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Using the <code>employees</code> table, get the total salary of all employees.

Example:

Input:

<code>employees</code> table

<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>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>manager_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

Total Salary

Let’s say that you are working on analyzing salary data. You are tasked by your manager with computing the average salary of a data scientist using a recency weighted average, and you are provided data from the past ‘n’ years.

The recency weighting should ensure that the salaries from more recent years are weighted more heavily than the less relevant salaries of years that are farther in the past.

Write the function to compute the average data scientist salary given a mapped linear recency weighting on the data. In this case the input <code>previous_salaries</code> is a list of the salaries of the last <code>n</code> years and the list is ordered chronologically, with the most recent year coming last.

Round the result to two-digit numbers.

Example:

<pre tabindex="0" class="chroma"><code>#input
previous_salaries = [64000,66000,75000,88000,90000]

#output
recency_weighted_salaries(previous_salaries) -&gt; 81533.33
</code></pre>

Explanation: We have the <code>5</code> salaries from the <code>5</code> years.
We should give weight <code>1</code> to the first-year salary, <code>2</code> to the second one and so on till we reach the end and give weight <code>5</code> to the most recent year salary at the end.

\(result = \frac{64000 * 1 + 66000 * 2 + 75000 *3 + 88000 *4 +90000 *5}
 {1+2+3+4+5} = 81533.33 \)

Write the function to compute the average data scientist salary given a mapped linear recency weighting on the data.

Recency Weighted Salaries

Python & General Programming

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.

What factors could have biased this result, and what would you look into?

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

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
Total Salary	SQL	Easy	Very High
Recency Weighted Salaries	Python & General Programming	Hard	Low
Boarding Times Bias	Statistics	Medium	Very Low

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