# hws8 - &¡¢£¢ ¤¥ ¦§¨©ª§«¬­...

This preview shows pages 1–4. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: &¡¢£¢ ¤¥ ¦§¨©ª§«¬­ ¡§®¯°±§²³ 2.46. a. The first variable ( X ) is the first number in the pair and is plotted on the horizontal axis, while the second variable ( Y ) is the second number in the pair and is plotted on the vertical axis. The scatterplot is shown in the figure below: b. There appears to be a positive relationship between X and Y ; that is, as X increases, so does Y . 2.48. a-b. The scatterplot is shown in the figure below. Notice that there is a negative relationship between X and Y . 11.3. Two points are needed to graph a straight line. When x=0, y=2. When x=1, y=1/2. The graph is shown below. 11.22. a. From the MINITAB printout, 1 ˆ β =.4736 and ˆ β =63.86, so that the least squares line is . 4736 . 86 . 63 ˆ ˆ ˆ 1 x x y + = + = β β Note that in the lecture notes, we use the notation α for the intercept and β for the slope coefficient. The graph of the least-squares line and the ten data points is shown below. In order to test for linear relationship between X and Y , we test the hypothesis H : ˆ 1 = β H a : ˆ 1 ≠ β and the test statistic is 75 . ˆ 1 1 =- = xx S s t β β The p-value is 2P (t > 0.75) = 0.475 which we can see in the MINITAB output. Since the observed level of significance is so large, H is not rejected. We cannot conclude that X and Y are linearly related (since β might be zero)....
View Full Document