在Python中随着时间的推移ANOVA，我在做什么？
我真的很喜欢统计数据，但是已经超过6年了。我无法确定我需要什么样的测试，以及用于这些问题的最好的numpy / scipy / R函数。
我有一个访问者及其相应属性的表格（例如“Browser = Mozilla，Referrer = Google”），以及每位访问者的变量值（例如5美元），随着时间的推移分组为数据点。
我的目标是：
A）找到最重要的财产家庭，得分为“家庭有多重要”
我想绘制的结论示例*：
Referrer has 10x larger effect size upon value-per-visitor than Browser
=> PropertyFamily('browser').significance = 1
=> PropertyFamily('referrer').significance = 10
GIVEN THAT Value:Baseline => $5/hit
5 hits from IE @ $5/hit (equal to baseline) => no significance
1 hit from Netscape @ $0 => little significance (not enough data)
10 hits from FF @ $10/hit => HIGH significance (hits and delta_value both high)
data = {
'baseline': [(hits, value, day) for hits, value, day in last_thirty_days('baseline')],
'browser': {
'mozilla': [(hits, value, day) for hits, value, day in last_thirty_days('browser', 'mozilla')],
... etc ...
}
}
... etc ...
# Runs after each (hits, value, date) tuple has been grouped
# into corresponding "plot points", as they would appear on a graph
pp = PlotPoint(property, date, hits, value)
pp.epc = float(pp.value/pp.hits) if pp.hits else 0
# Finds PlotPoint('baseline', date)
# if pp = PlotPoint('firefox', '1-1-10')
# then pp.baseline == PlotPoint('baseline', '1-1-10')
baseline = pp.baseline()
if baseline.hits == 0:
volume_ratio = 0
else:
volume_ratio = round(100*pp.hits/baseline.hits)
value_ratio = baseline.epc - pp.epc
# Make up a significance value --
# e.g. (10% of visitors * ($1 delta from baseline))^2
pp.significance = math.sqrt(volume_ratio * value_ratio **2)
# OK, we have values for each plotpoint, now sum them up
# to get values for the whole property (over a 30day period)
pps = property.plotpoint_set.all()
property.hits = sum([p.hits for p in pps])
property.value = sum([p.value for p in pps])
property.epc = property.value/property.hits
value_delta = baseline.epc - property.epc
# Make up a significance for the Property, based on each point's significance
property.significance = math.log(sum(
[sss.significance**2 for sss in pps]
)*abs(value_delta)+1)
没有找到相关结果
已邀请:
1 个回复
疾很毋悲
请注意，这一切都假设您的值是正态分布的。你应该检查这个假设（是一个好的开始。）。如果它们不是，要么找到规范化它们的方法（尝试记录日志），要么使用适当的非参数测试。 哦，最后，如果你想获得有关统计数据的建议，那么有一个专门用于此的姐妹网站：https：//stats.stackexchange.com/