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You work at a freight shipping company with a large fleet of trucks. These trucks are based around the nation, with a mix of Mercedes and BMW models represented. The location of each truck that your company owns is stored as <code>(x,y)</code> coordinates in your database.

Given a list of locations that your trucks are stored at, return the top location <code>(x,y)</code> for each model of truck (Mercedes or BMW).

You can assume that there is one location with the most model of a truck, e.g. there will not be any locations that are tied for most BMWs stored.

Example:

Input:

<pre tabindex="0" class="chroma"><code>truck_locations = [{
 &#34;model&#34; : &#34;BMW&#34;,
 &#34;location&#34; : (1,2)
 },
 {
 &#34;model&#34; : &#34;Mercedes&#34;,
 &#34;location&#34; : (2,3)
 },
 {
 &#34;model&#34; : &#34;Mercedes&#34;,
 &#34;location&#34; : (2,2)
 },
 {
 &#34;model&#34; : &#34;Mercedes&#34;,
 &#34;location&#34; : (2,3)
 },
 {
 &#34;model&#34; : &#34;BMW&#34;,
 &#34;location&#34; = (1,2)
 },
 {
 &#34;model&#34; : &#34;BMW&#34;,
 &#34;location&#34; : (3,3)
 }
 ]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def truck_frequent_location(truck_locations) -&gt; { 
 &#34;Mercedes&#34; : (2,3), 
 &#34;BMW&#34; : (1,2) 
 }
}
</code></pre>

Explanation:

Mercedes repeated two times at location <code>(2,3)</code> and one time at the location <code>(2,2)</code>, so <code>(2,3)</code> is our most stored Mercedes location.

BMW repeated two times at location <code>(1,2)</code> and one time at the location <code>(3,3)</code>, so <code>(1,2)</code> is our most stored BMW location.

Given a list of locations that your trucks are stored at, return the top location for each model of truck (Mercedes or BMW).

Location Frequency

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

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