def ComputeSkewnesses():
"""Plots KDE of birthweight and adult weight.
"""
def VertLine(x, y):
thinkplot.Plot([x, x], [0, y], color='0.6', linewidth=1)
live, firsts, others = first.MakeFrames()
data = live.totalwgt_lb.dropna()
print('Birth weight')
mean, median = Summarize(data)
y = 0.35
VertLine(mean, y)
thinkplot.Text(mean - 0.15, 0.1 * y, 'mean', horizontalalignment='right')
VertLine(median, y)
thinkplot.Text(median + 0.1, 0.1 * y, 'median', horizontalalignment='left')
pdf = thinkstats2.EstimatedPdf(data)
thinkplot.Pdf(pdf, label='birth weight')
thinkplot.Save(root='density_totalwgt_kde', xlabel='lbs', ylabel='PDF')
df = brfss.ReadBrfss(nrows=None)
data = df.wtkg2.dropna()
print('Adult weight')
mean, median = Summarize(data)
y = 0.02499
VertLine(mean, y)
thinkplot.Text(mean + 1, 0.1 * y, 'mean', horizontalalignment='left')
VertLine(median, y)
thinkplot.Text(median - 1.5,
0.1 * y,
'median',
horizontalalignment='right')
pdf = thinkstats2.EstimatedPdf(data)
thinkplot.Pdf(pdf, label='adult weight')
thinkplot.Save(root='density_wtkg2_kde',
xlabel='kg',
ylabel='PDF',
xlim=[0, 200])
def MakeCrediblePlot(suite):
"""Makes a plot showing several two-dimensional credible intervals.
suite: Suite
"""
d = dict((pair, 0) for pair in suite.Values())
percentages = [75, 50, 25]
for p in percentages:
interval = suite.MaxLikeInterval(p)
for pair in interval:
d[pair] += 1
thinkplot.Contour(d, contour=False, pcolor=True)
thinkplot.Text(17, 4, '25', color='white')
thinkplot.Text(17, 15, '50', color='white')
thinkplot.Text(17, 30, '75')
thinkplot.Save('paintball5',
xlabel='alpha',
ylabel='beta',
formats=FORMATS,
legend=False)
def PlotSamplingDistributions(live):
"""Plots confidence intervals for the fitted curve and sampling dists.
live: DataFrame
"""
ages = live.agepreg
weights = live.totalwgt_lb
inter, slope = thinkstats2.LeastSquares(ages, weights)
res = thinkstats2.Residuals(ages, weights, inter, slope)
r2 = thinkstats2.CoefDetermination(weights, res)
print('rho', thinkstats2.Corr(ages, weights))
print('R2', r2)
print('R', math.sqrt(r2))
print('Std(ys)', thinkstats2.Std(weights))
print('Std(res)', thinkstats2.Std(res))
# plot the confidence intervals
inters, slopes = SamplingDistributions(live, iters=1001)
PlotConfidenceIntervals(ages,
inters,
slopes,
percent=90,
alpha=0.3,
label='90% CI')
thinkplot.Text(42, 7.53, '90%')
PlotConfidenceIntervals(ages,
inters,
slopes,
percent=50,
alpha=0.5,
label='50% CI')
thinkplot.Text(42, 7.59, '50%')
thinkplot.Save(root='linear3',
xlabel='age (years)',
ylabel='birth weight (lbs)',
legend=False)
# plot the confidence intervals
thinkplot.PrePlot(2)
thinkplot.Scatter(ages, weights, color='gray', alpha=0.1)
PlotConfidenceIntervals(ages, inters, slopes, res=res, alpha=0.2)
PlotConfidenceIntervals(ages, inters, slopes)
thinkplot.Save(root='linear5',
xlabel='age (years)',
ylabel='birth weight (lbs)',
title='90% CI',
axis=[10, 45, 0, 15],
legend=False)
# plot the sampling distribution of slope under null hypothesis
# and alternate hypothesis
sampling_cdf = thinkstats2.Cdf(slopes)
print('p-value, sampling distribution', sampling_cdf[0])
ht = SlopeTest((ages, weights))
pvalue = ht.PValue()
print('p-value, slope test', pvalue)
print('inter', inter, thinkstats2.Mean(inters))
Summarize(inters, inter)
print('slope', slope, thinkstats2.Mean(slopes))
Summarize(slopes, slope)
thinkplot.PrePlot(2)
thinkplot.Plot([0, 0], [0, 1], color='0.8')
ht.PlotCdf(label='null hypothesis')
thinkplot.Cdf(sampling_cdf, label='sampling distribution')
thinkplot.Save(root='linear4',
xlabel='slope (lbs / year)',
ylabel='CDF',
xlim=[-0.03, 0.03],
loc='upper left')
