Performing simple regression analysis with an individual's height (in cm) as the independent variable and their weight (in kg) as the dependent variable, you come up with the following values...

r = 0.74

r²= 0.54

y = 0.85x - 59.66

Which would be the correct interpretation of the regression equation?

54% of the variability in weightcan be explained by the linear relationship betweena person's height and their weight.

Each additional 1kg in a person's weight leads to increase of 0.85cm in their height.

Each additional 1cm in a person's height leads to increase of 0.85kg in their weight.

Each additional 1kg in a person's weight leads to decrease of 59.66cm in their height.

Each additional 1cm in a person's height leads to decrease of 0.85kg in their weight.

There is a strong, positive linear relationship between a person's height and their weight.

74% of the variability in weightcan be explained by the linear relationship betweena person's height and their weight.

There is a weak, positivelinear relationship between a person's height and their weight.

Performing simple regression analysis with the the age of a car (in years) as the independent variable andthe resale value of the car (in $)as the dependent variable, you come up with the following values...

r = -0.84

r²= 0.71

y = 15,919 - 1,151x

Which would be the correct interpretation of the coefficient of determination?

Each additional year in a vehicle's age leads to a decrease of $15,919 in its resale value.

84% of the variability in age can be explained by the linear relationship betweenit's age and resale value.

84% of the variability in resale value can be explained by the linear relationship betweenit's age and resale value.

There is a weak,negative linear relationship between a vehicle's age and its resale value.

71% of the variability in age can be explained by the linear relationship betweenit's age and resale value.

Each additional year in a vehicle's age leads to a decrease of $1,151 in its resale value.

There is a strong, negative linear relationship between a vehicle's age and its resale value.

71%of the variability in resale value can be explained by the linear relationship betweenit's age and resale value.

Performing simple regression analysis with an individual's height (in cm) as the independent variable and their weight (in kg) as the dependent variable, you come up with the following values...

r = 0.79

r²= 0.63

y = 0.80x - 50.89

Which would be the correct interpretation of the correlation coefficient?

Each additional 1cm in a person's height leads to increase of 0.80kg in their weight.

Each additional 1kg in a person's weight leads to increase of 0.80cm in their height.

79% of the variability in weight can be explained by the linear relationship betweena person's height and their weight.

There is a weak, negative linear relationship between a person's height and their weight.

There is a weak, positivelinear relationship between a person's height and their weight.

There is a strong, negative linear relationship between a person's height and their weight.

There is a strong, positive linear relationship between a person's height and their weight.

63% of the variability in weight can be explained by the linear relationship betweena person's height and their weight.