diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index edb9ed7bba56a..6d397e2601797 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -35,6 +35,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the second column
    * @return the covariance of the two columns.
    *
+   * {{{
+   *    import org.apache.spark.sql.functions._
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.cov("rand1", "rand2")
+   * }}}
+   *
    * @since 1.4.0
    */
   def cov(col1: String, col2: String): Double = {
@@ -50,6 +57,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    import org.apache.spark.sql.functions._
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2", "pearson")
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String, method: String): Double = {
@@ -65,6 +79,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    import org.apache.spark.sql.functions._
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2")
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String): Double = {
@@ -85,6 +106,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *             of the DataFrame.
    * @return A DataFrame containing for the contingency table.
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+   *      (3, 3))).toDF("key", "value")
+   *    val ct = df.stat.crosstab("key", "value")
+   *    ct.show()
+   * }}}
+   *
    * @since 1.4.0
    */
   def crosstab(col1: String, col2: String): DataFrame = {
@@ -105,6 +133,18 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *                than 1e-4.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+   *    freqPairs.show()
+   * }}}
+   *
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Array[String], support: Double): DataFrame = {
@@ -140,6 +180,21 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param cols the names of the columns to search frequent items in.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+   *    freqPairs.show()
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Seq[String], support: Double): DataFrame = {