[lucene-solr] branch visual-guide updated: Visual Guide: Improve sampling docs

[jb...@apache.org]

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jbernste pushed a commit to branch visual-guide
in repository https://gitbox.apache.org/repos/asf/lucene-solr.git


The following commit(s) were added to refs/heads/visual-guide by this push:
     new 1aac8ad  Visual Guide: Improve sampling docs
1aac8ad is described below

commit 1aac8ad6a49b756e270e409d366aa68006edf91e
Author: Joel Bernstein <jb...@apache.org>
AuthorDate: Sun Dec 15 13:51:39 2019 -0500

    Visual Guide: Improve sampling docs
---
 .../src/images/math-expressions/univariate.png     | Bin 169949 -> 241993 bytes
 solr/solr-ref-guide/src/search-sample.adoc         |  34 +++++++++++++++------
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/solr/solr-ref-guide/src/images/math-expressions/univariate.png b/solr/solr-ref-guide/src/images/math-expressions/univariate.png
index e2ea1c2..c935639 100644
Binary files a/solr/solr-ref-guide/src/images/math-expressions/univariate.png and b/solr/solr-ref-guide/src/images/math-expressions/univariate.png differ
diff --git a/solr/solr-ref-guide/src/search-sample.adoc b/solr/solr-ref-guide/src/search-sample.adoc
index fe3ca96..1db0a3a 100644
--- a/solr/solr-ref-guide/src/search-sample.adoc
+++ b/solr/solr-ref-guide/src/search-sample.adoc
@@ -90,16 +90,26 @@ and Machine Learning sections of the user guide.
 
 === Univariate Scatter Plots
 
-In the example below the `random` function is used to draw 500 random samples
-from the *logs* collection. The query matches all log records and
-the *filesize_d* field is returned with each sample.
+In the example below the `random` function is called in its simplest form with just a collection name as the parameter.
 
-The visualization below shows the *filesize_d* field plotted on both the *x* and *y*
-axis which produces a diagonal line with a slope of 1. By studying the scatter plot
-we can learn a number of things about the distribution of the *filesize_d*
-variable:
 
-* The sample set ranges from 34,070 to 46,456.
+When called with no other parameters the `random` function returns a random sample
+of 500 records with all fields from
+the collection. When called without the *field list* parameter the `random` function also generates
+a sequence, 0-499 in this case, which can be used
+for plotting the `x` axis. This sequence is
+returned in a field called `x`.
+
+The visualization below shows a scatter plot with the *filesize_d* field
+plotted on the `y` axis and the `x` sequence
+plotted on the `x` axis. The effect of this is to spread the
+*filesize_d* samples across the length
+of the plot so they can be more easily studied.
+
+By studying the scatter plot we can learn a number of things about the
+distribution of the *filesize_d* variable:
+
+* The sample set ranges from 34,875 to 45,902.
 * The highest density appears to be at about 40,000.
 * The sample seems to have a balanced number of observations above and below
 40,000. Based on this the *mean* and *mode* would appear to be around 40,000.
@@ -113,7 +123,11 @@ image::images/math-expressions/univariate.png[]
 
 === Bivariate Scatter Plots
 
-In the next example two fields are returned with each sample: *filesize_d* and *response_d*.
+In the next example parameters have been added to the `random` function. The field list (*fl*)
+now specifies two fields to be
+returned with each sample: *filesize_d* and *response_d*. The `q` and `rows` parameters are the same
+as the defaults but are included as an example of how to set these parameters.
+
 By plotting *filesize_d* on the *x* axis and *response_d* on the y axis we can begin to study
 the relationship between the two variables.
 
@@ -125,7 +139,7 @@ be used to model the relationship.
 * The points appear to cluster more densely along a straight line through the middle
 and become less dense as they move away from the line.
 * The variance of the data at each *filesize_d* point seems fairly consistent. This means
-a predictive model would have consistent error across the range of predictor values.
+a predictive model would have consistent error across the range of predictions.
 
 image::images/math-expressions/bivariate.png[]