docs: update OLS now intercept (aka constant) returned first

Author: mohawk2

lib/PDL/Stats/GLM.pd

@@ -418,67 +418,57 @@ Usage:
     pdl> p "$_\t@{[$m{$_} =~ /^\n*(.*?)\n*\z/s]}\n" for sort keys %m
 
     # 2 persons' ratings, each fitted with 3 "different" models
-
-    F
-    [
+    F   [
      [ 38.314159  25.087209]
      [ 38.314159  25.087209]
      [ 38.314159  25.087209]
     ]
 
     # df is the same across dv and iv models
-
     F_df    [2 7]
-    F_p
-    [
+    F_p [
      [0.00016967051 0.00064215074]
      [0.00016967051 0.00064215074]
      [0.00016967051 0.00064215074]
     ]
 
-    R2
-    [
+    R2  [
      [ 0.9162963 0.87756762]
      [ 0.9162963 0.87756762]
      [ 0.9162963 0.87756762]
     ]
 
-    b
-    [  # linear      quadratic     constant
+    b   [ # constant     linear      quadratic
      [
-      [  0.99015152 -0.056818182   0.66363636]    # person 1
-      [  0.18939394  0.022727273          1.4]    # person 2
+      [ 0.66363636    0.99015152   -0.056818182] # person 1
+      [        1.4    0.18939394    0.022727273] # person 2
      ]
      [
-      [  0.49507576 -0.028409091   0.66363636]
-      [  0.09469697  0.011363636          1.4]
+      [ 0.66363636    0.49507576   -0.028409091]
+      [        1.4    0.09469697    0.011363636]
      ]
      [
-      [  0.33005051 -0.018939394   0.66363636]
-      [ 0.063131313 0.0075757576          1.4]
+      [ 0.66363636    0.33005051   -0.018939394]
+      [        1.4   0.063131313   0.0075757576]
      ]
     ]
 
     # our novice modeler realizes at this point that
     # the 3 models only differ in the scaling of the IV coefficients
-
-    ss_model
-    [
+    ss_model    [
      [ 20.616667  13.075758]
      [ 20.616667  13.075758]
      [ 20.616667  13.075758]
     ]
 
-    ss_residual
-    [
+    ss_residual [
      [ 1.8833333  1.8242424]
      [ 1.8833333  1.8242424]
      [ 1.8833333  1.8242424]
     ]
 
-    ss_total        [22.5 14.9]
-    y_pred
-    [
+    ss_total    [22.5 14.9]
+    y_pred      [
      [
       [0.66363636  1.5969697  2.4166667  3.1227273  ...  4.9727273]
     ...
@@ -1446,8 +1436,7 @@ Usage:
     # suppose this is a person's ratings for top 10 box office movies
     # ascending sorted by box office
 
-    pdl> p $y = qsort ceil( random(10) * 5 )
-    [1 1 2 2 2 2 4 4 5 5]
+    pdl> $y = pdl '[1 1 2 2 2 2 4 4 5 5]'
 
     # construct IV with linear and quadratic component
 
@@ -1459,19 +1448,18 @@ Usage:
 
     pdl> %m = $y->ols( $x )
 
-    pdl> p "$_\t$m{$_}\n" for sort keys %m
+    pdl> p "$_\t@{[$m{$_} =~ /^\n*(.*?)\n*\z/s]}\n" for sort keys %m
 
     F       40.4225352112676
     F_df    [2 7]
     F_p     0.000142834216344756
     R2      0.920314253647587
 
-    # coeff  linear     quadratic  constant
-
-    b       [0.21212121 0.03030303 0.98181818]
-    b_p     [0.32800118 0.20303404 0.039910509]
-    b_se    [0.20174693 0.021579989 0.38987581]
-    b_t     [ 1.0514223   1.404219  2.5182844]
+    # coeff  constant   linear    quadratic
+    b        [0.981818  0.212121  0.030303]
+    b_p      [0.039910  0.328001  0.203034]
+    b_se     [0.389875  0.201746  0.021579]
+    b_t      [2.518284  1.051422  1.404218]
     ss_model        19.8787878787879
     ss_residual     1.72121212121212
     ss_total        21.6