Answer :
Answer:
The model does not fits the data well.
Step-by-step explanation:
Correlation:
- Correlation is a technique that help us to find or define a relationship between two variables.
- It is a measure of linear relationship between two quantities.
- A positive correlation means that an increase in one quantity leads to an increase in another quantity
- A negative correlation means with increase in one quantity the other quantity decreases.
R-square, [tex]R^2[/tex]
- The quantity R-squared is an indicator of the predictive power of a model.
- It explains the variation in the dependent variable due to independent variable.
- It shows how well the model fits the data.
- R-squared is also known as the coefficient of determination.
[tex]R^2 = \text{(Correlation coefficient)}^2= (0.6)^2 = 0.36 = 36\%[/tex]
Therefore, only 36% of the variations in the dependent variable is explained by the independent variable in the model which means more than 50% of variation cannot still be explained in the dependent variable.
Hence, the model does not fits the data well.