Module 2 challenge :Regression Analysis: Simplify Complex Data Relationships (Google Advanced Data Analytics Professional Certificate) Answers 2025
1. What is the calculation of observed – predicted?
✔ Residual
❌ Coefficient
❌ Notion
❌ Parameter
2. Minimizing the sum of squared residuals is which method?
✔ Ordinary least squares
❌ Residual coefficients
❌ R squared
❌ Mean absolute error
3. Waveform pattern violates which regression assumption?
✔ Linearity
❌ Independent observation
❌ Normality
❌ Homoscedasticity
4. Scatterplot matrix shows _____ between variables.
✔ relationships
❌ distances
❌ variability
❌ discrepancies
5. U-shaped residual pattern violates which assumption?
✔ Linearity
❌ Independent observation
❌ Homoscedasticity
❌ Normality
6. Confidence band describes the _____ around predicted outcome.
✔ uncertainty
❌ inaccuracy
❌ certainty
❌ accuracy
7. Which metric tells how much variation in X explains variation in Y?
✔ R squared
❌ P-value
❌ MSE
❌ MAE
8. Which describe randomized controlled experiments?
✔ It is typically used when arguing for causation between variables.
✔ It is a study design that randomly assigns participants into groups.
❌ It cannot have a control group.
❌ Must control for every factor.
9. Correlation r = 0.75 — which statements are true?
✔ Scatterplot slopes upward (r > 0).
✔ Regression line is steeper than r = 0.5.
❌ Slopes downward (wrong: that’s r < 0).
✔ Variables are positively correlated.
10. X and Y correlation r = 1 (perfect positive). Points always on line:
Regression line is:
Y = 2 + 1·X → (X, X+2)
✔ (0, 2) → Yes
❌ (1, 1) → Should be 3
✔ (1, 3) → Correct
❌ (0, 0) → Should be 2
🧾 Summary Table of All Answers
| Q No. | Correct Answer(s) | Incorrect Options |
|---|---|---|
| 1 | Residual | Coefficient, Notion, Parameter |
| 2 | Ordinary least squares | Residual coefficients, R squared, MAE |
| 3 | Linearity | Independent observation, Normality, Homoscedasticity |
| 4 | Relationships | Distances, Variability, Discrepancies |
| 5 | Linearity | Independent observation, Homoscedasticity, Normality |
| 6 | Uncertainty | Inaccuracy, Certainty, Accuracy |
| 7 | R squared | P-value, MSE, MAE |
| 8 | Used for causation; Random assignment | No control group; Must control everything |
| 9 | Slopes upward; Steeper than r=0.5; Positively correlated | Slopes downward |
| 10 | (0,2), (1,3) | (1,1), (0,0) |