data_set_y = [
142000, 144000, 151000, 150000, 139000, 169000, 126000, 142900, 163000,
169000, 149000
]
# data_set_y = [73.13,56.56, 21.48,16.81,115.56,53.7,20.15]
arth_mean_x1 = mt.art_mean(data_set_x1)
arth_mean_x2 = mt.art_mean(data_set_x2)
arth_mean_x3 = mt.art_mean(data_set_x3)
arth_mean_x4 = mt.art_mean(data_set_x4)
arth_mean_y = mt.art_mean(data_set_y)
# geo_mean_x = mt.geo_mean(data_set_x)
# geo_mean_y = mt.geo_mean(data_set_y)
sum_x1_pow_2 = mt.sum_list_square(data_set_x1)
sum_x2_pow_2 = mt.sum_list_square(data_set_x2)
sum_x3_pow_2 = mt.sum_list_square(data_set_x3)
sum_x3_pow_4 = mt.sum_list_square(data_set_x4)
sum_y_pow_2 = mt.sum_list_square(data_set_y)
sum_x1 = mt.sum_list(data_set_x1)
sum_x2 = mt.sum_list(data_set_x2)
sum_x3 = mt.sum_list(data_set_x3)
sum_x4 = mt.sum_list(data_set_x4)
sum_y = mt.sum_list(data_set_y)
sum_x1_y = mt.sum_list1_dot_list2(data_set_x1, data_set_y)
sum_x2_y = mt.sum_list1_dot_list2(data_set_x2, data_set_y)
sum_x3_y = mt.sum_list1_dot_list2(data_set_x3, data_set_y)
sum_x4_y = mt.sum_list1_dot_list2(data_set_x4, data_set_y)
""" brutal force to avoid errors """ data_set_x1 = np.array(x, dtype=float) #transform your data in a numpy array of floats data_set_y = np.array(y, dtype=float) #so the curve_fit can work arth_mean_x1 = mt.art_mean(data_set_x1) arth_mean_y = mt.art_mean(data_set_y) # geo_mean_x = mt.geo_mean(data_set_x) # geo_mean_y = mt.geo_mean(data_set_y) sum_x1_pow_2 = mt.sum_list_square(data_set_x1) sum_y_pow_2 = mt.sum_list_square(data_set_y) sum_x1 = mt.sum_list(data_set_x1) sum_y = mt.sum_list(data_set_y) sum_x1_y = mt.sum_list1_dot_list2(data_set_x1,data_set_y) var_x1 = mt.variance_list(data_set_x1) var_y = mt.variance_list(data_set_y) cov_x1_y = mt.covariance_list1_list2(data_set_x1,data_set_y)