def EstimateGoals(lam, m):
def VertLine(x, y=1):
thinkplot.Plot([x, x], [0, y], color='0.8', linewidth=3)
lams = []
for _ in range(m):
goals = SimulateGame(lam)
lams.append(goals)
print('RMSE of Goals: ', estimation.RMSE(lams, lam))
print('Mean Error of Goals: ', estimation.MeanError(lams, lam))
cdf = thinkstats2.Cdf(lams)
ci = cdf.Percentile(5), cdf.Percentile(95)
VertLine(ci[0])
VertLine(ci[1])
thinkplot.Cdf(cdf)
#thinkplot.Show(xlabel = 'Goals', ylabel = 'CumProb', title = 'Sampling Distribution, lam = ' + str(lam))
thinkplot.SaveFormat(root='Q9_sampling_dist',
fmt='png',
xlabel='Goals',
ylabel='CumProb',
title='Sampling Distribution, lam = ' + str(lam))
import nsfg
import thinkstats2
import thinkplot
import probability
p = nsfg.ReadFemResp()
act_pmf = thinkstats2.Pmf(p.numkdhh, label='actual')
print(act_pmf)
bias_pmf = probability.BiasPmf(act_pmf, label='observed')
print(bias_pmf)
print('Mean number of children, actual: ', act_pmf.Mean())
print('Mean number of children, biased: ', bias_pmf.Mean())
fig = thinkplot.Pmfs([act_pmf, bias_pmf])
#thinkplot.show(xlabel='No. of Children', ylabel='pmf')
thinkplot.SaveFormat(root = 'act_vs_biased',
fmt = 'png',
xlabel = 'No. of Children',
ylabel = 'pmf')
thinkstats2.RandomSeed(1)
ns = np.arange(10, 1000, 10)
stderrs = []
cis = []
cdfs = []
for n in ns:
cdf, stderr, ci = Simulate_Sample(2, n, m=1000)
cdfs.append(cdf)
stderrs.append(stderr)
cis.append(ci)
print('Standard error, n = 10: ', stderrs[0])
print('Confidence interval, n = 10: ', cis[0])
idx = [i for i, x in enumerate(ns) if x == 100]
print('Standard error, n = 100: ', stderrs[list(ns).index(100)])
print('Confidence interval, n = 100: ', cis[list(ns).index(100)])
print('Standard error, n = 1000: ', stderrs[-1])
print('Confidence interval, n = 1000: ', cis[-1])
thinkplot.Cdf(cdfs[0])
#thinkplot.Show(xlabel='x', ylabel='CumProb')
thinkplot.SaveFormat(root='Q8_cdf', fmt='png', xlabel='x', ylabel='CumProb')
thinkplot.Plot(ns, stderrs)
#thinkplot.Show(xlabel='n', ylabel='Standard Error')
thinkplot.SaveFormat(root='Q8_stderr',
fmt='png',
xlabel='n',
ylabel='Standard Error')
import DataSet
import pdb
H155 = DataSet.DataSet('h155.pkl')
df = H155.df
errorlist = []
totalvars = len(H155.varnames)
for index, var in enumerate(H155.varnames):
print '\nPlotting %s, variable %d out of %d.' % (var, index + 1, totalvars)
thisplot = df[df[var] > 0][var]
try:
thishist = ts2.Cdf(thisplot)
tplt.Cdf(thishist)
tplt.Config(title=var, ylabel='Probability', xlabel='Response')
except Exception, e:
print 'ERROR CREATING %s' % var
errorlist.append((var, e))
else:
tplt.SaveFormat('graphs/cdftests2/%s' % var, 'png')
tplt.Clf()
# Exclude error codes
# Title the graphs
# place counter in for loop
print errorlist
with open('graphs/cdftests2/errorlog.txt', 'wb+') as wrfile:
for err in errorlist:
wrfile.write("%s\n" % err)
import first
import numpy as np
live, firsts, others = first.MakeFrames()
live = live.dropna(subset=['agepreg', 'totalwgt_lb'])
rho = thinkstats2.Corr(live.agepreg, live.totalwgt_lb)
rho_s = thinkstats2.SpearmanCorr(live.agepreg, live.totalwgt_lb)
print('Pearson\'s Correlation, Mother\'s age and Birth weight: ', rho)
print('Spearman\'s Rank Correlation, Mother\'s age and Birth weight: ', rho_s)
thinkplot.LEGEND = False
thinkplot.Scatter(live.agepreg, live.totalwgt_lb)
#thinkplot.Show(xlabel = 'Mother\'s age', ylabel = 'Birth weight')
thinkplot.SaveFormat(root='age_weight_scatter',
fmt='png',
xlabel='Mothers\'s age',
ylabel='Birth weight')
thinkplot.LEGEND = True
bins = np.arange(10, 45, 2.5)
indices = np.digitize(live.agepreg, bins)
groups = live.groupby(indices)
ages = [group.agepreg.mean() for i, group in groups]
cdfs = [thinkstats2.Cdf(group.totalwgt_lb) for i, group in groups]
for percent in [75, 50, 25]:
weights = [cdf.Percentile(percent) for cdf in cdfs]
label = '%dth' % percent
thinkplot.Plot(ages, weights, label=label)
#thinkplot.Show(xlabel = 'Mother\'s age', ylabel = 'Birth weight')
thinkplot.SaveFormat(root='age_weight_percentiles',
fmt='png',
import nsfg
import thinkstats2
import thinkplot
import random
import pandas as pd
import numpy as np
def rand_list(n):
rands = [random.random() for _ in range(n)]
return rands
rands = rand_list(1000)
pmf = thinkstats2.Pmf(rands)
cdf = thinkstats2.Cdf(rands)
thinkplot.Pmf(pmf)
#thinkplot.Show(xlabel='x', ylabel='pmf')
thinkplot.SaveFormat(root='Q4_2pmf', fmt='png', xlabel='x', ylabel='Prob')
thinkplot.Cdf(cdf)
#thinkplot.Show(xlabel = 'x', ylabel = 'cdf')
thinkplot.SaveFormat(root='Q4_2cdf', fmt='png', xlabel='x', ylabel='CumProb')