Time and Motion In a physics experiment at Doane College, a soccer ball was thrown upward from the bed of a moving truck. The table below lists the time (sec) that has lapsed from the throw and the height (m) of the soccer ball. What do you conclude about the relationship between time and height? What horrible mistake would be easy to make if the analysis is conducted without a scatterplot?

Time (sec)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Height (m)

0.0

1.7

3.1

3.9

4.5

4.7

4.6

4.1

3.3

2.1

Short Answer

Expert verified

The value of r is equal to 0.450.

Since the p-value of 0.192 is greater than 0.05, there is not a significant linear correlation between the time (sec) and height (m).

The scatter plot is represented as,

Step by step solution

01

Given information

The table represents the time (sec) that has lapsed from the throw and the height (m) of the soccer ball.

02

Calculate the correlation coefficient

Let x represents the Time (sec).

Let y represent the Height (m).

The formula for computing the correlation coefficient (r) between the values of Time (sec) and Height (m) is as follows:

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

The following calculations are done to compute the value of r:

x

y

xy

\({x^2}\)

\({y^2}\)

0

0

0

0

0

0.2

1.7

0.34

0.04

2.89

0.4

3.1

1.24

0.16

9.61

0.6

3.9

2.34

0.36

15.21

0.8

4.5

3.6

0.64

20.25

1

4.7

4.7

1

22.09

1.2

4.6

5.52

1.44

21.16

1.4

4.1

5.74

1.96

16.81

1.6

3.3

5.28

2.56

10.89

1.8

2.1

3.78

3.24

4.41

\(\sum x \)=9

\(\sum y \)=32

\(\sum {xy} \)=32.54

\(\sum {{x^2}} \)=11.4

\(\sum {{y^2}} \)=123.32

Substituting the above values, the value of r is obtained as,

\(\begin{aligned} r &= \frac{{n\sum {xy} - \sum x \sum y }}{{\sqrt {n\sum {{x^2}} - {{\left( {\sum x } \right)}^2}} \sqrt {n\sum {{y^2}} - {{\left( {\sum y } \right)}^2}} }}\\ &= \frac{{10\left( {32.54} \right) - \left( 9 \right)\left( {32} \right)}}{{\sqrt {10\left( {11.4} \right) - {{\left( 9 \right)}^2}} \sqrt {10\left( {123.32} \right) - {{\left( {32} \right)}^2}} }}\\ &= 0.450\end{aligned}\)

Therefore, the value of r is equal to 0.450.

03

Significance of r

Here, n=10.

If the value of the correlation coefficient lies between the critical values, then the correlation between the two variables is considered significant else, it is considered insignificant.

The critical values of r for n=10 and \(\alpha = 0.05\) are -0.632 and 0.632.

The corresponding p-value of r is equal to 0.192.

Since the computed value of r equal to 0.450 is greater than the larger critical value of 0.192, it can be said that the correlation between the two variables is insignificant.

Moreover, the p-value is greater than 0.05. This also implies that correlation is insignificant.

Therefore, there is not sufficient evidence to claim that there is a linear correlation between the time (sec) and the height (m).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

Heights (cm) and weights (kg) are measured for 100 randomly selected

adult males (from Data Set 1 “Body Data” in Appendix B). The 100 paired measurements yield\(\bar x = 173.79\)cm,\(\bar y = 85.93\)kg, r= 0.418, P-value = 0.000, and\(\hat y = - 106 + 1.10x\). Find the best predicted value of\(\hat y\)(weight) given an adult male who is 180 cm tall.

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

Testing for a Linear Correlation. In Exercises 13–28, 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.)

CSI Statistics Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths, foot lengths, and heights of males (from Data Set 2 “Foot and Height” in Appendix B). Is there sufficient evidence to conclude that there is a linear correlation between shoe print lengths and heights of males? Based on these results, does it appear that police can use a shoe print length to estimate the height of a male?

Shoe print(cm)

29.7

29.7

31.4

31.8

27.6

Foot length(cm)

25.7

25.4

27.9

26.7

25.1

Height (cm)

175.3

177.8

185.4

175.3

172.7

In Exercises 5–8, use a significance level 0.05 and refer to theaccompanying displays.Cereal Killers The amounts of sugar (grams of sugar per gram of cereal) and calories (per gram of cereal) were recorded for a sample of 16 different cereals. TI-83>84 Plus calculator results are shown here. Is there sufficient evidence to support the claim that there is a linear correlation between sugar and calories in a gram of cereal? Explain.

let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

For 50 randomly selected speed dates, attractiveness ratings by males of their

female date partners (x) are recorded along with the attractiveness ratings by females of their male date partners (y); the ratings are from Data Set 18 “Speed Dating” in Appendix B. The 50 paired ratings yield\(\bar x = 6.5\),\(\bar y = 5.9\), r= -0.277, P-value = 0.051, and\(\hat y = 8.18 - 0.345x\). Find the best predicted value of\(\hat y\)(attractiveness rating by female of male) for a date in which the attractiveness rating by the male of the female is x= 8.

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free