Understanding Correlation Analysis

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 °Definition:


What is Correlation Analysis?

•Correlation analysis is a statistical method that measures the relationship between two variables.

•It helps determine whether and how strongly two factors are related.



°Types of Correlation:

1. Positive Correlation → Both variables increase together.

Example: Study time vs. exam scores


2. Negative Correlation → One variable increases while the other decreases.

Example: Exercise vs. weight


3. No Correlation → No relationship between variables.

Example: Shoe size vs. intelligence



°Correlation Coefficient (r-value) – Problem & Solution:


°Problem:

A teacher wants to analyze the relationship between study hours and exam scores of five students.

•Solution:

The correlation coefficient (r) is calculated using:


•Table of Calculations:


•Final Calculation:


•Conclusion:


Since r ≈ 0.997, this means a very strong positive correlation between study hours and exam scores.


More study hours generally lead to higher exam scores.



°Real-Life Applications:


•Business: Sales vs. advertising expenses


•Finance: Stock prices vs. interest rates


•Health: Sleep hours vs. productivity


•Education: Attendance vs. academic performance


°Key Takeaways:


✔ Correlation does NOT imply causation (just because two things are related doesn’t mean one causes the other).

✔ The r-value helps determine the strength and direction of the relationship.

✔ Correlation is useful for predicting trends, but further analysis is needed for conclusions.


MEMBERS:

KENETH L. RABANG

FEI L. MAMARADLO

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