def test_CT20():
xk = 1119
slope = mmm.calculate_slope(numbers)
const = mmm.calculate_const(numbers, slope)
variance = mmm.calculate_variance_with_regression(numbers, slope, const)
std_dev = mmm.std_derivation(variance)
student_val = 1.860
assert mmm.calculate_interval(xk, numbers, std_dev, student_val) < 439.5455325
def tp1():
""" Fonction pour le tp1 """
numbers = mr.read_csv_data("test_tp1.csv")
mean = mmm.mean(numbers)
variance = mmm.variance(numbers, mean)
std_der = mmm.std_derivation(variance)
print("moyenne: {:10.2f}".format(mean))
print("variance: \t{:10.2f}".format(variance))
print("ecart-type: {:7.2f}".format(std_der))
def tp5():
""" Fonction pour le tp5 """
numbers = mr.read_csv_data("test_tp5.csv")
xk = float(
mr.get_user_input(
"Quelle valeur voulez-vous chercher l'intervalle de confiance?"))
slope = mmm.calculate_slope(numbers)
const = mmm.calculate_const(numbers, slope)
variance = mmm.calculate_variance_with_regression(numbers, slope, const)
std_dev = mmm.std_derivation(variance)
student_val = mmm.get_student_with_alpha()
interval = mmm.calculate_interval(xk, numbers, std_dev, student_val)
yk = const + xk * slope
bounds = mmm.calculate_bounds_interval(interval, yk)
print("Intevalle = {:0.6f}".format(interval))
print("Limite supérieure = {:0.6f}".format(bounds[0]))
print("Limite inférieure = {:0.6f}".format(bounds[1]))
def test_CT4():
numbers = [186, 699, 132, 272, 291, 331, 199, 1890, 788, 1601]
mean = mmm.mean(numbers)
var = mmm.variance(numbers, mean)
assert mmm.std_derivation(var) > 625