An economist conducted a study of the possible association between weekly income and weekly grocery expenditures. The particular interest was whether higher income would result in shoppers spending more on groceries. A random sample of shoppers at a local supermarket was obtained, and a questionnaire was administered asking about the weekly income of the shopper’s family and the grocery bill for that week. The gender of the shopper was also obtained. The correlation was found to be r = 0.649 . The amount of variation in the response explained by the regression line is: 0.421 %. 64.9 %. 42.1 %. 0.649 %.

42.1% of variation in the response is explained by the regression line

Step-by-step explanation:

Correlation coefficient is a measure which tells us that how strongly are two variables under study are linearly related to each other i.e correlation coefficient gives the strength of linear association between the variables.

If the magnitude of correlation coefficient is closer to 1, it indicates a strong linear relationship. If the magnitude of correlation coefficient is closer to 0, it indicates a weak linear relationship.

There is another variable known as "Coefficient of Determination", which is equal to square of Correlation Coefficient. Coefficient of Determination tells us that what percentage of variation in the response of the study can be explained by the regression line.

This means, for this question we need to calculate the Coefficient of Determination.

Correlation coefficient = r = 0.649

Coefficient of Determination = R = r² = (0.649)²= 0.421 = 42.1 %

This means that 42.1% of variation in the response is explained by the regression line.

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