# Value

## Scenario 1

### Which destination has a person visited the most?

1. Click on the **Add Aggregation** button.
2. Click on the tab entitled **FrequencyBookings** and change the **Type** box to show **Value**.
3. Set the **Grouping Table** to **People** and the **Transactional Table** to **Bookings**.
4. Drag the **Destination** variable on as the **value variable** and leave the default **Mode** as the **Function**.

A data grid can be used to validate the results:

Using a selector as the value variable results in a subset of relevant functions being presented. As well as the mode function, you can select from:

- **Count Distinct** - used to establish e.g. how many distinct destinations a person visited
- **Maximum Distinct Count** - used to establish e.g. how many times a person has been to their favourite destination

When using a numeric or currency value variable, a more extensive list of functions can be accessed.

## Scenario 2

### What is the difference between each person's maximum and minimum cost of a booking?

1. Click on the **Add Aggregation** button.
2. Click on the tab entitled **FrequencyBookings** and change the **Type** box to show **Value**.
3. Drag the **Cost** variable onto the **Function** drop box and change the function to display **Maximum**.
4. Click on the **Expression** tab and enter a **minus** sign after the aggregation title.
5. Click on the **Add Aggregation** button to generate a new aggregation tab.
6. Click on the tab entitled **FrequencyBookings** and change the **Type** box to show **Value**.
7. Drag the **Cost** variable onto the **Function** drop box and change the function to display **Minimum**.
8. Click the **Build** button to see a preview of the results.

A data grid can be used to validate the results:

## Scenario 3

### Use the linear trend value function to examine the sequence of transactions of an individual and identify those whose booking cost values are increasing the most over time

1. Create a new **Value** on the fly aggregation and select **Linear Trend (slope)** as the function.
2. Drag and drop **Cost** as the value variable and order the records by **Booking Date**.

   > **Tip**
   >
   > You can optionally filter the transactions to, for example, bookings made only in the last year, or to a particular destination. In this example, we will return all bookings.
3. Name the expression - **LinearTrendCost**.

A data grid can be used to validate the results:

Person URNs 262526 and 262527 both demonstrate a positive linear trend booking cost over time of £1.61 and £0.50 per day respectively, whilst Person URN 273961 displays a significant decrease of £65.87.

Grouping the display by LinearTrendCost allows you to more easily see those with the smallest or greatest values:

> [!NOTE]
>
> The values can be validated using online best fit calculators. Dates need to be converted into numbers for this purpose.