Typo in Update 07_Aggregation.md

Rx.NET/Documentation/IntroToRx/07_Aggregation.md

@@ -1,6 +1,6 @@
 # Aggregation
 
-Data is not always tractable is its raw form. Sometimes we need to consolidate, collate, combine or condense the mountains of data we receive. This might just be a case of reducing the volume of data to a manageable level. For example, consider fast moving data from domains like instrumentation, finance, signal processing and operational intelligence. This kind of data can change at a rate of over ten values per second for individual sources, and much higher rates if we're observing multiple sources. Can a person actually consume this? For human consumption, aggregate values like averages, minimums and maximums can be of more use.
+Data is not always tractable in its raw form. Sometimes we need to consolidate, collate, combine or condense the mountains of data we receive. This might just be a case of reducing the volume of data to a manageable level. For example, consider fast moving data from domains like instrumentation, finance, signal processing and operational intelligence. This kind of data can change at a rate of over ten values per second for individual sources, and much higher rates if we're observing multiple sources. Can a person actually consume this? For human consumption, aggregate values like averages, minimums and maximums can be of more use.
 
 We can often achieve more than this. The way in which we combine and correlate may enable us to reveal patterns, providing insights that would not be available from any individual message, or from simple reduction to a single statistical measure. Rx's composability enables us to express complex and subtle computations over streams of data enabling us not just to reduce the volume of messages that users have to deal with, but to increase the amount of value in each message a human receives.
 
@@ -472,4 +472,4 @@ source.Aggregate(0, (acc, current) => acc + current);
 source.Scan(0, (acc, current) => acc + current).TakeLast();
 ```
 
-Aggregation is useful for reducing volumes of data or combining multiple elements to produce averages, or other measures that incorporate information from multiple elements. But to perform some kinds of analysis we will also need to slice up or otherwise restructure our data before calculating aggregated values. So in the next chapter we'll look at the various mechanisms Rx offers for partitioning data.
+Aggregation is useful for reducing volumes of data or combining multiple elements to produce averages, or other measures that incorporate information from multiple elements. But to perform some kinds of analysis we will also need to slice up or otherwise restructure our data before calculating aggregated values. So in the next chapter we'll look at the various mechanisms Rx offers for partitioning data.