Lære Challenge: Correlation of Weather Variables | Statistical Analysis in Environmental Science

In environmental science, understanding how different weather variables interact is crucial for interpreting patterns and making predictions. You will explore the relationship between daily temperature and humidity by calculating their correlation coefficient and visualizing the results with a scatter plot. This analysis helps you determine if changes in one variable are associated with changes in the other, which is essential for both research and practical applications such as weather forecasting.


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import pandas as pd
import matplotlib.pyplot as plt

# Sample environmental data: daily temperature (°C) and humidity (%)
data = {
    "temperature": [22, 24, 19, 23, 25, 27, 21, 26, 28, 20, 22, 24, 23, 25],
    "humidity": [55, 50, 60, 53, 48, 45, 58, 47, 44, 62, 56, 51, 54, 49]
}
df = pd.DataFrame(data)

# Calculate the correlation coefficient
correlation = df["temperature"].corr(df["humidity"])
print("Correlation coefficient between temperature and humidity:", round(correlation, 2))

# Create a scatter plot
plt.scatter(df["temperature"], df["humidity"], color="green")
plt.title("Temperature vs. Humidity")
plt.xlabel("Temperature (°C)")
plt.ylabel("Humidity (%)")
plt.grid(True)
plt.show()

After running this code, you will see the calculated correlation coefficient and a scatter plot that shows the relationship between temperature and humidity. The correlation coefficient ranges from -1 to 1:

A value close to 1 means a strong positive correlation;
A value close to -1 means a strong negative correlation;
A value near 0 means no correlation.

By examining both the numerical value and the scatter plot pattern, you can interpret whether there is a strong, weak, or no correlation between these two weather variables. This process is a foundational skill for environmental data analysis.

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Calculate the correlation coefficient between temperature and humidity in the provided DataFrame, then interpret the strength and direction (strong, weak, or no correlation; positive or negative) of the relationship. Follow these steps:

Use the given DataFrame to compute the correlation coefficient between the "temperature" and "humidity" columns.
Print the correlation coefficient rounded to two decimal places.
Based on the coefficient, print a message stating whether the correlation is strong, weak, or none, and whether it is positive or negative.

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              123456789101112131415161718192021
            
import pandas as pd
import matplotlib.pyplot as plt

# Sample environmental data: daily temperature (°C) and humidity (%)
data = {
    "temperature": [22, 24, 19, 23, 25, 27, 21, 26, 28, 20, 22, 24, 23, 25],
    "humidity": [55, 50, 60, 53, 48, 45, 58, 47, 44, 62, 56, 51, 54, 49]
}
df = pd.DataFrame(data)

# Calculate the correlation coefficient
correlation = df["temperature"].corr(df["humidity"])
print("Correlation coefficient between temperature and humidity:", round(correlation, 2))

# Create a scatter plot
plt.scatter(df["temperature"], df["humidity"], color="green")
plt.title("Temperature vs. Humidity")
plt.xlabel("Temperature (°C)")
plt.ylabel("Humidity (%)")
plt.grid(True)
plt.show()

A value close to 1 means a strong positive correlation;
A value close to -1 means a strong negative correlation;
A value near 0 means no correlation.