Correlation analysis (Pearson, Spearman) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does Pearson correlation measure?

Pearson correlation measures the strength and direction of a straight-line (linear) relationship between two continuous variables.

beginner

What is Spearman correlation used for?

Spearman correlation measures the strength and direction of a monotonic relationship between two variables using their ranks, not the raw data values.

intermediate

When should you use Spearman correlation instead of Pearson?

Use Spearman when data is not normally distributed, has outliers, or the relationship is not linear but still monotonic (always increasing or decreasing).

beginner

What is the range of correlation coefficients for both Pearson and Spearman?

Both Pearson and Spearman correlation coefficients range from -1 to 1, where -1 means perfect negative correlation, 0 means no correlation, and 1 means perfect positive correlation.

beginner

How do you interpret a correlation coefficient of 0.8?

A correlation of 0.8 means a strong positive relationship: as one variable increases, the other tends to increase as well.

Which correlation method is best for measuring linear relationships?

A Spearman

B Kendall

C Pearson

D Chi-square

Pearson correlation measures linear relationships between continuous variables.

Spearman correlation uses which of the following to calculate correlation?

A Ranks of data

B Categorical labels

C Raw data values

D Mean values

Spearman correlation uses ranks of the data to measure monotonic relationships.

What does a correlation coefficient of 0 indicate?

A Perfect negative correlation

B No correlation

C Perfect positive correlation

D Monotonic relationship

A coefficient of 0 means no linear or monotonic relationship between variables.

Which correlation is more robust to outliers?

A Neither

B Pearson

C Both are equally sensitive

D Spearman

Spearman correlation is less affected by outliers because it uses ranks.

If data is not normally distributed, which correlation should you prefer?

A Spearman

B None

C Both are fine

D Pearson

Spearman correlation does not assume normal distribution and is better for non-normal data.

Explain the difference between Pearson and Spearman correlation in simple terms.

Think about how each method treats the data and what kind of relationships they detect.

Describe a real-life example where Spearman correlation is more appropriate than Pearson.

Consider situations where data is not perfectly straight but still ordered.