The systolic blood pressure data set in the third sheet of...

The systolic blood pressure data set (in the third sheet of the spreadsheet linked above) contains the systolic blood pressure and age of 30 randomly selected patients in a medical facility. What is the equation for the least square regression line where the independent or predictor variable is age and the dependent or response variable is systolic blood pressure? ^y =________ x + __________ Patient 9 is 67 years old and has a systolic blood pressure of 158 mm Hg. What is the residual? __________ mm Hg Is the actual value above, below, or on the line?_______ Pick: 1) Above 2) Below 3) On What is the interpretation of the residual?__________ Pick: 1) Difference In actual and predicted blood pressure for a given age 2) Difference in age between random people in the sample 3) The amount systolic blood pressure as age increases

Transcript

00:01 Hello everyone.

00:02 In this question, we are given a systolic blood pressure data set of 30 randomly selected patients.

00:10 We have to calculate the least square regression line and then we have to calculate the residual blood pressure also.

00:21 So let us calculate the regression line.

00:26 So consider the regression model, why, which is the regression.

00:44 Are independent, which is the dependent variable times which is equal to the constant beta naught plus beta one another constant times x plus bf epsilon which is another constant so these are arbitrary constants and then we write the data so the data which we have is the number of sample, which is n equal to 30.

01:41 Then we calculate this summation of x over all values of x.

01:51 So this comes out to be 13, 5, 4.

01:55 Similarly, we calculate the summation of y over all such given y, which is 4276.

02:06 Next we calculate the summation of x squared.

02:11 This is given to be 6, 7, 8, 9, 4.

02:20 Similarly, the summation of y squared is given to be 6242 -60.

02:32 So this symbol basically means some overall such value.

02:41 And if i square it basically gets squared.

02:47 So it's just a short notation for summation.

02:50 That's it.

02:51 Now we calculate the product, which is summation x, y, which is given to me, 1 -9 -576.

03:05 Then we calculate the mean of x, which is x bar, and mean of y, which is y bar, which is y bar.

03:18 So the x bar mean is given to be 45 .13 and the y bar is given to be 142 .43.

03:35 Next, we calculate the peta 1 cap.

03:40 So pita 1 cap is the constant of a regression model, which is given by the formula, is divided by n summation x.

04:05 Minus summation of x whole squared and upon substituting the values i get this as 0 .97.

04:19 After this we calculate another constant which is beta not cap...

Question

Please give Ace some feedback