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Explore! Exercises 9 and 10 provide two data sets from 鈥淕raphs in Statistical Analysis,鈥 by F. J. Anscombe, the American Statistician, Vol. 27. For each exercise,

a. Construct a scatterplot.

b. Find the value of the linear correlation coefficient r, then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables.

c. Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot.

x

10

8

13

9

11

14

6

4

12

7

5

y

9.14

8.14

8.74

8.77

9.26

8.10

6.13

3.10

9.13

7.26

4.74

Short Answer

Expert verified

a. The scatter plot is shown below:

b. The correlation coefficient is 0.8162. There is enough evidence to support the claim that there is a linear correlation between the two variables.

c. The scatterplot shows that the data follows a non-linear pattern missing in part (b).

Step by step solution

01

Given information

The paired data for two variables arerecorded.

x

10

8

13

9

11

14

6

4

12

7

5

y

9.14

8.14

8.74

8.77

9.26

8.1

6.13

3.1

9.13

7.26

4.74

02

Sketch a scatterplot

a.

A scatterplot is a graph that represents observations for a paired set of data.

Steps to sketch a scatterplot:

  1. Define thex and yaxes for each of the two variables. The horizontal axis is thex-axis, and the vertical axis is the y-axis.
  2. Map each paired value corresponding to the axes.
  3. Thus, a scatter plot for the paired data is obtained.

03

Compute the measure of the correlation coefficient

b.

The correlation coefficient is computed below:

\(r = \frac{{n\sum {xy} - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {n\left( {\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \sqrt {n\left( {\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} }}\)

The valuesare listedin the table below:

x

y

\({x^2}\)

\({y^2}\)

\(xy\)

10

9.14

100

83.5396

91.4

8

8.14

64

66.2596

65.12

13

8.74

169

76.3876

113.62

9

8.77

81

76.9129

78.93

11

9.26

121

85.7476

101.86

14

8.1

196

65.61

113.4

6

6.13

36

37.5769

36.78

4

3.1

16

9.61

12.4

12

9.13

144

83.3569

109.56

7

7.26

49

52.7076

50.82

5

4.74

25

22.4676

23.7

\(\sum x = 99\)

\(\sum y = 82.51\)

\(\sum {{x^2}} = 1001\)

\(\sum {{y^2} = } \;660.1763\)

\(\sum {xy\; = \;} 797.59\)

Substitute the values in the formula:

\(\begin{aligned} r &= \frac{{11\left( {797.59} \right) - \left( {99} \right)\left( {82.51} \right)}}{{\sqrt {11\left( {1001} \right) - {{\left( {99} \right)}^2}} \sqrt {11{{\left( {660.1763} \right)}^2} - {{\left( {82.51} \right)}^2}} }}\\ &= 0.8162\end{aligned}\)

Thus, the correlation coefficient is 0.8162.

04

Step 4:Conduct a hypothesis test for correlation

Let\(\rho \)be the true correlation coefficient measure for the paired variables.

For testing the claim, form the hypotheses as shown below:

\(\begin{array}{l}{{\rm{H}}_{\rm{o}}}:\rho = 0\\{{\rm{{\rm H}}}_{\rm{a}}}:\rho \ne 0\end{array}\)

The samples size is11(n).

The test statistic is computed as follows:

\(\begin{aligned} t &= \frac{r}{{\sqrt {\frac{{1 - {r^2}}}{{n - 2}}} }}\\ &= \frac{{0.8162}}{{\sqrt {\frac{{1 - {{0.8162}^2}}}{{11 - 2}}} }}\\ &= 4.238\end{aligned}\)

Thus, the test statistic is 4.238.

The degree of freedom is computed below:

\(\begin{aligned} df &= n - 2\\ &= 11 - 2\\ &= 9\end{aligned}\)

The p-value is computed using the t-distribution table.

\(\begin{aligned} p{\rm{ - value}} &= 2P\left( {T > t} \right)\\ &= 2P\left( {T > 4.238} \right)\\ &= 2\left( {1 - P\left( {T < 4.238} \right)} \right)\\ &= 0.002\end{aligned}\)

Thus, the p-value is 0.002.

Since the p-value is lesser than 0.05, the null hypothesis is rejected.

Therefore, there is sufficient evidence to conclude that variables x and y have a linear correlation between them.

05

Analyze the importance of the scatterplot

c.

The scatterplot reveals that the data follows a strong non-linear pattern. It means that the observations do not align on a straight line.

The characteristic of the data would be missed in part (b) if the scatterplot was not sketched.

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Most popular questions from this chapter

Effects of an Outlier Refer to the Minitab-generated scatterplot given in Exercise 11 of

Section 10-1 on page 485.

a. Using the pairs of values for all 10 points, find the equation of the regression line.

b. After removing the point with coordinates (10, 10), use the pairs of values for the remaining 9 points and find the equation of the regression line.

c. Compare the results from parts (a) and (b).

Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Use the foot lengths and heights to find the best predicted height of a male

who has a foot length of 28 cm. Would the result be helpful to police crime scene investigators in trying to describe the male?

In Exercises 5鈥8, we want to consider the correlation between heights of fathers and mothers and the heights of their sons. Refer to the

StatCrunch display and answer the given questions or identify the indicated items.

The display is based on Data Set 5 鈥淔amily Heights鈥 in Appendix B.

Should the multiple regression equation be used for predicting the height of a son based on the height of his father and mother? Why or why not?

Testing for a Linear Correlation. In Exercises 13鈥28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

Tips Listed below are amounts of bills for dinner and the amounts of the tips that were left. The data were collected by students of the author. Is there sufficient evidence to conclude that there is a linear correlation between the bill amounts and the tip amounts? If everyone were to tip with the same percentage, what should be the value of r?

Bill(dollars)

33.46

50.68

87.92

98.84

63.6

107.34

Tip(dollars)

5.5

5

8.08

17

12

16

What is the relationship between the linear correlation coefficient rand the slope\({b_1}\)of a regression line?

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