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kolbaska11 [484]
2 years ago
14

A department of transportation's study on driving speed and miles per gallon for midsize automobiles resulted in the following d

ata. Speed (Miles per Hour) 30 50 40 55 30 25 60 25 50 55 Miles per Gallon 29 26 26 22 29 31 21 35 25 26 Compute the sample correlation coefficient. (Round your answer to two decimal places.) Interpret the sample correlation coefficient. There is a strong negative relationship between the driving speed and miles per gallon. For driving speed between 25 and 60 miles per hour, higher speeds are associated with lower miles per gallon.
Mathematics
1 answer:
dezoksy [38]2 years ago
5 0

Answer:

R = -0.90 (2 decimal places)

Step-by-step explanation:

Given the data :

Speed (Miles per Hour) :

30

50

40

55

30

25

60

25

50

55

Miles per Gallon :

29

26

26

22

29

31

21

35

25

26

The correlation Coefficient, R shows the degree or level of relationship between the dependent and independent variable. A positive R value denotes a positive correlation while a negative R value denotes a negative relationship. R values should range between - 1 to +1 with values close to 1 and - 1 depicting strength while value of zero means no relationship exist.

Hence, R value of -0.90 depicts a strong negative relationship. Hence, higher speed are related to lower miles per gallon and vice versa.

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z=\frac{(7.6-8.1)-0}{\sqrt{\frac{2.3^2}{40}+\frac{2.9^2}{55}}}}=-0.936  

The p value can be founded with this formula:

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Step-by-step explanation:

Information given

\bar X_{1}=7.6 represent the mean for Gibbs products

\bar X_{2}=8.1 represent the mean for the competitor

\sigma_{1}=2.3 represent the population standard deviation for Gibbs

\sigma_{2}=2.9 represent the sample standard deviation for the competitor

n_{1}=40 sample size for the group Gibbs

n_{2}=55 sample size for the group competitor

\alpha=0.05 Significance level provided

z would represent the statistic

Hypothesis to verify

We want to check if babies using the Gibbs brand gained less weight, the system of hypothesis would be:  

Null hypothesis:\mu_{1}-\mu_{2}=0  

Alternative hypothesis:\mu_{1} - \mu_{2}< 0  

The statistic would be given by:

z=\frac{(\bar X_{1}-\bar X_{2})-\Delta}{\sqrt{\frac{\sigma^2_{1}}{n_{1}}+\frac{\sigma^2_{2}}{n_{2}}}} (1)  

Replacing the info given we got:

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The p value can be founded with this formula:

p_v =P(z  

Since the p value is higher than the significance level provided of 0.05 we have enough evidence to FAIL to reject the null hypothesis and we can't conclude that the true mean for the Gibbs brand is significantly lower than the true mean for the competitor

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3 years ago
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