CS计算机代考程序代写 python #!/usr/bin/env python3

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
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COMP9334 Revision problem 5B_1

Solution

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# import
import numpy as np
from scipy.stats import t

#%% The given data
# store the mean response times in 2-D numpy arrays
# Each column is for a system
mrt_sys = np.array([ [13.64, 12.78, 12.21],
[13.09, 13.98, 13.64],
[13.84, 13.58, 13.09],
[12.28, 14.59, 13.84],
[14.55, 12.72, 12.28] ])
# The required significance
p = 0.95

#%% Solution

# Compute the following differenes
# System 0 – System 1
dt01 = mrt_sys[:,0]- mrt_sys[:,1]
# System 0 – System 2
dt02 = mrt_sys[:,0]- mrt_sys[:,2]
# System 1 – System 2
dt12 = mrt_sys[:,1]- mrt_sys[:,2]

# multiplier for confidence interval
num_tests = mrt_sys.shape[0]
mf = t.ppf(1-(1-p)/2,num_tests-1)/np.sqrt(num_tests)

# To compute the confidence interval
pm1 = np.array([-1,1])

# confidence interval for dt01
ci01 = np.mean(dt01) + pm1 * np.std(dt01, ddof=1) * mf

# confidence interval for dt02
ci02 = np.mean(dt02) + pm1 * np.std(dt02, ddof=1) * mf

# confidence interval for dt12
ci12 = np.mean(dt12) + pm1 * np.std(dt12, ddof=1) * mf