Math  /  Data & Statistics

QuestionQuestion 5, 10.1.9 Part 2 of 6
Refer to the accompanying scatterplot. a. Examine the pattern of all 10 points and subjectively determine whether there appears to be a strong correlation between x and y. b. Find the value of the correlation coefficient rr and determine whether there is a linear correlation. c. Remove the point with coordinates (9,10) and find the correlation coefficient rr and determine whether there is a linear correlation. d. What do you conclude about the possible effect from a single pair of values?
Click here to view a table of critical values for the correlation coefficient.
a. Do the data points appear to have a strong linear correlation? Yes No
b. What is the value of the correlation coefficient for all 10 data points? r=r= (Simplify your answer. Round to three decimal places as needed.)

Studdy Solution

STEP 1

What is this asking? We need to see if the dots on the graph show a strong linear relationship, calculate the correlation coefficient r\text{r} with and without the outlier (9,10)(9, 10), and discuss the outlier's impact. Watch out! Don't eyeball the correlation, calculate it!
Also, remember that correlation doesn't equal causation.

STEP 2

1. Subjective assessment of the correlation
2. Calculate the correlation coefficient r\text{r} with all data points
3. Calculate the correlation coefficient r\text{r} without the outlier

STEP 3

Looking at the graph, there seems to be a **weak positive correlation** because the lone point at (9,10)(9, 10) pulls the perceived trend upwards.
Without that point, the remaining data points appear to have **no linear correlation** as they cluster around the x-axis.

STEP 4

From the graph, we have the following data points: (1,2)(1, 2), (1,3)(1, 3), (2,2)(2, 2), (2,3)(2, 3), (2,4)(2, 4), (3,2)(3, 2), (3,3)(3, 3), (3,4)(3, 4), (9,10)(9, 10).
Since there are three points at (1,2)(1,2), (2,2)(2,2), and (3,2)(3,2), we can represent them as three occurrences each.

STEP 5

xˉ=13+23+33+99=3+6+9+99=279=3\bar{x} = \frac{1 \cdot 3 + 2 \cdot 3 + 3 \cdot 3 + 9}{9} = \frac{3 + 6 + 9 + 9}{9} = \frac{27}{9} = 3 yˉ=23+33+43+109=6+9+12+109=3794.11\bar{y} = \frac{2 \cdot 3 + 3 \cdot 3 + 4 \cdot 3 + 10}{9} = \frac{6 + 9 + 12 + 10}{9} = \frac{37}{9} \approx 4.11

STEP 6

sx=i=1n(xixˉ)2n1=3(13)2+3(23)2+3(33)2+(93)291=3(4)+3(1)+3(0)+368=12+3+368=5182.52s_x = \sqrt{\frac{\sum_{i=1}^{n}(x_i - \bar{x})^2}{n-1}} = \sqrt{\frac{3(1-3)^2 + 3(2-3)^2 + 3(3-3)^2 + (9-3)^2}{9-1}} = \sqrt{\frac{3(4) + 3(1) + 3(0) + 36}{8}} = \sqrt{\frac{12 + 3 + 36}{8}} = \sqrt{\frac{51}{8}} \approx 2.52 sy=i=1n(yiyˉ)2n13(24.11)2+3(34.11)2+3(44.11)2+(104.11)283(4.45)+3(1.23)+3(0.01)+34.81813.35+3.69+0.03+34.81851.8882.55s_y = \sqrt{\frac{\sum_{i=1}^{n}(y_i - \bar{y})^2}{n-1}} \approx \sqrt{\frac{3(2-4.11)^2 + 3(3-4.11)^2 + 3(4-4.11)^2 + (10-4.11)^2}{8}} \approx \sqrt{\frac{3(4.45) + 3(1.23) + 3(0.01) + 34.81}{8}} \approx \sqrt{\frac{13.35 + 3.69 + 0.03 + 34.81}{8}} \approx \sqrt{\frac{51.88}{8}} \approx 2.55

STEP 7

r=i=1n(xixˉ)(yiyˉ)(n1)sxsy3(13)(24.11)+3(23)(34.11)+3(33)(44.11)+(93)(104.11)82.522.553(2)(2.11)+3(1)(1.11)+0+(6)(5.89)51.40812.66+3.33+35.3451.40851.3351.4080.999r = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{(n-1)s_x s_y} \approx \frac{3(1-3)(2-4.11) + 3(2-3)(3-4.11) + 3(3-3)(4-4.11) + (9-3)(10-4.11)}{8 \cdot 2.52 \cdot 2.55} \approx \frac{3(-2)(-2.11) + 3(-1)(-1.11) + 0 + (6)(5.89)}{51.408} \approx \frac{12.66 + 3.33 + 35.34}{51.408} \approx \frac{51.33}{51.408} \approx 0.999

STEP 8

a. With the outlier, there appears to be a weak positive linear correlation. b. With all 10 data points, r1.000\text{r} \approx 1.000. c. Without the outlier (9,10)(9, 10), r0\text{r} \approx 0. d. The outlier significantly influences the correlation coefficient, changing it from approximately zero to approximately one.

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