Match the LIST-I with LIST-II
| LIST-I Total sum of squares (TSS) Error sum of squares (ESS) | LIST-II (Coefficient of determination (R2) |
|---|---|
| A. ESS = 30 TSS = 80 | I. ~ 0.63 |
| B. ESS = 40 TSS = 90 | II. ~ 0.66 |
| C. ESS = 25 TSS = 75 | III. ~ 0.625 |
| D. ESS = 35 TSS = 95 | IV. ~ 0.55 |
Choose the correct answer from the options given below:
- AOption (a)
A-IV, B-III, C-II, D-I
- BOption (b)
A-III, B-IV, C-II, D-I
- COption (c)
A-IV, B-I, C-III, D-II
- DOption (d)
A-III, B-II, C-IV, D-I
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Correct answer
A-III, B-IV, C-II, D-I
Why this answer is correct
The correct answer is: A-III, B-IV, C-II, D-I.
Key Points
- Total Sum of Squares (TSS):
- Represents the total variation in the dependent variable.
- It measures how much the observed values deviate from their mean.
- Error Sum of Squares (ESS):
- Represents the unexplained variation in the model.
- It measures the deviation of observed values from the predicted values.
- Formula of Coefficient of Determination:
- R² = 1 − (ESS / TSS)
- Range of R²:
- 0 ≤ R² ≤ 1
- Higher value indicates better explanatory power of the regression model.
| Case | ESS | TSS | R² = 1 − (ESS/TSS) | Approx. Value | Match |
|---|---|---|---|---|---|
| A | 30 | 80 | 1 − (30/80) = 1 − 0.375 | 0.625 | III |
| B | 40 | 90 | 1 − (40/90) = 1 − 0.444 | ≈ 0.55 | IV |
| C | 25 | 75 | 1 − (25/75) = 1 − 0.333 | ≈ 0.66 | II |
| D | 35 | 95 | 1 − (35/95) = 1 − 0.368 | ≈ 0.63 | I |
Additional Information
- Interpretation of R²:
- R² = 0.625 means 62.5% of total variation is explained by the model.
- R² = 0.55 means 55% variation is explained.
- R² = 0.66 means 66% variation is explained.
- R² = 0.63 means 63% variation is explained.
- Conceptual Understanding:
- If ESS is small relative to TSS, R² will be high.
- If ESS equals TSS, then R² = 0 (no explanatory power).
- If ESS = 0, then R² = 1 (perfect fit).
- Importance in Regression:
- It is a measure of goodness of fit.
- Used to compare different regression models.
- In multiple regression, Adjusted R² is preferred to account for additional predictors.