
alternatives = coursesFunctionsBll.arithmeticPercentageOptions(
[sol1, sol2], 5, 1)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"a) " + str(alternatives[ta][0]) +
r"\%, b) " + str(alternatives[ta][1]) +
r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def bayesTheoremProblem():
try:
a = 0.1
b = 0.1
c = 0.1
for i in range(70):
d = random.randint(0, 2)
a = a + (0.01 if d == 0 else 0)
b = b + (0.01 if d == 1 else 0)
c = c + (0.01 if d == 2 else 0)
fa = random.randint(10, 20) / 100
fb = random.randint(10, 20) / 100
fc = random.randint(10, 20) / 100
sol1 = round(100 * (1 - (fc * c / ((fa * a) + (fb * b) + (fc * c)))),
4)
sol2 = round(
100 * ((1 - fb) * b / (1 - (fa * a) - (fb * b) - (fc * c))), 4)
solution = r"a) " + str(sol1) + r"\%, b) " + str(sol2) + r"\%"
question = r"Three machines, A, B and C produce " + str(
round(a * 100)
) + r"\%, " + str(round(b * 100)) + r"\% and " + str(
round(c * 100)
) + r"\% of total items. Percentage of broken production for this machines are " + str(
round(fa * 100)
) + r"\%, " + str(round(fb * 100)) + r"\% and " + str(
round(fc * 100)
) + r"\%.\\ If you choose a random produced item a) which is the probability of don't get a machine C item if the item is broken, b) get a machine B item if the item is not broken:"
alternatives = coursesFunctionsBll.arithmeticPercentageOptions(
[sol1, sol2], 5, 1)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"a) " + str(alternatives[ta][0]) +
r"\%, b) " + str(alternatives[ta][1]) +
r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def averageComparisonProblem():
try:
x = random.randint(70, 90)
y = x + (random.randint(1, 5) * ((random.randint(0, 1) * 2) - 1))
sx = random.randint(5, 10)
sy = random.randint(5, 10)
nx = random.randint(35, 40)
ny = nx + (random.randint(1, 5) * ((random.randint(0, 1) * 2) - 1))
d = random.randint(1, 10) / 10
v = ((sx**2) / nx) + ((sy**2) / ny)
sol = round(
(1 - coursesFunctionsBll.normalDistributionAprox(d, (x - y), v)) *
100, 4)
solution = r"" + str(sol) + r"\%"
question = r'The average argentine lives ' + str(
x
) + r' years, with standard deviation of ' + str(
sx
) + r' years, while the average chilean lives ' + str(
y
) + r' years, with standard deviation of ' + str(
sy
) + r' years. If we make a sample of ' + str(
nx
) + r' argentines and another samples whith ' + str(
ny
) + r' chileans, which is the probability that argentine sample in average lives at least ' + str(
d) + r' years more than chilean sample? choose closest option: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def normalSampleFiniteProblem():
try:
nn = random.randint(50, 100)
n = random.randint(5, 10)
m = random.randint(100, 110)
d = random.randint(1, 6)
x = m - d
v = random.randint(50, 60)
v1 = (v / n) * ((nn - n) / (nn - 1))
sol = round(
(1 - (2 * coursesFunctionsBll.normalDistributionAprox(x, m, v1))) *
100, 4)
solution = r"" + str(sol) + r"\%"
question = r'In a little village with ' + str(
nn
) + r' houses, the average size of a house is ' + str(
m
) + r' mts^{2}, with a variance of ' + str(
v
) + r' mts^{2}. For a sample of ' + str(
n
) + ' different houses, find the probability that sample average size is between ' + str(
m - d) + r' mts^{2} and ' + str(
m + d
) + r' mts^{2}. Choose the option closer to the right solution: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def proportionComparisonProblem():
try:
x = random.randint(40, 60) / 100
y = x + ((random.randint(1, 5) *
((random.randint(0, 1) * 2) - 1)) / 100)
nx = random.randint(35, 40)
ny = nx + (random.randint(1, 5) * ((random.randint(0, 1) * 2) - 1))
d = random.randint(1, 5) / 100
v = ((x * (1 - x)) / nx) + ((y * (1 - y)) / ny)
sol = round(
(coursesFunctionsBll.normalProportionAprox(d,
(x - y), v)) * 100, 4)
solution = r"" + str(sol) + r"\%"
question = r'The ' + str(
round(x * 100)
) + r'\% of ferrari cars are red color, while ' + str(
round(y * 100)
) + r'\% of williams cars are red. For a sample of ' + str(
nx
) + r' ferrari cars and a sample of ' + str(
ny
) + r' williams cars, which is the probability that ferrari sample percentage of red cars is not more than ' + str(
round(d * 100)
) + r'\% above the percentage of red cars in williams sample. Choose closest option: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def proportionFiniteProblem():
try:
nn = random.randint(500, 1000)
n = random.randint(50, 100)
m = random.randint(10, 90) / 100
d = random.randint(1, 5) / 100
x = m - d
v = ((m * (1 - m)) / n) * ((nn - n) / (nn - 1))
sol = round(
(1 -
(2 *
(0.5 - coursesFunctionsBll.normalProportionAprox(x, m, v)))) *
100, 4)
solution = r"" + str(sol) + r"\%"
question = r'In Mexico City the ' + str(
round(m * 100)
) + r'\% hospitals report losses last year. If this city have a total of ' + str(
nn
) + r' hospitals and a research study make a sample of ' + str(
n
) + ' different hospitals, find the probability that the percentage of hospitals in the sample which report losses last year is not between ' + str(
round((m - d) * 100)) + r'\% and ' + str(round(
(m + d) *
100)) + r'\%. Choose the option closer to the right solution: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def normalSampleInfiniteProblem():
try:
n = random.randint(5, 10)
x = random.randint(144, 150)
m = x
while m == x:
m = random.randint(144, 150)
v = random.randint(40, 50)
v1 = v / n
sol = round(
(1 - coursesFunctionsBll.normalDistributionAprox(x, m, v1)) * 100,
4)
solution = r"" + str(sol) + r"\%"
question = r'Average height for school students in New York is ' + str(
m
) + r' cms, with a variance of ' + str(
v
) + r' cms. For a sample of ' + str(
n
) + ' students, find the probability that sample average height is above ' + str(
x) + r' cms. Choose the option closer to the right solution: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def geometryDistributionProblem():
try:
x = random.randint(2, 10)
y = random.randint(2, 10)
p = random.randint(1, 9) / 100
q = 1 - p
sol1 = round((q**(x - 1)) * p * 100, 4)
sol2 = 0
i = 1
while i < y:
sol2 += (q**(i - 1)) * p * 100
i += 1
sol2 = round(sol2, 4)
solution = r"a) " + str(sol1) + r"\%, b) " + str(sol2) + r"\%"
question = r"The probability to call a station radio and get a response is " + str(
round(p * 100)
) + r"\%. a) which is the probability to call and only get a response until the call number " + str(
x) + r", b) get your first response on less than " + str(
y) + r" calls:"
alternatives = coursesFunctionsBll.arithmeticPercentageOptions(
[sol1, sol2], 5, 1)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"a) " + str(alternatives[ta][0]) +
r"\%, b) " + str(alternatives[ta][1]) +
r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def normalDistributionProblem():
try:
x = random.randint(14, 20)
m = x
while m == x:
m = random.randint(14, 20)
v = random.randint(4, 10)
sol = round(
coursesFunctionsBll.normalDistributionAprox(x, m, v) * 100, 4)
solution = r"" + str(sol) + r"\%"
question = r'The average salary in google is ' + str(
m
) + ' thousand dollars, with a variance of \$' + str(
v
) + ' thousand. If distribution follows a normal distribution, which percentage of google employeers get a salary below \$' + str(
x) + ' thousand. Choose the option closer to the right solution: '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
def sampleProportionProblem():
try:
a = random.randint(10, 90) / 100
x = a + (random.randint(1, 5) / 100 * ((random.randint(0, 1) * 2) - 1))
n = random.randint(25, 50)
v = (a * (1 - a)) / n
sol = round(
(1 - coursesFunctionsBll.normalProportionAprox(x, a, v)) * 100, 4)
solution = r"" + str(sol) + r"\%"
question = r'In Colombia ' + str(
round(a * 100)
) + r'\% of citizens think that president is doing a great job. If you make a sample of ' + str(
n
) + r' citizens a) which is the probability that more than ' + str(
round(x * 100)
) + r'\% of sample thinks the same way (choose the closest option): '
alternatives = coursesFunctionsBll.arithmeticPercentageOptions([sol],
5, 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) + r"\%")
options = json.loads(
json.dumps({
'a': tempAlternatives[0],
'b': tempAlternatives[1],
'c': tempAlternatives[2],
'd': tempAlternatives[3],
'e': tempAlternatives[4]
}))
jsonResponse = json.dumps({
"question":
coursesFunctionsBll.replaceSpace(question),
"solution":
coursesFunctionsBll.replaceSpace(solution),
"options":
coursesFunctionsBll.replaceOptions(options)
})
return [jsonResponse]
except Exception as er:
return er
