Suppose a researcher wants to evaluate the effect of cholesterol on the blood pressure. The following data on serum cholesterol (in mg/dL) and systolic blood pressure (in mm/Hg) were obtained for 15 patients to explore the relationship between cholesterol and blood pressure:
| S. No. | Cholesterol (mg/dL) | SBP (mm/Hg) | ||
| 1 | 300 | 150 | ||
| 2 | 410 | 270 | ||
| 3 | 380 | 210 | ||
| 4 | 530 | 310 | ||
| 5 | 570 | 350 | ||
| 6 | 490 | 310 | ||
| 7 | 340 | 210 | ||
| 8 | 320 | 150 | ||
| 9 | 280 | 110 | ||
| 10 | 550 | 320 | ||
| 11 | 340 | 220 | ||
| 12 | 350 | 170 | ||
| 13 | 410 | 260 | ||
| 14 | 390 | 230 | ||
| 15 | 450 | 270 | ||
(i) Fit a linear regression model using the method of least squares.
(ii) Construct the normal probability plot for the data on serum cholesterol and systolic blood pressure.
(iii) Test the significance of the fitted regression model.
(i) Fit a linear regression model using the method of least squares.
To fit a linear regression model, we first need to find the slope (b1) and the y-intercept (b0) using the method of least squares. The formulae for these values are:
b1 = Σ((xi - x_mean) * (yi - y_mean)) / Σ((xi - x_mean)^2)
b0 = y_mean - b1 * x_mean
where xi and yi are individual data points, and x_mean and y_mean are the means of x and y, respectively.
Here's the summary of the data provided:
Cholesterol (x): 300, 410, 380, 530, 570, 490, 340, 320, 280, 550, 340, 350, 410, 390, 450
SBP (y): 150, 270, 210, 310, 350, 310, 210, 150, 110, 320, 220, 170, 260, 230, 270
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