The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y=b 0+b1x, or predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant.

age: 35, 42, 49, 54, 60

bone density:350, 334, 333, 327, 323

Step 1 of 6:

Find the estimated slope. Round your answer to three decimal places.

Step 2 of 6:

Find the estimated y-intercept. Round your answer to three decimal places.

Step 3 of 6:

Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.

Step 4 of 6:

Find the estimated value of y when x=60

. Round your answer to three decimal places.

Step 5 of 6:

Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable y

Step 6 of 6:

Find the value of the coefficient of determination. Round your answer to three decimal places.