Click the link to access the real data:

https://docs.google.com/spreadsheets/d/1aRCPAS98_KdvATmR6f-K3y2JnHdB3UPauHkty75CBA0/edit?usp=sharing

Click the link to see what to do:

https://www.youtube.com/watch?v=wV8XsjXiu9E

Now,

1. Which of the following columns linearly correlates most strongly with "sbp" (systolic blood pressure)? To test two columns, you can use the "=CORREL([first column], [second column])" command. For instance:

=CORREL(A2:A161,B2:B161)

calculates the correlation coefficient between sbp (systolic blood pressure) and tobacco use.

2. Construct the least squares regression equation that uses age to predict systolic blood pressure. In other words, let "x" be the age column and let "y" be the sbp column and compose a linear regression equation.

You can use the "=SLOPE(..., ...) command to calculate the slope that best fits the data:

=SLOPE([y-column], [x-column])

For example: =SLOPE(c2:c161, g2:g161) calculates the slope of the linear regression equation between the ldl and obesity variables.

You can use the =INTERCEPT([y-column], [x-column)] command to calculate the y-intercept for the linear regression equation. For example:

=INTERCEPT((c2:c161, g2:g161) calculates the y-intercept of the linear regression equation between the ldl and obesity variables.

3. Produce a scatter plot for the variables sbp and age. Does there appear to be a pattern between the two variables?

4. Use the least square regression equation that you constructed in part two to make a prediction. What systolic blood pressure does your regression equation predict for a person who is 60 years old?

5. If the linear regression equation was a good fit for the data, would you use the linear regression equation to predict the blood pressure of a ten-year-old? Why or why not?