bpo-44151: linear_regression() minor API improvements (GH-26199)

2 files changed, 15 insertions, 16 deletions

diff --git a/Lib/statistics.py b/Lib/statistics.py
index bd3813c..c505a05 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -94,7 +94,7 @@ for two inputs:
 >>> correlation(x, y)  #doctest: +ELLIPSIS
 0.31622776601...
 >>> linear_regression(x, y)  #doctest:
-LinearRegression(intercept=1.5, slope=0.1)
+LinearRegression(slope=0.1, intercept=1.5)
 
 
 Exceptions
@@ -932,18 +932,18 @@ def correlation(x, y, /):
         raise StatisticsError('at least one of the inputs is constant')
 
 
-LinearRegression = namedtuple('LinearRegression', ['intercept', 'slope'])
+LinearRegression = namedtuple('LinearRegression', ('slope', 'intercept'))
 
 
-def linear_regression(regressor, dependent_variable, /):
+def linear_regression(x, y, /):
     """Intercept and slope for simple linear regression
 
     Return the intercept and slope of simple linear regression
     parameters estimated using ordinary least squares. Simple linear
-    regression describes relationship between *regressor* and
-    *dependent variable* in terms of linear function:
+    regression describes relationship between *x* and
+    *y* in terms of linear function:
 
-        dependent_variable = intercept + slope * regressor + noise
+        y = intercept + slope * x + noise
 
     where *intercept* and *slope* are the regression parameters that are
     estimated, and noise represents the variability of the data that was
@@ -953,19 +953,18 @@ def linear_regression(regressor, dependent_variable, /):
 
     The parameters are returned as a named tuple.
 
-    >>> regressor = [1, 2, 3, 4, 5]
+    >>> x = [1, 2, 3, 4, 5]
     >>> noise = NormalDist().samples(5, seed=42)
-    >>> dependent_variable = [2 + 3 * regressor[i] + noise[i] for i in range(5)]
-    >>> linear_regression(regressor, dependent_variable)  #doctest: +ELLIPSIS
-    LinearRegression(intercept=1.75684970486..., slope=3.09078914170...)
+    >>> y = [2 + 3 * x[i] + noise[i] for i in range(5)]
+    >>> linear_regression(x, y)  #doctest: +ELLIPSIS
+    LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)
 
     """
-    n = len(regressor)
-    if len(dependent_variable) != n:
+    n = len(x)
+    if len(y) != n:
         raise StatisticsError('linear regression requires that both inputs have same number of data points')
     if n < 2:
         raise StatisticsError('linear regression requires at least two data points')
-    x, y = regressor, dependent_variable
     xbar = fsum(x) / n
     ybar = fsum(y) / n
     sxy = fsum((xi - xbar) * (yi - ybar) for xi, yi in zip(x, y))
@@ -973,9 +972,9 @@ def linear_regression(regressor, dependent_variable, /):
     try:
         slope = sxy / s2x   # equivalent to:  covariance(x, y) / variance(x)
     except ZeroDivisionError:
-        raise StatisticsError('regressor is constant')
+        raise StatisticsError('x is constant')
     intercept = ybar - slope * xbar
-    return LinearRegression(intercept=intercept, slope=slope)
+    return LinearRegression(slope=slope, intercept=intercept)
 
 
 ## Normal Distribution #####################################################
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 3e6e17a..a7cb027 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2501,7 +2501,7 @@ class TestLinearRegression(unittest.TestCase):
             ([1, 2, 3], [21, 22, 23], 20, 1),
             ([1, 2, 3], [5.1, 5.2, 5.3], 5, 0.1),
         ]:
-            intercept, slope = statistics.linear_regression(x, y)
+            slope, intercept = statistics.linear_regression(x, y)
             self.assertAlmostEqual(intercept, true_intercept)
             self.assertAlmostEqual(slope, true_slope)