Correlation and regression are statistical methods used to examine relationships between variables. Correlation measures strength and direction of association. Regression predicts the value of one variable based on another.

1. CORRELATIO
N AND
REGRESSION

2. OVERVIEW
• Correlation and regression are statistical methods used
to examine relationships between variables.
• Correlation measures strength and direction of
association.
• Regression predicts the value of one variable based on
another.

3. • Indicates the degree to which two variables move together.
• Measured by the correlation coefficient, r, ranging from -1
to +1.
• Positive correlation: variables increase together.
• Negative correlation: one variable increases as the other
decreases.
CORRELATION EXPLAINED

4. • Perfect positive (+1), perfect negative (-1), and no correlation
(0).
• Linear vs. non-linear correlation.
• Pearson’s correlation for linear relationships.
TYPES OF CORRELATION
Example: Height and weight show strong positive correlation.

5. Formula for Pearson's r:
CALCULATING
CORRELATION
COEFFICIENT

6. • Strong (>0.7), moderate (0.3-0.7), weak (<0.3) correlation
strength.
• Sign indicates direction.
• Correlation does not imply causation.
INTERPRETING
CORRELATION VALUES
Example: r = 0.85 between marketing budget and revenue —
strong positive.

7. • Regression estimates the relationship between dependent
and independent variables.
• Simple linear regression equation:
REGRESSION EXPLAINED
b0: intercept; b1: slope.

8. • b1 represents change in y for one unit change in x.
• b0 is the predicted value when x=0.
UNDERSTANDING
REGRESSION
COEFFICIENTS
Example: Predict sales (y) based on advertising spend (x).

9. • Linear relationship between variables.
• Homoscedasticity: constant variance of errors.
• Normally distributed residuals.
ASSUMPTIONS IN
REGRESSION

10. • Sales forecasting based on advertising spend.
• Risk assessment and portfolio analysis.
• Customer behavior prediction.
APPLICATIONS IN
BUSINESS AND FINANCE

11. • Correlation measures strength and direction of
relationship.
• Regression models and predicts dependent
variables.
• Both are fundamental tools in data-driven
decision-making.
SUMMARY AND
CONCLUSION

12. Thank you.