Concept explainers
(a)
To find: The multiple regression for BMI using
(a)
Answer to Problem 25E
Solution: The required multiple regression model is
Explanation of Solution
Given: The data for the PA and BMI are given below:
Calculation: The explanatory variables are
To obtain multiple
Step 1: Enter the data in Minitab worksheet.
Step 2: Go to Calc>Calculator.
Step 3: Enter
Step 4: Click OK.
Step 5: Repeat the steps. Enter
Step 6: Go to Stat > Regression > Regression
Step 7: Select BMI in Response and select
Step 8: Click OK.
The multiple regression model is obtained as
(b)
To find: The value of
(b)
Answer to Problem 25E
Solution: The value of
Explanation of Solution
(c)
To graph: The Normal quantile plot.
(c)
Explanation of Solution
Graph: To perform the multiple regression by using year and census count as explanatory variables use Minitab. Follow the steps below:
Step 1: Enter the data in Minitab worksheet.
Step 2: Go to Stat> Regression >Regression.
Step 3: Select BMI in Response and select
Step 4: Click on Graphs and select “Residuals versus fits.”
Step 5: Click OK.
The residual plot is obtained as:
Interpretation: From the obtained residual plot, it can be concluded that the plot represents no pattern and the data points are randomly scattered.
(d)
To test: The hypothesis that coefficient of the variable
(d)
Answer to Problem 25E
Solution: There is enough evidence to conclude that there is no linear increase over time.
Explanation of Solution
Calculation: From the output which is obtained in part (b), the regression equation is
The level of significance is 0.05. The test statistic under null hypothesis is calculated as:
The p-value can be calculated as:
The p-value is 0.0678.
Conclusion: The obtained p-value is greater than the significance level. Hence, there is enough evidence to conclude that the quadratic term contributes insignificantly to the fit.
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Chapter 11 Solutions
INTRO PRAC STATS LL+ SAPLING HW
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