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Subject [lucene-solr] branch SOLR-13105-visual updated: SOLR-13105: Update search/sample/agg viz2
Date Fri, 23 Aug 2019 14:23:27 GMT
This is an automated email from the ASF dual-hosted git repository.

jbernste pushed a commit to branch SOLR-13105-visual
in repository

The following commit(s) were added to refs/heads/SOLR-13105-visual by this push:
     new d217333  SOLR-13105: Update search/sample/agg viz2
d217333 is described below

commit d217333cca92cfa6d9a922d3d8d7ac57e416f070
Author: Joel Bernstein <>
AuthorDate: Fri Aug 23 10:23:03 2019 -0400

    SOLR-13105: Update search/sample/agg viz2
 .../src/images/math-expressions/bivariate.png      | Bin 0 -> 227303 bytes
 .../src/images/math-expressions/univariate.png     | Bin 0 -> 169949 bytes
 solr/solr-ref-guide/src/search-sample.adoc         |  55 +++++++++++++++++++--
 3 files changed, 52 insertions(+), 3 deletions(-)

diff --git a/solr/solr-ref-guide/src/images/math-expressions/bivariate.png b/solr/solr-ref-guide/src/images/math-expressions/bivariate.png
new file mode 100644
index 0000000..364ad04
Binary files /dev/null and b/solr/solr-ref-guide/src/images/math-expressions/bivariate.png
diff --git a/solr/solr-ref-guide/src/images/math-expressions/univariate.png b/solr/solr-ref-guide/src/images/math-expressions/univariate.png
new file mode 100644
index 0000000..e2ea1c2
Binary files /dev/null and b/solr/solr-ref-guide/src/images/math-expressions/univariate.png
diff --git a/solr/solr-ref-guide/src/search-sample.adoc b/solr/solr-ref-guide/src/search-sample.adoc
index 0c211dd..1d99b8b 100644
--- a/solr/solr-ref-guide/src/search-sample.adoc
+++ b/solr/solr-ref-guide/src/search-sample.adoc
@@ -16,7 +16,10 @@
 // specific language governing permissions and limitations
 // under the License.
+Data is the indispensable factor in statistical analysis. This section
+provides an overview of the key functions for retrieving data for
+visualization and statistical analysis: searching, sampling
+and aggregation.
 == Searching
@@ -36,7 +39,7 @@ for exploring the fields in the data and understanding how to start refining
-==== Searching and Sorting
+=== Searching and Sorting
 Once the format of the records is known, parameters can be added to the *search* function
to begin analyzing
 the data.
@@ -65,16 +68,62 @@ a text field. The example below shows an example of this scoring and ranking
 == Sampling
+The `random` function returns a random sample from a distributed search result set.
+This allows for fast visualizations, statistical analysis and modeling of
+samples that can be applied to the larger result set.
+For the visualization examples below smaller random samples are used. But
+Solr's random sampling provides sub-second
+response times on sample sizes of over 200,000, which can be used to build
+reliable statistical models that describe large data sets (billions of
+documents) with sub-second performance.
+The examples below demonstrate univariate and bivariate scatter
+plots of random samples. Statistical modeling with random samples
+is covered in the Statistics, Probability, Linear Regression, Curve Fitting
+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.
+The visualization below shows the *filesize_d* field plotted on both the x and y
+axis which produces a diagnal line with a slop of 1. By studying the scatter plot
+we can learn a number of things about the distribution of the *filesize_d*
+* The sample set ranges from 34,070 to 46,456.
+* 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.
+* The number of observations tapers off to a small number of outliers on
+the and low end of the sample.
+This sample can be rerun multiple times to see if the samples
+produce similar plots.
 === Bivariate Scatter Plots
+In the next example two fields are returned with each sample: *filesize_d* and *response_d*.
+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.
+By studying the scatter plot we can learn the following:
+* As filesize_d rises response_d tends to rise.
+* This relationship appears to be linear, as a straight line put through the data could
+be used to model the relationship.
+* The points would cluster most densely
+* 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.
 == Aggregations

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