Configure Tableau Data Story Settings: Analytics
Within your Tableau Data Story, you can choose which analytics to write about and when those analytics are written about. Different types of analytics are available depending on your story type and how many dimensions and measures your story has. However, analytics aren't currently supported for scatter plot story types. For more information, see Choose the Right Story Type for Your Tableau Data Story.
Configure analytics for your story
 Add a Tableau Data Story to a Dashboard.
 From your dashboard, click the Settings icon at the topleft corner of your Data Story object.
 In the Data Story dialog box, click the Analytics tab.
 Click the switches to turn on different types of analytics.
 For Segments and Trend line, expand Settings to set thresholds for performing those analytics.
 Click Save.
Understand different types of analytics
Correlation
Use Correlation to identify true statistical correlations between two series. If you have more than two series, then all combinations are analyzed for correlations. For example, you might turn on Correlation to identify when two products are often purchased together.
Clustering
Use Clustering to identify distinct groups of data points (clusters) using a single statistical analysis. For example, you might turn on Clustering to identify when a product is very popular in a specific geographic region.
Distribution
Use Distribution to rank data points relative to each other using nonstatistical observations, such as mean, median, skew, etc. For example, you might turn on Distribution to identify which product has the highest profit ratio.
Segments
Use Segments to highlight noteworthy changes to data points within a series. First, set the minimum percentage of change that you want to be written about in a segment. Changes that fall below your defined threshold aren't written about. For example, if you set your segment threshold for changes that are greater than 60%, then your story doesn't write about a trough in a time series that features a 30% decrease.
After you’ve set your threshold, choose whether to Apply formatting, and set the minimum percentage of change that you want formatted.
Trend line
Use Trend line to calculate a linear best fit line and identify data that falls within a defined percentage of confidence. Data that has high variability has a lower confidence level than data that's more consistent, and that confidence level affects whether trend lines are written about. You can use trend lines for stories that have one dimension and one measure, or you can use trend lines in a drilldown. For more information about drilldowns, see Configure Tableau Data Story Settings: Narrative.
Set the minimum percentage of confidence for your trend line. If you set your threshold at 95%, but a trend line could be drawn at 90% confidence, then your story doesn't write about trend lines. After you’ve set your threshold, choose whether to Apply formatting. Then set the minimum percentage of change that you want formatted.
Tableau Data Stories about trend lines communicate the absolute change over a period. The story written about your trend line varies depending on the level of verbosity you set for your story. If your story uses high verbosity, then your story writes about the Rsquared value, which is a statistical concept that quantifies how well your data fits the trend line. For more information about verbosity settings, see Configure Tableau Data Story Settings: Narrative.
Within the Trend line settings, you can also choose how many periods into the future for which you want your story to write predictions. When you use predictions, your story uses the slope and intercept of the trend line to calculate predicted values for future periods. The confidence of the prediction adds upper and lower bounds to the confidence threshold you set for trend lines. You can use predictions when your story has at least 30 data points that are linear.
Volatility
Use Volatility to analyze standard deviations over time. For example, use Volatility when you want your story to write about values that fall outside the average range for your data.
Break down how analytics are used to generate stories
At this point, you might be wondering how the analytics for different story types work. Let’s take a look at an example for each story type and break down each sentence in the story.
Understand analytics for discrete stories
Because continuous stories measure trends over time, Data Stories writes about performance, progression, averages, totals, streaks, volatility, segments, and predictions.
The following example of a continuous story is about sales per month:
Example story  Story breakdown 

The first two sentences use average and range functions to write about the average, maximum, and minimum values across the period you’re analyzing. 

The third sentence is about overall performance of the measure over the period. For example, a sentence can be about whether sales increased, decreased, or trended differently during a specific period. 

The fourth sentence uses progression analysis. This sentence writes about the largest increase and decrease based on the measure during the period using both a percentage basis and absolute basis. 

This sentence is a Correlation insight. This type of analytic insight writes about notable correlations between different series in your data. 

This sentence is a Segment insight. This type of analytic insight writes about noteworthy increases and decreases over time. 

This sentence is a Trend line insight. This type of insight writes about how well trends fit your data with a certain percentage of confidence, and trend lines allow you to make predictions based on historic trends. 
Understand analytics for discrete stories
Because discrete stories allow you to compare values and understand the distribution of the data, the story writes about distribution, averages, totals, and groupings or clusters across the data.
The following example of a discrete story is about sales by product:
Example story  Story breakdown 

The first sentence calculates the total value of your measure. 

The second sentence writes about the dimension drivers. In this example, the dimension drivers are the products that contributed the most to total sales. 

The third and fourth sentences analyze the distribution of the data. This analyzes the averages, medians, concentration of data (if any exist), and how the data is skewed. This helps identify how balanced these grouped variables are compared to one another. 

This sentence uses Clustering to write about measures that can be grouped. This helps identify whether there are distinct groups that stand out in the data. 

The final sentence writes about notable outliers. 
Understand analytics for scatter plot stories
Scatter plot story types are best used to understand the relationship between two measures, and for that reason, scatter plot stories require 2–3 measures. The scatter plot analysis writes about the relationship (regression) between two measures, and it writes about groups (clusters) within the data, if they exist.
The following example of a scatter plot story is about profit and sales across a dimension:
Example story  Story breakdown 

The first two sentences are powered by regression analytics. Regression shows how one measure affects another. Notice that in the first sentence, the story has identified a relationship between profit and sales. 

The third sentence is derived from clustering. Clustering analytics tries to identify key groups or clusters across all the variables in the data. 

The fourth sentence is written about outliers–values that fall significantly above or below the average. 

The remaining sentences for scatter plot stories use range and average analysis to write insights. 
Understand analytics for percent of whole stories
Percent of whole story types are best for understanding what part of a whole a dimension or measure represents.
The following example of a percent of whole story is about sales by segment:
Example story  Story breakdown 

The first sentence calculates the total value of your measure. 

The second sentence writes about drivers. In this example, the drivers are segments that contributed the most to total sales. 

The final sentence analyzes the distribution of the data. 