When performing linear regression, why does scipy use Wald Statistic followed by a t-test, as opposed to Wald Statistic followed by a Wald test?
The following code performs linear regression on two random 5-element vectors using scipy:
import scipy.stats
import random
# Draw some random X and Y
sampleSize = 5
X = [random.uniform(0, 1) for i in range(sampleSize)]
Y = [random.uniform(0, 1) for i in range(sampleSize)]
# named tuple for scipy's linear regression
resultScipy = scipy.stats.linregress(X, Y)
print(resultScipy)
Example output is:
LinregressResult(slope=-0.36654925096390012, intercept=0.67985896267369861, rvalue=-0.32357812225448918, pvalue=0.59531436421063166, stderr=0.61883685654551934)
I very much wanted to know how scipy obtains the pvalue, in this case 0.59531436421063166. Note that the scipy's documentation isn't particularly enlightening:
p-value: float
two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero.
After some python heavy-lifting I've written some code, which fully reproduces scipy's behaviour:
import random
import math
import collections
import scipy.stats
import scipy.stats.distributions as distributions
import numpy as np
import sys
# Draw some random X and Y
sampleSize = 5
X = [random.uniform(0, 1) for i in range(sampleSize)]
Y = [random.uniform(0, 1) for i in range(sampleSize)]
# Compute their average
avgX = sum(X)/sampleSize
avgY = sum(Y)/sampleSize
# Partial steps to compute estimators of linear regression parameters.
XDiff = [X_i - avgX for X_i in X]
XDiffSquared = [i*i for i in XDiff]
YDiff = [Y_i - avgY for Y_i in Y]
# B1 is the estimator of slope.
# B0 is the estimator of intercept.
# r is the estimator of Y given X.
B1 = sum(x * y for x, y in zip(XDiff, YDiff)) / sum(XDiffSquared)
B0 = avgY - B1*avgX
r = lambda x: B0 + B1*x
# Partial steps to compute Wald Statistic.
errs = [y - r(x) for x, y in zip(X, Y)]
errStd = math.sqrt((1/(sampleSize-2))*(sum([err**2 for err in errs])))
XStd = math.sqrt((1/(sampleSize))*sum([diff**2 for diff in XDiff]))
stdB1 = errStd / (XStd * math.sqrt(sampleSize))
# Wald Statistic.
W = (B1 - 0)/stdB1
# pvalue of Wald Test of B1 = 0.
pvalueWald = 2*scipy.stats.norm.cdf(-abs(W))
# pvalue of T test of B1 = 0.
pvalueT = 2*distributions.t.sf(abs(W), sampleSize - 2)
# named tuple for our linear regression, with p-value coming from Wald Statistic
LinregressResult = collections.namedtuple("LinregressResult", ["slope", "intercept", "rvalue", "pvalue", "stderr"])
resultWald = LinregressResult(slope=B1, intercept=B0, rvalue=None, pvalue=pvalueWald, stderr=stdB1)
LinregressResult = collections.namedtuple("LinregressResult", ["slope", "intercept", "rvalue", "pvalue", "stderr"])
resultT = LinregressResult(slope=B1, intercept=B0, rvalue=None, pvalue=pvalueT, stderr=stdB1)
# named tuple for scipy's linear regression
resultScipy = scipy.stats.linregress(X, Y)
print(resultWald)
print(resultT)
print(resultScipy)
The code computes Wald Statistic on the estimator of the slope (gradient), followed by a (sampleSize - 2) degree of freedom t-test.
Why is the p-value computed in such way? My go-to statistics textbook by Larry Wasserman, suggests that I compute the p-value of linear regression in a different way, by computing Wald Statistic, followed by a Wald test.
The two tests give very different results when the sample size is small and the slope is extreme, with Wald's test p-values being consistently lower than t-test's (even by orders of magnitude).
Which approach is better, more correct, and which one should I use?
