Practice of Statistics in the Life Sciences
Practice of Statistics in the Life Sciences
4th Edition
ISBN: 9781319013370
Author: Brigitte Baldi, David S. Moore
Publisher: W. H. Freeman
Question
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Chapter 23, Problem 23.30E

a)

To determine

To construct a scatter plot.

a)

Expert Solution
Check Mark

Explanation of Solution

Given:

Practice of Statistics in the Life Sciences, Chapter 23, Problem 23.30E , additional homework tip  1

Scatter plot for Actual vs 3D volume construction is,

Practice of Statistics in the Life Sciences, Chapter 23, Problem 23.30E , additional homework tip  2

    Regression Analysis     
      
     0.954 n 30  
     r 0.977 k 1  
     Std. Error 0.649 Dep. Var. Actual 
      
    ANOVA table 
    Source SS df MS F p-value 
    Regression 242.8360 1 242.8360 576.933.17E-20 
    Residual 11.7855 28 0.4209  
    Total 254.6214 29     
      
      
    Regression output confidence interval
    variables coefficients std. error t (df=28) p-value 95% lower 95% upper
    Intercept0.4196 0.4671 0.898 .3767-0.5373 1.3764
    3D2.4752 0.1031 24.019 3.17E-202.2641 2.6863

Therefore, least square regression equation is,

  y^=0.4196+2.4752x

b)

To determine

To verify the conditions for inference.

b)

Expert Solution
Check Mark

Answer to Problem 23.30E

All the conditions satisfied.

Explanation of Solution

Given:

Practice of Statistics in the Life Sciences, Chapter 23, Problem 23.30E , additional homework tip  3

The scatter plot shows linearly increasing trend. Therefore, the relationship is clearly linear, the scatterplot shows no unusual pattern that would indicate not Normally distributed residuals or residuals without a constant standard deviation, and the observations are independent.

c)

To determine

To test whether the linear relationship is statistically significant.

c)

Expert Solution
Check Mark

Answer to Problem 23.30E

There is sufficient evidence to conclude that the linear relationship between two variables is statistically significant.

Explanation of Solution

Given:

    Regression Analysis     
      
     0.954 n 30  
     r 0.977 k 1  
     Std. Error 0.649 Dep. Var. Actual 
      
    ANOVA table 
    Source SS df MS F p-value 
    Regression 242.8360 1 242.8360 576.933.17E-20 
    Residual 11.7855 28 0.4209  
    Total 254.6214 29     
      
      
    Regression output confidence interval
    variables coefficients std. error t (df=28) p-value 95% lower 95% upper
    Intercept0.4196 0.4671 0.898 .3767-0.5373 1.3764
    3D2.4752 0.1031 24.019 3.17E-202.2641 2.6863

Null and alternative hypotheses:

  H0:β=0

  H0:β0

Test statistic is,

t = 24.019

P-value = 0.0000

Decision: P-value< 0.05, reject H0.

Conclusion: There is sufficient evidence to conclude that the linear relationship between two variables is statistically significant.

Therefore, 95% confidence interval for slope is,

(2.2641, 2.6863)

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