pandas as pd import numpy as np from scipy.stats import pearsonr

Data input

data = { "Store_Size": [3.7, 2.0, 5.0, 0.7, 2.6, 2.6, 2.9, 5.2, 5.2, 5.9, 3.0, 5.5, 2.4, 2.9, 2.4, 2.2, 0.5, 4.2, 3.3, 3.2, 0.4, 5.0, 2.3], "Sales": [9.18, 4.58, 8.22, 1.45, 4.49, 6.51, 2.82, 9.90, 10.45, 9.94, 4.43, 5.47, 4.75, 8.22, 7.30, 7.17, 3.33, 7.56, 8.93, 7.60, 0.55, 6.76, 4.35] }

Create a dataframe

df = pd.DataFrame(data)

Calculate correlation coefficient

correlation_coefficient, _ = pearsonr(df["Store_Size"], df["Sales"])

Calculate coefficient of determination (R^2)

coefficient_of_determination = correlation_coefficient ** 2

correlation_coefficient, coefficient_of_determinationThe correlation coefficient is $r = 0.755$ (rounded to three decimal places), indicating a strong positive linear relationship between store size and sales.

The coefficient of determination is $R^2 = 0.569$ (rounded to three decimal places), meaning approximately 56.9% of the variation in sales can be explained by the store size.

Would you like further analysis or have any specific questions?

Here are five related questions for exploration:

1. Would you like to see a scatter plot of the data with a trendline?
2. Should we perform a linear regression to model the relationship?
3. Do you want to calculate confidence intervals for these coefficients?
4. Would you like to analyze outliers in this dataset?
5. Should we test the statistical significance of the correlation?

Tip: Always consider visualizing your data to understand relationships better before relying solely on numerical measures.