a) Researchers begin with simple linear regression model with dadheight as the only independent variable and save the results as model m1. The output from this model is shown below. ## Call model m1 ## 1m (formula = childheight ~ dadheight) ## ## Estimate Std. Error t value Pr (>|t|) ## (Intercept) 101.954 8.0262 12.70 0.000 ## dadheight 0.385 0.0456 8.43 0.000 ## ## Residual standard error: 8.768 on 932 degrees of freedom ## F-statistic: 70.988 on 1 and 932 DF, p-value: <2e-16 Use the model m1 output above to predict the height of a child whose father has a height of 180cm. Furthermore, assuming SSx = 36916, x = 175.8 and to.025,932 1.96, derive a 95% prediction interval for th prediction for an individual child.
a) Researchers begin with simple linear regression model with dadheight as the only independent variable and save the results as model m1. The output from this model is shown below. ## Call model m1 ## 1m (formula = childheight ~ dadheight) ## ## Estimate Std. Error t value Pr (>|t|) ## (Intercept) 101.954 8.0262 12.70 0.000 ## dadheight 0.385 0.0456 8.43 0.000 ## ## Residual standard error: 8.768 on 932 degrees of freedom ## F-statistic: 70.988 on 1 and 932 DF, p-value: <2e-16 Use the model m1 output above to predict the height of a child whose father has a height of 180cm. Furthermore, assuming SSx = 36916, x = 175.8 and to.025,932 1.96, derive a 95% prediction interval for th prediction for an individual child.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
7.
How do I calculate the prediction interval?
is it 0. 385 + / - (0.0456 x 1.96) ?
![a) Researchers begin with simple linear regression model with dadheight as the only independent variable
and save the results as model m1. The output from this model is shown below.
## Call model m1
## 1m (formula = childheight ~ dadheight)
##
# #
Estimate Std. Error t value Pr (> [t])
## (Intercept)
101.954
8.0262 12.70
0.000
## dadheight
0.385
0.0456
8.43
0.000
# #
## Residual standard error: 8.768 on 932 degrees of freedom
## F-statistic: 70.988 on 1 and 932 DF, p-value: <2e-16
Use the model m1 output above to predict the height of a child whose father has a height of 180cm.
Furthermore, assuming SSx 36916, x = 175.8 and to.025,932= 1.96, derive a 95% prediction interval for this
prediction for an individual child.
(4)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F848ff6e8-adb0-458e-9c24-72fac47eee7d%2F4dcac07d-cb1e-4150-b394-40c80daca131%2Fawqz0a8_processed.png&w=3840&q=75)
Transcribed Image Text:a) Researchers begin with simple linear regression model with dadheight as the only independent variable
and save the results as model m1. The output from this model is shown below.
## Call model m1
## 1m (formula = childheight ~ dadheight)
##
# #
Estimate Std. Error t value Pr (> [t])
## (Intercept)
101.954
8.0262 12.70
0.000
## dadheight
0.385
0.0456
8.43
0.000
# #
## Residual standard error: 8.768 on 932 degrees of freedom
## F-statistic: 70.988 on 1 and 932 DF, p-value: <2e-16
Use the model m1 output above to predict the height of a child whose father has a height of 180cm.
Furthermore, assuming SSx 36916, x = 175.8 and to.025,932= 1.96, derive a 95% prediction interval for this
prediction for an individual child.
(4)
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