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<p>Let’s say that you work as a data scientist at a food delivery company.</p>

<p>You are tasked with building a model to predict food preparation times at a restaurant from the time when a customer sends in the order to when the meal is ready for the driver to pick up.</p>

<p>What are some reasons why measuring bias would be important in building this particular model?</p>


Food Delivery Times

<p>An electronics retail company tracks its sales and wants to run a performance analysis. They’re hoping to categorize sales into different groups, based on the amount of sales and the region where the sale occurred. The conditions for categorization are:</p>

<ul>
<li><strong>Standard Sales</strong>: Any sales below 2000 are considered standard, except if they occur in the <code>East</code> region, or in the month of July.</li>
<li><strong>Premium Sales</strong>: Any sales that are over or equal to 2000, or any sales in the <code>East</code> region (regardless of the amount), are considered premium, except again during the month of July.</li>
<li><strong>Promotional Sales</strong>: All sales made in the promotional period of July, regardless of the amount or region, are considered promotional.</li>
</ul>

<p>The company wants a report that sums these amounts by region to help with financial planning and bonus allocations.</p>

<p><em>Note: You may assume that table contains data only for 2023</em></p>

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

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

<p><code>sales</code> table</p>

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>sale_amount</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>sale_date</code></td>
<td>DATE</td>
</tr>

<tr>
<td><code>region</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>region</code></td>
<td>TEXT</td>
</tr>

<tr>
<td><code>total_sales</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>premium_sales</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>standard_sales</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>promotional_sales</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Categorize sales based on the amount of sales and the region

Categorize Sales

<p>Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.</p>

<p>What factors could have biased this result, and what would you look into?</p>


<p>What could potentially bias the average boarding time in favor of an airline that only flies domestically?</p>

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Question	Topic	Difficulty	Ask Chance
Food Delivery Times	Machine Learning	Medium	Very High
Categorize Sales	SQL	Medium	High
Boarding Times Bias	Statistics	Medium	High

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