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#Without using numpy or statistics librariesimportsysfromcollectionsimportdefaultdictforlineinsys.stdin:nums=line.split(" ")mean=0mode=0median=0deviation=0lowerconfidence=0upperconfidence=0d=defaultdict(int)forninnums:mean+=int(n)d[n]+=1n=len(nums)mean=mean/nnums=[int(x)forxinnums]median=(sorted(nums)[n//2]) if n % 2 else (sorted(nums)[n//2] + sorted(nums)[n//2 - 1])/ 2modeCandidates=[]freq=max(d.values())forkind:iffreq==d[k]:modeCandidates.append(int(k))iflen(k)==1:mode=kelse:mode=sorted(modeCandidates)[0]deviation=[(x-mean)**2forxinnums]deviation=(sum(deviation)/n)**.5lowerconfidence=mean-1.96*(deviation)/(len(nums)**.5)upperconfidence=mean+1.96*(deviation)/(len(nums)**.5)print('{:.1f}'.format(mean))print('{:.1f}'.format(median))print('{:.0f}'.format(mode))print('{:.1f}'.format(deviation))print(('{lowerconfidence:.1f}{upperconfidence:.1f}').format(lowerconfidence=lowerconfidence,upperconfidence=upperconfidence))
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Basic Statistics Warmup
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