def confidenceComparisonSamplesProblem():
try:
n1 = random.randint(41, 50)
n2 = random.randint(31, 40)
u1 = random.randint(31, 40)
u2 = random.randint(41, 50)
s1 = random.randint(2, 5)
s2 = random.randint(6, 9)
pOptions = [0.2, 0.1, 0.05, 0.02, 0.01, 0.005]
p = pOptions[random.randint(0, 5)]
z = coursesFunctionsBll.normalDistributionInverse(1 - (p / 2))
sol = round(z * ((((s1**2) / n1) + ((s2**2) / n2))**0.5), 4)
solution = r"" + str(abs(u1 - u2)) + r" \pm " + str(sol) + r""
question = r'In a software company the average age of programmers have standard deviation of ' + str(
s1
) + r' years, and for human resources they have a standard deviation of ' + str(
s2
) + r' years. \\ In a sample of ' + str(
n1
) + r' programmers the average age was ' + str(
u1
) + r' years, and in a sample of ' + str(
n2
) + r' human resources employees the average age was ' + str(
u2
) + r' years. \\ Find the confidence interval for the difference of height with a level of ' + str(
round(100 * (1 - p), 2)) + r'\%. Choose the closest option: '
alternatives = coursesFunctionsBll.multipleOptions([sol], 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(abs(u1 - u2)) + r" \pm " +
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 confidenceProportionProblem():
try:
n = random.randint(40, 60)
u = random.randint(20, 30)
pr = u / n
pOptions = [0.2, 0.1, 0.05, 0.02, 0.01, 0.005]
p = pOptions[random.randint(0, 5)]
z = coursesFunctionsBll.normalDistributionInverse(1 - (p / 2))
sol1 = round(100 * pr, 4)
sol2 = round(z * 100 * ((pr * (1 - pr) / n)**0.5), 4)
solution = r"" + str(sol1) + r"\% \pm " + str(sol2) + r"\%"
question = r'In colombia the police officers are being spied by a criminal organization, but the proportion is unknown. \\ A search founds that in a sample of ' + str(
n
) + r' police officers they found that ' + str(
u
) + r' police officers are spied . \\ Find the confidence limit with a ' + str(
round(100 * (1 - p), 1)
) + r'\% for the real proportion of police officers spied (choose the closest option): '
alternatives = coursesFunctionsBll.percentageProportionOptions(
[sol1, sol2], 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(alternatives[ta][0]) +
r"\% \pm " + 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 confidenceBigSamplesProblem():
try:
x = random.randint(50, 100)
m = random.randint(150, 175)
s = random.randint(5, 10)
pOptions = [0.2, 0.1, 0.05, 0.02, 0.01, 0.005]
p = pOptions[random.randint(0, 5)]
z = coursesFunctionsBll.normalDistributionInverse(1 - (p / 2))
sol = round(z * s / (x**0.5), 4)
solution = r"" + str(m) + r" \pm " + str(sol) + r""
question = r'A sample of ' + str(
x
) + r' school girls give an average height of ' + str(
m
) + r' cms and standard deviation of ' + str(
s
) + r' cms. Find the confidence limit with a ' + str(
round(100 * (1 - p), 1)
) + r'\% for the real height of all the school girls (choose the closest option): '
alternatives = coursesFunctionsBll.multipleOptions([sol], 5)
tempAlternatives = []
for ta in range(5):
tempAlternatives.append(r"" + str(m) + r" \pm " +
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 proportionSizeProblem1():
try:
nn = random.randint(5000, 10000)
p = random.randint(10, 90) / 100
q = 1 - p
e = random.randint(5, 10) / 100
a = random.randint(1, 10) / 100
z = coursesFunctionsBll.normalDistributionInverse(1 - (a / 2))
sol = round((((z**2) * p * q * nn) + (nn * (e**2))) /
((nn * (e**2)) + ((z**2) * p * q)))
solution = r"" + str(sol) + r""
question = r'In a city a previous study shows that ' + str(
round(p * 100)
) + r'\% of population are fans of A team. A survey wants to comfirm this previous result. The city have ' + str(
nn
) + r' citizens. Which should be the size of the sample for the new study for an error limit of ' + str(
e) + r' and a significance level of ' + str(round(
a * 100)) + r'\%. Choose the closest option: '
alternatives = coursesFunctionsBll.positiveArithmeticOptions([sol], 5,
10)
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 sampleSizeProblem():
try:
p = random.randint(500, 1000)
s = random.randint(100, 150)
e = random.randint(20, 30)
a = random.randint(1, 10) / 100
z = coursesFunctionsBll.normalDistributionInverse(1 - (a / 2))
sol = round(
(z**2) * (s**2) * p / (((p - 1) * (e**2)) + ((z**2) * (s**2))))
solution = r"" + str(sol) + r""
question = r'A bank wants to know the average saving per client. A previous study shows the standard deviation on clients saving is \$' + str(
s
) + r'. The bank has ' + str(
p
) + r' clients. What should be the size of the sample for an error limit of ' + str(
e) + r' and a significance level of ' + str(round(
a * 100)) + r'\%. Choose the closest answer: '
alternatives = coursesFunctionsBll.positiveArithmeticOptions([sol], 5,
10)
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 proportionSizeProblem2():
try:
p = random.randint(10, 90) / 100
q = 1 - p
e = random.randint(5, 10) / 100
a = random.randint(1, 10) / 100
z = coursesFunctionsBll.normalDistributionInverse(1 - (a / 2))
sol = round(((z**2) * p * q) / (e**2))
solution = r"" + str(sol) + r""
question = r'An international statistical service founds that ' + str(
round(p * 100)
) + r'\% of population in country B lives in poverty. A new research wants to comfirm the former conclusion. Which should be the size of the sample for the new study for a error limit of ' + str(
e) + r' and a significance level of ' + str(round(
a * 100)) + r'\%. Choose the closest option: '
alternatives = coursesFunctionsBll.positiveArithmeticOptions([sol], 5,
10)
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 sampleSizeProblem2():
try:
s = random.randint(20, 30)
e = random.randint(5, 10)
a = random.randint(1, 10) / 100
z = coursesFunctionsBll.normalDistributionInverse(1 - (a / 2))
sol = round((z**2) * (s**2) / e**2)
solution = r"" + str(sol) + r""
question = r'A research shows that average height of basketball players follows a normal distribution with standard deviation of ' + str(
s
) + r' cms. If a research group wants to find the average size, what should be the size of the sample for an error limit of ' + str(
e) + r' and a significance level of ' + str(round(
a * 100)) + r'\% . Choose the closest option: '
alternatives = coursesFunctionsBll.positiveArithmeticOptions([sol], 5,
10)
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