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Machine Learning

Product Metrics

A/B Testing

Analytics

Statistics

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<p>We’re given three tables representing a forum of users and their comments on posts.</p>

<p>Write a query to get the top three users that got the most upvotes on their comments written in 2020. </p>

<p><em>Note: Do not count deleted comments and upvotes by users on their own comments</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>users</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>username</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>comments</code> table</p>

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>datetime</td>
</tr>

<tr>
<td><code>post_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>is_deleted</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><code>comment_votes</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>comment_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>is_upvote</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>username</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>upvotes</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Comment Upvotes

<p>You are testing hundreds of hypotheses with many <em>t</em>-tests. What considerations should be made?</p>


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

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?

<p>Find the three best-performing days ever recorded for each advertiser who achieved the highest weekly revenue in 2021.</p>

<p><em>Note:</em></p>

<p><em>You may assume that all the transactions happened in 2021.</em></p>

<p><em>You may assume that every record within the amount column is different.</em></p>

<p><code>advertisers</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>advertiser_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>advertiser_name</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>
<p><code>revenue</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>payment_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>advertiser_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>transaction_date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>amount</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>advertiser_name</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>transaction_date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>amount</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Find all advertisers who reported revenue over $40

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