Calculate The Quartiles of The Co2_emission Column of Food_consumption

What are quartiles?

Quartiles divide a dataset into four equal parts, each containing 25% of the data. The three quartiles are:

• First quartile (Q1): The value below which 25% of the data falls
• Second quartile (Q2): The median value (50th percentile)
• Third quartile (Q3): The value below which 75% of the data falls

Quartiles help identify the spread of data, detect outliers, and understand the distribution of CO2 emissions in food products.

How to calculate quartiles

To calculate quartiles for CO2 emissions:

1. Sort the CO2 emission values in ascending order
2. Calculate the position of each quartile using the formula:
   Position = (n + 1) × p / 100
   Where n = number of data points, p = percentile (25 for Q1, 50 for Q2, 75 for Q3)
3. If the position is an integer, use that data point as the quartile
4. If the position is not an integer, interpolate between the two nearest data points

This method provides a robust measure of the distribution of CO2 emissions in your food consumption dataset.

Example calculation

Consider the following CO2 emissions (in kg CO2 per kg of food) for different food products:

• Beef: 27.0
• Lamb: 39.0
• Cheese: 13.5
• Pork: 12.1
• Turkey: 10.9
• Tuna: 6.1
• Chicken: 6.9
• Tofu: 2.0
• Beans: 2.0
• Lentils: 1.8

Sorted values: 1.8, 2.0, 2.0, 6.1, 6.9, 10.9, 12.1, 13.5, 27.0, 39.0

Calculating Q1 (25th percentile):

Calculating Q2 (median):

Calculating Q3 (75th percentile):

Interpretation of results

The quartiles provide several insights about your food consumption data:

• Q1 (5.075 kg CO2): 25% of foods have lower CO2 emissions than this value
• Q2 (6.5 kg CO2): The median CO2 emission, with half of foods below and half above
• Q3 (16.875 kg CO2): 75% of foods have lower CO2 emissions than this value

This distribution shows that most foods in this dataset have relatively low CO2 emissions, with a few high-emission outliers (beef and lamb).