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kvv77 [185]
3 years ago
8

The correlation coefficient between two quantitative variables is approximately 0.6. What does the value of this correlation coe

fficient indicate about how well the model fits the data?
Mathematics
1 answer:
murzikaleks [220]3 years ago
8 0

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, R^2

  • 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.

R^2 = \text{(Correlation coefficient)}^2= (0.6)^2 = 0.36 = 36\%

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.

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