Tue Oct 19 11:58:48 2021 log_normal_test Python version: 3.6.9 Test log_normal(). log_normal_cdf_test Python version: 3.6.9 log_normal_cdf evaluates the Log Normal CDF log_normal_cdf_inv inverts the Log Normal CDF. log_normal_pdf evaluates the Log Normal PDF PDF parameter MU = 1 PDF parameter SIGMA = 0.5 X PDF CDF CDF_inv 4.80671 0.0866714 0.872862 4.80671 3.18548 0.238185 0.624457 3.18548 3.84239 0.163417 0.755591 3.84239 3.46542 0.204628 0.686398 3.46542 3.04806 0.254993 0.590569 3.04806 5.6624 0.0479928 0.928906 5.6624 3.95911 0.151891 0.773986 3.95911 4.57644 0.101324 0.851257 4.57644 1.18923 0.171026 0.049126 1.18923 2.47719 0.316584 0.42632 2.47719 log_normal_cdf_test Normal end of execution. log_normal_sample_test Python version: 3.6.9 log_normal_mean computes the Log Normal mean log_normal_sample samples the Log Normal distribution log_normal_variance computes the Log Normal variance. PDF parameter MU = 1 PDF parameter SIGMA = 0.5 PDF mean = 3.08022 PDF variance = 2.69476 Sample size = 1000 Sample mean = 3.08067 Sample variance = 2.61274 Sample maximum = 12.0838 Sample minimum = 0.581668 log_normal_sample_test Normal end of execution. normal_01_cdf_test Python version: 3.6.9 normal_01_cdf evaluates the Normal 01 CDF normal_01_cdf_inv inverts the Normal 01 CDF. normal_01_pdf evaluates the Normal 01 PDF X PDF CDF CDF_inv -0.207463 0.390449 0.417824 -0.207463 0.402784 0.367859 0.656446 0.402784 -1.30542 0.170162 0.0958743 -1.30542 0.820518 0.284915 0.79404 0.820518 -0.213105 0.389986 0.415622 -0.213105 -0.802515 0.289108 0.211128 -0.802515 -0.507625 0.350716 0.305858 -0.507625 -0.816118 0.285943 0.207216 -0.816118 -1.42401 0.144736 0.0772218 -1.42401 0.932896 0.258183 0.824563 0.932896 normal_01_cdf_test Normal end of execution. normal_01_cdf_values_test: Python version: 3.6.9 normal_01_cdf_values stores values of the unit normal CDF. X normal_01_cdf(X) 0.000000 0.5000000000000000 0.100000 0.5398278372770290 0.200000 0.5792597094391030 0.300000 0.6179114221889526 0.400000 0.6554217416103242 0.500000 0.6914624612740131 0.600000 0.7257468822499270 0.700000 0.7580363477769270 0.800000 0.7881446014166033 0.900000 0.8159398746532405 1.000000 0.8413447460685429 1.500000 0.9331927987311419 2.000000 0.9772498680518208 2.500000 0.9937903346742240 3.000000 0.9986501019683699 3.500000 0.9997673709209645 4.000000 0.9999683287581669 normal_01_cdf_values_test: Normal end of execution. normal_01_sample_test Python version: 3.6.9 normal_01_mean computes the Normal 01 mean normal_01_sample samples the Normal 01 distribution normal_01_variance returns the Normal 01 variance. PDF mean = 0 PDF variance = 1 Sample size = 1000 Sample mean = 0.000902678 Sample variance = 0.985311 Sample maximum = 3.7677 Sample minimum = -2.85612 normal_01_sample_test Normal end of execution. normal_cdf_test Python version: 3.6.9 normal_cdf evaluates the Normal CDF normal_cdf_inv inverts the Normal CDF. normal_pdf evaluates the Normal PDF PDF parameter A = 100 PDF parameter B = 15 X PDF CDF CDF_inv 118.98 0.0119438 0.897127 118.98 102.289 0.0262882 0.560654 102.289 102.046 0.0263498 0.554258 102.046 84.4548 0.0155453 0.15002 84.4548 113.632 0.0175986 0.818269 113.632 91.772 0.0228814 0.291663 91.772 86.0185 0.017225 0.175643 86.0185 94.3779 0.0247921 0.353902 94.3779 96.4185 0.0258487 0.405644 96.4185 98.6672 0.0264914 0.4646 98.6672 normal_cdf_test Normal end of execution. normal_sample_test Python version: 3.6.9 normal_mean computes the Normal mean normal_sample samples the Normal distribution normal_variance returns the Normal variance. PDF parameter A = 100 PDF parameter B = 15 PDF mean = 100 PDF variance = 225 Sample size = 1000 Sample mean = 99.757 Sample variance = 239.519 Sample maximum = 158.501 Sample minimum = 50.1704 normal_sample_test Normal end of execution. r8poly_print_test Python version: 3.6.9 r8poly_print prints an R8POLY. The R8POLY: p(x) = 9 * x^5 + 0.78 * x^4 + 56 * x^2 - 3.4 * x + 12 r8poly_print_test: Normal end of execution. r8poly_value_horner_test Python version: 3.6.9 r8poly_value_horner evaluates a polynomial at a point using Horners method. The polynomial coefficients: p(x) = 1 * x^4 - 10 * x^3 + 35 * x^2 - 50 * x + 24 I X P(X) 0 0.0000 24 1 0.3333 10.8642 2 0.6667 3.45679 3 1.0000 0 4 1.3333 -0.987654 5 1.6667 -0.691358 6 2.0000 0 7 2.3333 0.493827 8 2.6667 0.493827 9 3.0000 0 10 3.3333 -0.691358 11 3.6667 -0.987654 12 4.0000 0 13 4.3333 3.45679 14 4.6667 10.8642 15 5.0000 24 r8poly_value_horner_test: Normal end of execution. log_normal_test: Normal end of execution. Tue Oct 19 11:58:48 2021