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x_avg = sum(physics_marks)/10
y_avg = sum(history_score)/10
corr_coeff_num = sum([(physics_marks[i] - x_avg)*(history_score[i]-y_avg) for i in range(10)])
corr_coeff_deno = (sum([(physics_marks[i]**2)for i in range(10)])*sum([(history_score[i]**2)for i in range(10)]))**0.5
correlation_coefficient = corr_coeff_num/corr_coeff_deno
print(f"{correlation_coefficient:.3f}")
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Correlation and Regression Lines - A Quick Recap #1
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physics_marks = [15,12,8,8,7,7,7,6,5,3] history_score = [10,25,17,11,13,17,20,13,9,15]
calculate correlation coefficient
correlation_coefficient = np.corrcoef(physics_marks,history_score)[0,1]
x_avg = sum(physics_marks)/10 y_avg = sum(history_score)/10 corr_coeff_num = sum([(physics_marks[i] - x_avg)*(history_score[i]-y_avg) for i in range(10)]) corr_coeff_deno = (sum([(physics_marks[i]**2)for i in range(10)])*sum([(history_score[i]**2)for i in range(10)]))**0.5 correlation_coefficient = corr_coeff_num/corr_coeff_deno print(f"{correlation_coefficient:.3f}")